Complete Guide to AI SDKs: From Code to Product Success
You picked an AI SDK last month. Maybe Vercel's AI SDK because you're on Next.js. Maybe LangChain because everyone talks about it. Maybe you went straight to the OpenAI API because you hate dependencies.
Now you're three weeks in, your prompt engineering feels like voodoo, your error handling is a mess, and your API costs are climbing faster than your user count. The demo worked great. Production is a different story.
Here's what nobody tells you: the SDK isn't the hard part. Fitting AI into your existing codebase without creating a maintenance nightmare is the hard part.
The SDK Landscape Is Messier Than You Think
The AI SDK space moves fast. Too fast. What worked six months ago is legacy code now.
is the reference implementation. Clean, well-documented, does one thing well. But it's just an API wrapper. You're on your own for streaming, retries, token counting, and everything else production needs.
LangChain tries to be everything. Chains, agents, memory, vector stores, tools. The abstractions look elegant until you need something custom. Then you're fighting the framework instead of building features. The docs are comprehensive but scattered. You'll spend more time reading examples than writing code.
Vercel AI SDK is the new hotness. React-first, streaming-first, actually designed for real products. The useChat hook is genuinely good. But it's opinionated about your stack. If you're not on React and Edge, you're swimming upstream.
Anthropic's SDK is criminally underrated. The prompt caching alone saves you serious money if you're sending the same context repeatedly. But tool calling is different from OpenAI's format, so you can't just swap providers.
Then there's LlamaIndex, Haystack, Semantic Kernel, and a dozen others. Each with their own mental model, their own abstractions, their own sharp edges.
Pick wrong and you'll rewrite everything in three months. Pick right and you still need to understand your existing architecture.
Where Most Integrations Go Wrong
You start simple. Add a chat endpoint. Call the OpenAI API. Return the response. Ship it.
Then reality hits:
Your database queries are slow, so AI responses time out. You add caching. Now cache invalidation is your problem.
Users start sending huge messages. You add token limits. Now you need graceful degradation.
The API goes down. You add retries. Now you need exponential backoff and circuit breakers.
You want to A/B test prompts. Now you need feature flags, logging, and analytics.
You realize your error messages leak prompt details. Now you need sanitization.
Someone uploads a 50MB PDF. Your serverless function dies. You need background jobs and status polling.
Each "small addition" touches five different parts of your codebase. Your AI feature isn't isolated anymore—it's woven through everything.
This is where Glue becomes useful. Before you start jamming AI into random files, you need to understand what you already have. Glue indexes your codebase and shows you the actual architecture: where your API routes live, what your database schema looks like, which files are changing constantly (code churn), and who owns what.
You can see that your auth middleware is 400 lines and touches twelve different endpoints. Maybe don't couple your AI streaming to that. You can see that utils/api.ts is imported by forty files. Maybe that's not where your retry logic should live.
A Better Architecture (That Actually Scales)
Forget "AI features." Think about integration points.
Separate your prompt layer from your application layer. Your AI service should not know about Express routes or React components. It should know about inputs, outputs, and errors. That's it.
OpenAI charges per token. That's the easy cost. The real costs are hidden:
Engineering time. Your team is now debugging prompt behavior instead of fixing bugs. Every feature needs an AI variant. Everything takes longer.
Code complexity. AI features add branching logic everywhere. If the AI call fails, then what? If it returns garbage, then what? If it's slow, then what?
Technical debt. You shipped fast. The AI code is in twenty different files. Nobody wants to touch it. It works but nobody knows how. When GPT-5 comes out, you're looking at a month-long migration.
Monitoring and debugging. Your logs are full of giant prompt strings. Your error messages are useless. You can't reproduce issues because AI is non-deterministic.
This is exactly what teams miss when they bolt AI onto existing products. You're not just adding a feature. You're adding a new category of complexity.
Glue helps here too. When you need to understand how your AI integration spread through your codebase, Glue shows you the full dependency graph. You can see which files import your AI utilities, track technical debt hotspots, and identify knowledge risks (that one engineer who wrote all the prompt code and is thinking about leaving).
Choosing Your SDK: A Decision Framework
Stop asking "which SDK is best?" Start asking "which SDK fits my codebase?"
You're on Next.js, React, Vercel: Use Vercel AI SDK. The path of least resistance. The streaming hooks are excellent. The edge runtime support is real.
You need maximum control and minimum magic: Raw OpenAI SDK. You'll write more code, but you'll understand every line. Good choice for teams that already have strong backend architecture.
You're building complex AI workflows with multiple steps: LangChain or LlamaIndex. The abstractions help when you're chaining prompts, doing RAG, managing agents. Just be ready to read a lot of docs.
You want to switch providers easily: Build your own thin wrapper. Seriously. If provider independence matters, abstractions from others won't fit perfectly. A 200-line wrapper you control beats a 20,000-line dependency you don't.
You're doing RAG or search: LlamaIndex is underrated here. The indexing abstractions are solid. The query engine is flexible. It's less popular than LangChain but often simpler.
The Integration Checklist Nobody Shares
Before you ship AI features to production:
Can you test your prompts? Not manually. Automated tests. With fixtures. That run in CI.
Can you swap models? You will want to try GPT-4, Claude, Llama. Can you A/B test providers without code changes?
Can you rate-limit users? Someone will spam your endpoint. They always do.
Can you see what's actually happening? Logs, traces, analytics. You need visibility before things break.
Can you roll back? When the new prompt makes the AI useless, can you revert without deploying?
Can someone else understand this code? Six months from now, when you're on a different team, will the next person be able to fix bugs?
If you answered no to any of these, you're not ready for production. You're ready for an incident.
What Success Actually Looks Like
Good AI integration feels boring. Your team ships features without fighting the SDK. Your error rates are low. Your costs are predictable. When something breaks, you know why.
The AI code lives in clear boundaries. It doesn't leak into your auth layer or your database queries or your frontend components. When you need to change models, you change one file. When prompts need updates, you version them like any other code.
Your monitoring shows you what's working and what's not. You're not guessing about prompt effectiveness. You have data.
And when you need to understand the full picture—how AI integration affected your architecture, where complexity grew, what needs refactoring—tools like Glue show you the map. You can see code health, track changes, identify patterns.
Most teams skip this step. They integrate AI and hope for the best. Then six months later they're doing a full rewrite because nobody can maintain the mess they created.
Don't be that team. Build it right the first time. Your future self will thank you.