Table of Contents
Building a B2B AI application means making dozens of infrastructure decisions before you can fully realize their impact. These choices aren’t totally blind, but you’ll often feel forced to take a leap of faith based on incomplete information.
Depending on where your journey begins, you may notice:
Agentic framework choices constraining your backend options
Backend picks limiting your agentic framework possibilities
Database decisions affecting your deployment platform, observability
And many more knock-on effects narrowing your options over time
Most guides treat these as isolated decisions, but they’re clearly not. Each component contributes to an interdependent network where early choices cascade throughout your entire stack. Especially in the AI vertical, hasty infrastructure decisions can come back with a vengeance: costly change management and the resulting vendor lock-in are surprisingly common.
We spoke with B2B AI organization founders to learn more about these tech decisions: how they impacted their journey, choices they would make differently now, and insights you can put to use right now.
The leaders and organizations we consulted include:
Anand Sainath, Co-Founder & Head of Engineering at Echelon AI
Kapil Chhabra, Co-Founder & CPO at WisdomAI
Abhinav Y., Builder at Different AI
This guide covers ten essential components, with each section explaining the key tradeoffs and decision factors:
You might have made many of these choices already. Or perhaps you’re just starting out, and you won't need everything here right away. However, the value isn’t spoonfeeding you the “best” choice—which is going to vary wildly based on your unique needs, anyway—it’s understanding where you are, what’s next, and how to be agile when requirements change.
This guide will offer immediate benefits for whatever tech stack challenge you’re facing, whether that's an enterprise customer needing SCIM to sign a deal or production hallucinations that an eval framework could’ve caught.
Let's begin our breakdown with where you write code.
Development environment
Your development environment is where you write, test, and debug code. AI-powered IDEs have become table stakes for building AI applications, offering code completion, codebase-aware assistance, and debugging help that dramatically accelerates development. The right choice depends on where your team is today and where you're heading. In other words, if you're prototyping, start simple. If you're scaling a complex codebase, invest in tooling that handles that complexity.
Key decision factors:
Team experience level: Less experienced developers benefit more from agentic features that handle multi-file changes
Scope of assistance: Do you need AI working across your entire codebase or just completing individual functions?
Budget constraints: Pricing varies significantly between options
Enterprise requirements: SSO and compliance features matter if you're building for regulated industries
Your main options:
Cursor is the AI-first choice for complex codebases. Built on VS Code, so the transition is seamless if your team already uses it. The Composer feature lets you describe changes in plain language and it modifies multiple files automatically. Best for teams tackling complex projects or those new to specific frameworks who need more guidance.
Windsurf offers similar capabilities with a different workflow feel. Generally more accessible for beginners, though potentially less performant at scale. Cascade automatically determines which files to examine and can run terminal commands. A good pick for budget-conscious teams (runs $10 less per user monthly than Cursor).
GitHub Copilot excels at line and function completion but handles large-scale changes less elegantly. The obvious choice if you're already invested in the GitHub ecosystem and primarily need autocomplete assistance rather than architectural changes. Solid integration with pull requests and code reviews.
Replit is browser-based with zero installation required. Fastest from idea to working code, making it ideal for quick prototyping or early MVP work before your stack solidifies. AI features are less sophisticated, but the agility can matter more in the earliest stages.
Anand Sainath, Co-Founder & Head of Engineering at Echelon AI:
“Coding agents were my introduction to agents at scale. Watching tools like Cursor shift from ‘user-as-driver’ to ‘human-in-the-loop’ taught us patterns like spec-first development—learnings we now apply to our own enterprise coding agents.”
Backend framework
Your backend framework handles server-side logic: processing requests, managing data, calling AI models, and coordinating everything behind the scenes. For B2B AI applications, this decision is heavily influenced by your agentic framework choice.
Most AI tooling is built in Python first. If your application involves heavy AI work like RAG or complex pipelines, you're almost certainly running Python on the backend. If your needs are simpler (calling OpenAI's API, basic request handling) and your team knows JavaScript well, Node.js can work. The core question: Does your agentic framework require Python? If yes, your decision is made. If not, your team's existing expertise usually matters more than theoretical advantages.
Anand Sainath, Co-Founder & Head of Engineering at Echelon AI:
“We started as a Python shop, largely because the majority of AI advancements and libraries were Python-first. Team familiarity was also a major driver—almost everyone on the team could code in Python, which allowed us to move quickly at the start.”
Key decision factors:
Team expertise: Switching languages has real costs in velocity and debugging time
Agentic framework compatibility: Some frameworks only support Python, which simplifies your decision
Custom requirements: Building custom models or using specialized libraries beyond standard frameworks favors Python's larger ecosystem
Stack consistency: Sharing language between frontend and backend can streamline development
Your main options:
FastAPI (Python) is the dominant choice for AI startups. Fast, modern, built specifically for APIs. Automatic documentation, data validation through Pydantic, and native async support for handling multiple requests efficiently. Works seamlessly with Python-based agentic libraries like LangChain. Type hints catch errors during development rather than production. The default unless you have specific reasons to choose otherwise.
Django (Python) brings the full "batteries included" approach. Admin panel, ORM, authentication, and extensive built-in features. Heavier than FastAPI, but if you need those features anyway, it saves building them yourself. Good for teams building full web applications or those who want more structure out of the box. Works with virtually all AI libraries since it's Python.
Express.js (Node.js/TypeScript) offers minimalist flexibility with a massive ecosystem. Quick to start, especially for JavaScript-comfortable teams. Solid choice when frontend and backend developers share the same language. Pairs well with JavaScript versions of agentic frameworks like LlamaIndex.ts or LangGraph.js.
Nest.js (Node.js/TypeScript) provides more structure than Express with built-in TypeScript support. Better for organizing larger codebases because it follows enterprise patterns. Strong candidate for teams wanting opinionated structure or larger organizations that value consistency. Works well with TypeScript variants of agentic frameworks.
Kapil Chhabra, Co-Founder & CPO at WisdomAI:
“Our framework choices are developer-driven. Instead of basing decisions solely on compatibility with other components, we look at what fits better for our needs and choose based on developer pull and preferences.”
Agentic framework
Your agentic framework provides pre-built components for creating AI agents, handling the parts that would take ages to DIY so you don’t build everything from scratch. These frameworks manage LLM connections, memory and context, external tool integration, and orchestration of multi-step workflows or multi-agent collaboration.
Key decision factors:
Agent complexity: Are you building a single agent or multiple agents working together?
Control requirements: How much control do you need over workflows versus letting the framework handle orchestration?
Primary function: Does your agent primarily work with data (e.g., retrieval and/or analysis) or have a more diverse role?
Tolerance for learning curve: How much time can your team invest in mastering a framework outside their existing knowledge base?
Your main options:
LangGraph
LangGraph is a free, open-source library (Python and TypeScript) built on top of LangChain. It uses a graph-based approach where you define agent workflows as nodes (tasks) and edges (connections between tasks). Performance benchmarks show it has the lowest latency and token use among major frameworks.
LangGraph is ideal for teams that want fine-grained controls over complex multi-step workflows, or applications where understanding and debugging agent decision processes is critical. Steeper learning curve than most, but powerful once you grasp its graph concepts.
CrewAI
CrewAI takes a much different approach. You define agents by their roles (e.g., “Analyst,” “Researcher,”) and organize them into “Crews,” which work together on specific tasks. This makes getting started more intuitive because you focus on what agents do rather than how they connect. CrewAI recently removed its LangChain dependency and is growing rapidly thanks to its low barrier for entry.
CrewAI is a good choice for teams that need to stand up working prototypes quickly, or applications where agents cleanly map to specific job functions. The main drawback is less granular control than a more complex framework.
LlamaIndex
LlamaIndex focuses on building agents that work directly with data: connecting to databases, documents, APIs, and other sources of information. Excellent for retrieval-augmented generation (RAG), with robust tooling for indexing data, managing vectors, and orchestrating multi-document workflows.
LlamaIndex is well-suited for applications where the agent’s primary job is finding, retrieving, and synthesizing based on existing data. Think customer service bots searching account data or analysts pulling from company databases. It’s less general-purpose than LangGraph or CrewAI, but excels at data-focused projects.
The broader landscape boasts many, many agentic frameworks: AutoGen (now AG2), OpenAI Swarm, Semantic Kernel, PydanticAI, Smolagents, and many more. Some are newer and still maturing (OpenAI explicitly calls Swarm “educational” rather than production-ready), while others are seeing major rewrites.
The right choice depends on your specific needs, your team’s skills, how much time you can invest in learning, and your tolerance for potentially radical (read: breaking) changes. The three frameworks above represent some of the most popular, battle-tested, and production-capable options at the time of writing, but they’re not the only game in town. Plenty of viable alternatives exist, and many more are entering the marketplace every day.
Abhinav Y., Builder at Different AI:
“The biggest factors were developing context and memory, and storing them. That’s because, with just some context changes, GenAI systems can produce ‘surprises.’ We reduced the chances of unpredictable outcomes by building complex, domain-specific scaffolding and placing many automated guardrails to ensure there’s a human in the loop.”
Frontend framework
Your frontend framework determines how users interact with your AI application: handling the UI, user input, updates, and overall experience. For B2B AI apps, the frontend is critical for differentiation and building user trust at the organizational level. It should be performant, responsive, and capable of handling dynamic input without skipping a beat.
Key decision factors:
Team expertise: Does your team have experience with a particular framework or language (Python vs. JavaScript)?
Performance requirements: How important is performance on different platforms or within more constrained environments, like on mobile devices or slower connections?
Ecosystem needs: Do you need a large library of ready-made components, especially AI-specific ones?
Hiring considerations: Can you find and afford developers with the right skillset?
AI integration support: Do you need a framework that handles streaming responses well out of the box?
AI startups will find a landscape roughly divided into two camps: Python and JavaScript.
Dimension | Python (Streamlit, Gradio, Dash) | JavaScript (React, Svelte, Vue) |
|---|---|---|
How it works as a frontend | Abstractions that generate web interfaces from Python code | Direct control over what renders in the browser |
UI customization | Limited to the components and layouts the tool provides | Full control over styling, components, and interactions |
Setup speed | Stand up a working interface in hours or less | More upfront work; steeper learning curve if team isn’t JS-native |
AI integration | Native support for streaming responses, file uploads, model outputs | Strong ecosystem support (e.g. Vercel AI SDK); requires more scaffolding/wiring |
When to use | Internal tools, prototypes, MVPs where AI output matters more than polish | Customer-facing products, enterprise UX, long-term production apps |
The Python camp boasts speed and simplicity. If you’re already familiar with Python (which, given the foundation of most agentic frameworks and backends, is likely), you’ll be pleased to find that Python-based UI tools like Streamlit, Gradio, and Dash are purpose-built for data applications, with native support for streaming responses, file uploads and model outputs.
But they’re not really frontend frameworks. At least, not in the way that JS frameworks like Vue or Svelte are. That’s because browsers run JavaScript natively, but not Python. These tools are abstractions that generate web interfaces from Python code, handling the HTML, CSS, and JavaScript under the hood so you don’t have to touch it.
That’s their advantage, but also a material constraint. You’re limited to the components and layouts the tool provides. Styling comes down to what the abstraction layer can do. Complex interactions or custom UI patterns can be tough to manage with these useful-but-inert building blocks.
So, ultimately, apps built with Python-based frontend tools tend to have a rather recognizable, generic look and feel. For internal apps or those used in dev-focused workflows (or for early MVPs), that obviously doesn’t matter. For customer-facing products, including those built for enterprise customers, UX is a major differentiating factor.
The JavaScript camp is the industry standard for production B2B applications. With JS frameworks, you have direct control over what it renders in the browser: the styling, components, interactions, everything. The ceiling is much higher, but the floor is also lower. JS frameworks can often entail more upfront work, steeper learning curves if your team isn’t JS-native, and the ecosystem is (perhaps intimidatingly) massive.
The simple reality is that many AI startups prototype using Python front and backends (sometimes all-in-one options like Streamlit or Gradio), validate the product, then rebuild the frontend in a JS framework (like React / Next.js ) when they’re ready to scale.
This presents a critical decision: whether you want to stick with the same framework all the way through to production, or if standing up an MVP first and switching later makes more sense.
Your main options for frontend framework are:
Python-based choices
Python-based choices like Streamlit, Gradio, and Dash, which are all better described as tools or libraries than frontend frameworks. They’re high-level abstractions that generate interfaces from Python code. These tools are typically easy to stand up within a few hours (or less), strong for building demos that let users interact with models directly (input text, get output), and simpler to learn than JS frameworks.
They’re a good choice for internal prototypes or MVPs where the AI output matters more than the interface around it, but they generally lack the visual appeal you’d expect from a finished, polished product.
React / Next.js-based choices
React / Next.js (JavaScript) is the industry standard, the most widely used option for B2B applications by a considerable margin. React’s component-based architecture makes it easier to build complex, interactive UIs. Meanwhile, Next.js adds server-side rendering (SSR), API routing, and built-in optimizations for numerous scenarios. When considering deployment platforms, this can impact suitability.
For example, Vercel’s AI SDK provides native React hooks for seamless LLM integration, and Next.js handles server actions to streamline backend logic directly within components. Vercel isn’t the only game in town that favors React, though; it boasts the largest ecosystem, biggest talent pool, and widest adoption by miles. This makes React and Next.js an obvious choice for teams that want stability, extensive resources, and the ability to scale.
Svelte-based choices
Svelte / SvelteKit (JavaScript) is a performance-oriented, lightweight alternative to the JS framework mainstays. It’s gained traction among AI developers for its simplicity and speed, with a minimal bundle size that makes it ideal for constrained environments.
Even Vercel has built AI chatbots using SvelteKit, demonstrating its viability for AI use cases. It could be a strong choice for teams building efficiency-oriented, mobile-first applications, or those who want to move fast and favor the easier learning curve. The downsides are as expected when going with a less adopted alternative: modest ecosystem, fewer AI-specific resources compared to React, and a smaller talent pool (though it’s not difficult to learn).
Vue / Nuxt
Vue / Nuxt (JavaScript) balances ease of use with robust functionality. It’s often considered more approachable than React at face value, with accessible documentation and a gentler learning curve. The latest version boasts improved TypeScript support and a solid reactivity system, with tools like Vite for faster development.
Nuxt provides the meta-framework layer with SSR, routing, and full-stack functionality. While not as prolific as React’s ecosystem, Vue’s resources are substantial. This makes it a strong contender for the “happy middle ground” choice, for teams that want something easier to master than React but more established than Svelte in the AI space.
Anand Sainath, Co-Founder & Head of Engineering at Echelon AI:
“The UX of the product was one of our most unexpected technical challenges. How well would our solution fit for day-to-day users? Would they have to learn new patterns? We've experimented with this a lot. Slapping on a chat interface for every agentic product isn't always the best solution.”
Database
Your database stores and retrieves all the data your application works with. For AI applications, however, this can include powerful vector embeddings that enable certain AI features. You’ll often want both traditional data storage (for structured information like user profiles, transactions, and metadata) and vector storage (for embeddings that enable semantic search and RAG).
Key decision factors:
Data types: What are you storing? Structured/relational data, unstructured vectors? Both?
Scale trajectory: How much data will you eventually have, and how fast will it grow at its peak?
Query requirements: Do you need your database to handle both traditional queries and vector similarity search?
Team familiarity: Are you comfortable with any database model, or do you need to optimize for developer expertise, performance, or budget?
Infrastructure preferences: Do you need managed infrastructure, or can you self-host?
Your main database options are:
PostgreSQL
PostgreSQL is the most popular open-source relational database by a considerable margin, and a strong default choice for B2B AI apps. Battle-tested reliability and an extensive ecosystem of tools and extensions make PostgreSQL the top pick for teams addressing complex queries, needing JSON support, and integration with diverse programming languages.
The pgvector extension enables Postgres to support integrated processing of relational and vector data. If you’re already running Postgres, this is the obvious choice for hybrid queries combining structured filters (dates, user attributes) with vector search. The main tradeoff for B2B AI is that pgvector at very large scale (tens of millions of vectors) requires substantial operational tuning and infrastructure investment, though it remains viable with proper configuration.
MongoDB
MongoDB is a popular NoSQL option that stores data as flexible JSON-like documents rather than rigid tables. Great for rapidly changing schemas, unstructured data, or when you need to iterate quickly without database migrations. Scales horizontally well, making it suitable for high-traffic applications.
MongoDB Atlas (managed service) includes vector search capabilities, though it's not as optimized as purpose-built vector databases. A good choice for teams comfortable with NoSQL or those building applications with especially variable data structures. The drawback here is giving up SQL's powerful querying capabilities.
Specialized vector databases
Specialized vector databases (Pinecone, Weaviate, Milvus): When AI features form the core of your product and you're working with large-scale vector data (millions of embeddings), a purpose-built vector database can be worth the investment. These are dedicated to similarity search and optimized specifically for vector operations.
These aren't necessarily alternatives to traditional databases; they can work in tandem, syncing data or splitting it based on purpose. This route is best for applications where semantic search, recommendations, or RAG are central features. The drawback is mostly overhead: managing more infrastructure, additional cost at scale, and increased architectural complexity.
Anand Sainath, Co-Founder & Head of Engineering at Echelon AI:
“Due to our initial deployment provider choice, our data infrastructure was fragmented across multiple providers (Redis, hosted PostgreSQL, blobs, etc.). We migrated to consolidate our cloud footprint, which significantly improved our security posture (centralized IAM/VPC) and made managing multi-region deployments much easier.”
AI model providers / LLM APIs
Your LLM choice represents the intelligence behind your application. What it actually does depends on your product: maybe it's a sales assistant querying customer data to generate forecasts, or a note-taker for developers that taps into running apps and GitHub repos for context. The model you choose directly impacts capabilities, costs, and user experience. You'll need to decide between commercial API providers (pay-per-token, fully managed) or self-hosting open-source models (upfront infrastructure investment, but full control).
Here are the key decision factors:
Budget: API costs can stack up quickly at scale, but self-hosting requires significant infrastructure investment
Data control: How important is control over privacy and compliance?
Capabilities: What do you require? Content generation, coding, reasoning, multimodal?
Team expertise: How much AI/ML expertise does your team have?
Usage volume: What's your expected scale?
Your main options are:
OpenAI (GPT)
OpenAI (GPT) is the market leader and most widely adopted LLM provider. Excellent documentation, community support, and robust API. GPT is a strong generalist, well-suited to many applications. The chief downside is high cost for the more capable models: OpenAI's premium LLMs (o1-pro, GPT-4, GPT-5.2 Pro), are among the more expensive options.
OpenAI is a solid pick for teams looking for a vast ecosystem, solid support, and those able to keep a tight leash on budget. You could leverage one of OpenAI’s cheaper models (which are conversely among the least expensive around), though their capabilities can vary wildly. Some of the less costly options (like GPT-5 mini or nano) trade reasoning depth for speed and price, making them less suited for complex multi-step workflows.
Anthropic (Claude)
Anthropic (Claude) offers models that many consider to be more nuanced than OpenAI’s offerings, with deep “understanding” and strong ability to follow complex instructions. A considerable number of developers find Claude produces more thoughtful, detailed responses and is better at grasping context.
Anthropic’s models are a good fit for use cases like content moderation or areas where safety and quality matter more than raw speed. It weighs in at a slightly higher cost than OpenAI for comparable tiers, making it less viable for very high-volume scenarios. Like OpenAI, Anthropic offers more affordable models that are, once again, less performant for complex workflows due to their reduced capabilities.
Google (Gemini)
Google (Gemini) boasts a massive context window, ideal for applications processing large amounts of text in a single request. While not as widely adopted among AI app developers, integration with Google's suite of services can be a major advantage. Pricing is slightly less than OpenAI (thus less than Anthropic).
Recent models in the Gemini family (e.g., Gemini 2.5 Flash) have wowed coders, earning impressive marks in synthetic benchmarks and earning a reputation as the best developer assistant model yet. The biggest tradeoff is that it's less mature and less proven in production apps compared to Anthropic or OpenAI, with a smaller dev community for support.
Open-source models
Open-source models (Llama, Mistral) via self-hosting can offer comparable performance to proprietary options while keeping sensitive data entirely within your infrastructure. This avoids HIPAA or compliance issues from external transmission and allows fine-tuning on specific datasets. However, self-hosting requires upfront infrastructure investment, ML/DevOps expertise, responsibility for performance and uptime, and generally lower quality than premiere models like Claude or GPT (though the gap is narrowing).
Anand Sainath, Co-Founder & Head of Engineering at Echelon AI:
“We experiment with the newer models as soon as they come through and are able to ‘benchmark’ results compared to the current models in production. Better instruction adherence, longer context, and improved tool usage are all key factors.”
Authentication and identity
Authentication and authorization determine who can access your application and what they're allowed to do inside it. For B2B identity management, this goes well beyond login screens. It encompasses user management, multi-tenancy, and the organizational structures that enterprise customers expect.
Anand Sainath, Co-Founder & Head of Engineering at Echelon AI:
“Treat authentication as a core requirement from day one. It dictates your UX, user onboarding flows, and how you manage third-party integrations. For us, authentication became urgent sooner than anticipated because of Fortune 500 or security-conscious customers.”
There are two elements of your identity infrastructure that need to be enterprise-grade before you can expect enterprise deals: user management and multi-tenancy.
User management complexity grows as you move upmarket. Early-stage apps might get away with simple email/password auth and a single admin role. Enterprise deals demand role-based access control, custom permission sets, user provisioning workflows, and integration with existing identity providers. The gap between "we have a way to log in" and "we're enterprise-ready" is wider than most founders anticipate.
That’s because enterprise-grade user management involves far more complicated flows and patterns than those of the small teams you’re likely to serve at first. From identity merging and deduplication to handling tokens and SSO migrations, enterprise user management isn’t just “the same thing, but bigger.” It’s a fundamentally different set of tools and processes purpose-built for scale.
Multi-tenancy (the ability to serve multiple organizations from a single application instance while keeping their data and configurations isolated) is foundational for B2B SaaS. Enterprise customers expect their users, roles, and permissions to be managed within their own organizational boundary. And for B2B2X scenarios, they also expect to manage their own tenants.
As you scale, you'll encounter customers with complex organizational hierarchies: parent companies with subsidiaries, departments with distinct access requirements inside a larger org, contractors who need limited visibility. B2B2X companies need delegated administration so their IT teams, and their customers’ teams, can manage access without filing support tickets. They require audit trails showing who did what and when. These are table stakes for enterprise.
Kapil Chhabra, Co-Founder & CPO at WisdomAI:
“If I have one piece of advice about the timing and implementation of enterprise authentication, it’s this: For B2B products, you can't ever be too early.”
Key decision factors:
Target market: Are you targeting SMBs or large enterprises? Enterprises require capabilities like SSO, SCIM provisioning, and sophisticated tenant management.
Speed to market: What are your build vs. buy sentiments and tolerances?
Budget: What can you allocate for authentication infrastructure?
Multi-tenancy needs: Do you need organizational hierarchies, delegated admin, and per-tenant configuration out of the box?
Maintenance capacity: How much engineering time can you dedicate to authentication and user management upkeep?
Your main authentication and identity options are:
Provider Type | Enterprise readiness | Implementation effort | When to use |
|---|---|---|---|
Managed providers | High; SSO, SCIM, multi-tenancy, MFA typically included | Low; Prebuilt components, minimal code to integrate | B2B startups targeting enterprise deals without dedicated authentication teams |
Open-source (self-hosted) | Moderate; Enterprise features often require additional development | High; You own deployment, compliance, and upgrades | Teams with strong DevOps and budget sensitivity to per-MAU pricing |
Built-in framework auth | Low; Rarely includes multi-tenancy or org management | Low; Comes with the backend framework | Very early validation stages where basic login suffices |
Roll-your-own (homegrown) | Variable; Depends on what you build and maintain | Very high; Security, user management, everything from scratch | Almost never; the overhead and security risks outweigh the control |
Managed authentication providers
Managed authentication providers handle all (or most) aspects of authentication, authorization, and user management for you. These platforms vary in out-of-the-box functionality, with some offering robust multi-tenancy, organizational management, and delegated admin capabilities alongside core auth. Managed providers help you meet enterprise standards without tearing focus from your core product.
They typically offer pre-built components, robust documentation, and compatibility with modern auth protocols (OIDC, SAML, OAuth). Integration can be as simple as copying a few lines of code, instantly connecting your product with user databases, MFA, session management, and organizational structures. A strong pick for B2B startups that want to focus on their core product rather than becoming authentication and identity management experts.
If you're pre-Series A and targeting B2B, having these capabilities ready can dramatically influence enterprise deals. The drawbacks are ongoing costs (typically based on Monthly Active Users, or MAUs), potential vendor lock-in, and less control over underlying infrastructure. However, the best practices built into these platforms would take months to implement in-house. Pricing can ramp up if you are growing quickly, so look for those offering startup/free tiers and clarify how many SSO connections and tenant management features come with your plan.
Open-source solutions
Open-source solutions are self-hosted authentication platforms you run on your own infrastructure. This gives you complete control over implementation but also all the responsibilities: building secure deployment, hosting while following compliance rules (yours and your customers'), upgrading to prevent exploits, tracking vulnerabilities, and building out multi-tenancy and user management features that may not come fully baked.
Some organizations favor this route because they own everything and can customize almost anything. A potential choice for those with niche data governance requirements, companies who need deep customization at any cost, or teams with strong DevOps capabilities confident they can save budget by avoiding per-MAU pricing.
The downside is considerable overhead and tech debt that becomes massive at scale. Building and maintaining auth, user management, and multi-tenancy, even with an open-source foundation, is a significant investment. Enterprise features like SSO, SCIM, and organizational hierarchies frequently require substantial additional development work.
Built-in framework authentication
Built-in framework authentication uses libraries and patterns built into your backend (Django's auth system, Passport.js for Next.js, and so on). These provide varying levels of security and capabilities, typically with minimal implementation of basic user authentication (signup, login, sessions) as part of the framework's environment.
The advantage is speed getting started. Good fit for very early-stage startups validating product-market fit or apps with basic authentication needs. The drawback is that these options rarely include multi-tenancy, organizational management, or the user management sophistication that B2B customers expect.
For the vast majority of B2B SaaS products, the strategic imperative is building a great core product while trusting foundational components to expert providers. Built-in framework auth has advantages in early days, but most options don't scale to meet enterprise demands.
Roll your own auth
Building from scratch is the nuclear option: auth, user management, and multi-tenancy yourself from basic building blocks. No open-source frameworks to rely on (or hold you back), but a tremendous amount of effort, expertise, and time required. Security vulnerabilities like broken auth methods, insecure session management, and exploitable gaps can easily arise from non-standard implementation. Putting out fires in a homegrown auth stack becomes a distraction from refining your core product.
In today's market, building authentication and user management from scratch makes no practical sense for startups: it's slower, more expensive, less secure without abundant expertise, and leaves you maintaining critical security infrastructure instead of pushing your product forward.
Abhinav Y., Builder at Different AI:
“My two cents for AI startup founders: It’s better to bake enterprise identity in your architecture and design choices from day one rather than trying to retrofit it later on.”
Deployment
Your deployment platform is where your application lives and runs. This choice affects how you build code, how you serve requests, and how you scale. For B2B AI applications, you need a platform that scales reliably, deploys quickly, and ideally reduces operational overhead so your team can focus on building features rather than troubleshooting infrastructure.
Key decision factors:
Application type: Frontend-heavy, full-stack, backend services, long-running tasks?
Connection requirements: Do you need persistent connections, background workers, or scheduled jobs?
Scale expectations: What's your expected traffic volume and patterns?
Control vs. speed: How important is deployment speed versus fine-grained control?
Geographic requirements: Do you need global deployment, regional hosting, or data residency compliance?
Your main deployment options are:
Vercel
Vercel is the go-to platform for frontend developers, created by the team behind Next.js. Excels at deploying static sites and serverless functions with a smooth developer interface. Code is parsed at build time and server-side logic deploys as AWS Lambda functions, with automatic scaling. Git-based deployments, automatic preview environments for every PR, and global CDN out of the box.
Vercel is ideal for frontend-focused teams, especially those using Next.js or React, or applications where the frontend is the primary complexity. But, depending on your use case, the downsides can be significant: serverless functions have strict memory limits, execution limits, and cold starts that add latency. Because Vercel focuses on stateless, serverless architectures, it's less suited for stateful backend services, long-running tasks, or persistent connections. Increasing bandwidth, memory, CPU, and storage charges get passed through to you.
Railway
Railway uses a custom builder that automatically builds and deploys applications from GitHub or Docker with minimal configuration. Unlike Vercel's serverless-only approach, Railway supports persistent services, background workers, and long-running processes (all critical for AI apps needing stateful operations or continuous processing). Usage-based pricing charges only for actual resource consumption, which can be significantly more cost-effective than fixed-tier pricing for applications with variable load. Built-in databases, cron jobs, and automatic scaling.
Railway excels at rapid deployment with a straightforward experience: push to GitHub and it handles the rest. It’s a strong pick for full-stack AI applications needing both frontend and backend complexity covered, startups migrating from costlier platforms, or teams needing persistent connections for real-time features. The drawbacks include: fewer regional options than larger competitors (affecting global latency and data residency), less maturity for compliance needs, and a credit-based system that can shut down apps if you run out.
Cloud providers
AWS (Amazon Web Services) / GCP (Google Cloud Platform) / Azure / Cloudflare offer unparalleled control, scalability, and feature breadth at the expense of much greater complexity and operational overhead. These platforms give you the full spectrum: compute (EC2, Azure VMs, Compute Engine), managed Kubernetes, serverless functions (Lambda, Azure Functions, Cloud Functions), AI-specific services (SageMaker, Azure AI, Vertex AI), and virtually unlimited configuration options.
This approach is ideal for AI startups with specific requirements and deep DevOps expertise. However, the tradeoffs are steep: significantly higher learning curve, need for dedicated team members, complex pricing that can spiral without careful administration, and substantial time investment. Most early-stage startups should exhaust simpler options first, but mature organizations with enterprise customers, specialized compliance needs, or those building custom AI infrastructure may eventually need this level of control.
Abhinav Y., Builder at Different AI:
“The two biggest variables that affected our decision to migrate from our initial provider were the flexibility and scalability of the underlying platform, and, of course, cost considerations.”
Observability and monitoring
Observability and monitoring track your application's health and reliability in production. Observability extends beyond simple logging by connecting disparate data points: performance metrics, customer support requests, issue tracking. These help you detect patterns to not only mitigate degradation but also optimize for better throughput. Downtime can be especially costly for B2B AI applications (depending on your SLAs), and poor latency leads to negative user experience. Tackling these challenges proactively cements your product as enterprise-ready.
Abhinav Y., Builder at Different AI:
“AI, like any production-grade software at scale, needs practical and effective observability—not just for incidents, but to know the limitations of the architecture, stacks, and systems. Since most AI calls are long-running and costly, you need a good handle on the performance, reliability, and operational excellence of these subsystems.”
Key decision factors:
Monitoring scope: What do you need to monitor? If your application is tied to specific data types, what kind and how is it queried?
Architecture complexity: Microservices or monolith? Single server or multiple clusters?
Scale: What's your current and future scale?
Alerting needs: Do you need real-time alerts and incident management?
Integration requirements: How important is integration with other tools and workflows?
Your main observability options are:
Dimension | Full-stack observability platforms | Dev-focused tools | Open-source stacks | Built-in platform monitoring |
|---|---|---|---|---|
Scope | Infrastructure, applications, databases, network; one single source of truth | Error tracking, performance, monitoring, user experience visibility | Modular; lets you assemble components to fit your specific needs | Basic logs (e.g., API requests), CPU, memory, bandwidth, network per service |
Complexity | Feature-rich, but usage-based pricing scales with data volume | Light-weight by default; easier to start with than full platforms | Moderate-to-high investment. You handle deployment, scaling, maintenance, and configuration; | Minimal. Included with hosting |
When to use | Complex architectures, enterprise SLAs, or teams that need real-time alerts and cross-system visibility | Early-stage startups focused on catching bugs first, before infrastructure monitoring becomes critical | Teams with dedicated DevOps bandwidth who want full controls and can absorb the maintenance overhead | Earliest stages only; useful as a first signal, not a diagnostic tool |
Observability platforms
Comprehensive observability platforms (Datadog, New Relic, Dynatrace) provide full-stack visibility across infrastructure, application processes, databases, network performance, and the entire DevOps stack. These platforms consolidate data from many signals and integrations into a single source of truth, helping teams monitor dynamic flows with analytics, alerts, and collaboration capabilities.
These excel at giving your team information needed to fix issues, optimize inefficiencies, and communicate with collaborators. A good fit for B2B applications where reliability is critical or teams managing complex architectures with real-time implications. Usage-based pricing increases with data volume, calling for larger budgets or more conservative use at scale. A hefty contract with one of these platforms might be overkill for early-stage startups with simple architecture.
Dev-focused tools
Dev-focused tools (Sentry, LogRocket, BugSnag) hone in on error and performance monitoring while offering visibility into user experience. Not as comprehensive as full observability platforms, but excel at managing monitoring for dev-centric environments like microservices and early-stage failure points.
These are typically lighter-weight and easier to start than 360-degree monitoring platforms. Best for dev teams wanting to catch and fix bugs quickly or early-stage startups needing basic monitoring without full observability complexity. The tradeoff is limited scope. You'll likely need additional tools for infrastructure and database performance insights or network issues as you scale.
Open-source solutions
Open-source solutions (ELK, LGTM, ClickStack) are an option, but open-source observability is rarely one solution alone. ELK (Elasticsearch, Logstash, Kibana, potentially with Beats for data shipping) is one example: at least three different open-source options where each component serves a different purpose. These DIY stacks can stand toe-to-toe with out-of-the-box solutions, but construction and maintenance are up to you.
Advantages include control and modularity: pick what you like, configure how you want, get the output you're looking for. Some options are offered as managed services with the caveat that you sacrifice some control over deployment. The tradeoff is pretty significant, though: You're responsible for deployment, scaling, upkeep, and configuration. Without dedicated DevOps and bandwidth, that can be a tall order.
Built-in platform monitoring
Many deployment platforms (Vercel, Railway) include basic monitoring dashboards showing limited logs, metrics, and performance data. Railway, for example, offers built-in logs with basic performance data (CPU, memory, network usage) per service. Vercel offers baked-in observability for events like external API requests, and an optional “Plus” plan for more granular access. Usually enough for very early stages; if your application is crashing for want of resources, these can be the first signal. But "free" or "included with hosting costs" reflects the granularity and usefulness of output.
Compared to comprehensive platforms, built-in options are like diagnosing a car's mechanical issue by looking at the speedometer. It's data about performance, but not the same as specialized diagnostic equipment. If you need more observability across dev and production pipelines (which you will), you'll need a dedicated stack or external tools.
Anand Sainath, Co-Founder & Head of Engineering at Echelon AI:
“You need deep visibility into inputs, outputs, and reasoning traces. Without this, you are literally flying blind and cannot explain why the AI responded the way it did. This is crucial for debugging production performance.”
Evaluation
Unlike standard applications, AI apps can produce highly variable output. Evaluation tools assess the quality, accuracy, and reliability of your AI application's outputs (distinct from observability, which monitors system performance). For B2B AI applications, evaluation is crucial for ensuring your AI delivers consistent, trustworthy results that meet business demands.
This includes measuring response accuracy, detecting hallucinations, assessing relevance, checking for bias or toxicity, and validating that outputs align with user expectations. Unlike traditional software testing with deterministic pass/fail criteria, AI evaluation requires sophisticated approaches because LLM outputs are non-deterministic and context-dependent.
Abhinav Y., Builder at Different AI:
“Before you go deep into building core, AI-driven product capabilities, invest in a solid and easy-to-update eval framework. Given the nondeterministic nature of the outputs from GenAI platforms, it is imperative to have an evolving and well-tested eval framework to have any confidence in the output of your own systems.”
Key decision factors:
Quality priorities: What aspects of AI output matter most? Accuracy, relevance, safety, tone, factual correctness?
Scale requirements: Do you need automated evaluation at scale or human-in-the-loop review?
System type: Are you evaluating simple text generation, agentic workflows, or RAG systems?
Dataset strategy: How will you create test datasets and define success criteria?
Integration needs: Do you need evaluation in CI/CD pipelines or during active development?
Your main options for evaluation tools are:
DeepEval
DeepEval is an open-source Python framework designed like Pytest but for LLM testing. Offers over a dozen prebuilt metrics including hallucination detection, answer relevancy, faithfulness, bias detection, and toxicity checks. Uses advanced techniques like G-Eval (asking LLMs to score based on rubrics), LLM-as-a-judge evaluation, and component-level testing. Integrates easily with CI/CD pipelines for automated regression testing with each code change.
The framework treats evaluations as unit tests, making it familiar to developers, and includes synthetic dataset generation from your knowledge base. Best for teams wanting developer-friendly evaluation integrated into their workflow, Python-based projects, or those building RAG or agentic systems. The tradeoff is that it's primarily code-centric without a rich visual UI, and metrics can occasionally produce unexpected scores as the library matures.
RAGAS
RAGAS is an open-source Python framework purpose-built for evaluating RAG pipelines. Provides specialized metrics like Context Precision (how relevant are retrieved documents), Context Recall (did retrieval find all necessary information), Faithfulness (is the answer grounded in retrieved context), and Answer Relevance (does the response address the question).
RAGAS offers deep, component-level insights into each stage of your RAG pipeline, helping identify whether issues stem from retrieval, generation, or context use. Highly extensible with support for custom metrics. Ideal for organizations pursuing RAG use cases or those where RAG is core functionality. RAGAS has become the de facto standard for RAG evaluation across the AI dev community. The main limitation is that metrics rely on API calls to powerful LLMs like GPT-4, which can incur significant token costs. Also, as a relatively new project, some metrics can be brittle, and it's code-centric without a built-in UI.
LangSmith
LangSmith is a debugging and evaluation platform specifically designed for LangChain applications, offering end-to-end tracing, monitoring, and testing capabilities. Provides visual workflow inspection to see exactly how multi-step agent interactions unfold, automated evaluation utilities, and production monitoring with real-time alerts. Excels at tracing complex multi-agent workflows, assessing tool usage patterns, and identifying bottlenecks in agent reasoning.
LangSmith offers dataset management, A/B testing for prompts, and collaborative debugging. The go-to solution for teams using LangChain or LangGraph, applications with complex agent orchestration, or those needing both development-time evaluation and production monitoring in one platform. The most significant limitation is tight coupling with LangChain. Migrating non-LangChain applications presents substantial challenges due to framework-specific dependencies.
Roll your own eval
Rolling your own evaluation framework or process can be time-consuming and technically demanding, but allows for enormous freedom and dataset flexibility. The creator of DeepEval, Jeffrey Ip, noted that it took a little over five months to bring the open-source framework to life. Based on Ip’s own challenges, expect to navigate blockers like the inherent inaccuracy and unpredictability of LLM evaluation metrics, and the need to gather and prepare datasets that cover all edge cases. Here, the core challenge is how to interpret that data, which means looking at it as an agent would.
During debugging, you must reverse-engineer the agent's decision process. Maybe the answer is missing entirely, or just poorly worded—that’s a problem of context. Perhaps the correct signal is drowned out by irrelevant information in the context window (“noise”), or the agent simply lacks the right tool to execute the task. Echelon AI Co-Founder Anand Sainath recommends a "golden set of expectations”: ground truths that help you objectively measure if changes are actual improvements.
Anand Sainath, Co-Founder & Head of Engineering at Echelon AI:
“We hand-rolled an evaluation framework with extensive ‘golden datasets.’ This acts as ‘offline observability,’ giving us the confidence to swap model providers and immediately take advantage of the latest advancements without regression. We didn't have a ‘golden set of expectations’ on day one, but we curated it by partnering with domain experts.”
B2B AI tech stack choices for long-term success
If there's one thing to take away from this guide, it's that these decisions don't exist in isolation. Every time you say “yes” to one option, you’re saying “no” to many others. Ideally, your “yes” for which backend to use dovetails neatly with your “yes” in frontend, deployment, and agentic framework. It won’t always be so tidy, though, and sometimes a choice will be a foregone conclusion due to previous decisions. But the takeaway here is simply that you can’t (and shouldn’t) say “yes” to everything.
A few key principles hold true across nearly every category we discussed:
Start with what your team knows, but keep an eye on what's coming. If your team has deep expertise in a particular language or framework, lean into that strength. Just because something is more popular in the AI community doesn't automatically make it right for you. But consider whether that choice will constrain you later, especially around AI-specific tooling, scaling, and enterprise readiness.
Anand Sainath, Co-Founder & Head of Engineering at Echelon AI:
“You need to be aware of all of the advancements, and see which ones would make an impact on your product. Skip the rest.”
Let your use case and target customer drive your choices. A chatbot serving internal employees has different requirements than a customer-facing one. High-volume, low-latency apps need different infrastructure than batch processing systems. Enterprise customers will demand capabilities that SMB customers might not care about.
Build incrementally, but design with room to grow. Get something running. Worry less about finding the perfect solution for every component on day one. But architect with growth and pivoting in mind. The best time to think about foundational components isn't when urgency is dictating terms.
Anand Sainath, Co-Founder & Head of Engineering at Echelon AI:
“If I could change one thing, it would be thinking about evals earlier. But on the flip side, I am glad we focused on attempting to solve actual day-to-day problems for our users. The latter helped shape our product direction every step of the way.”
Budget for total cost of ownership, not just day-zero pricing. The cheapest option is often an illusion. What's cost-effective upfront can turn into a maintenance drain as requirements evolve and feature needs for ancillary elements consume your team's bandwidth.
Abhinav Y., Builder at Different AI:
“Do not build your core product under the assumption that one (or a few) GenAI providers will suffice for all your use cases. Build so that you’re able to plug and play with multiple providers on a per-use-case basis, and with abstractions so it’s easier to adapt to changes (think model upgrades, API contract changes, etc.).”
Recognize that DIY vs. buy is a recurring question. This tension appears throughout your stack. Whether it's observability, deployment, authentication, or evaluation, there's always the temptation to build it yourself. Sometimes that makes sense. Often it doesn't. The calculus usually comes down to whether a given component is core to your differentiation or simply necessary infrastructure.
Anand Sainath, Co-Founder & Head of Engineering at Echelon AI:
“Enterprises have complex configuration requirements (SSO, MFA, RBAC) that are best handled by established, dedicated solutions. Once you are ready to sell to enterprises, do not hand-roll or ‘vibe code’ your auth layer. It will be incredibly painful to debug later.”
Descope exists precisely because building B2B application authentication from scratch rarely makes sense for startups. The same logic applies across many of these categories. Your job is building an AI product that solves real problems for your customers. Everything else is infrastructure that should enable that goal, not distract from it.
Sometimes, you’ll have to accept that early tech decisions you initially felt confident about were missteps. But, at the end of the day, the best tech stack is the one that gets obstacles out of your way so you can solve the problems that matter to your customers. As long as you keep moving toward that goal, you’ll land the right combination sooner rather than later.
