Going from "I have an API key" to a production-ready AI integration involves more than just calling an endpoint. This guide covers the patterns that prevent outages, cost overruns, and security incidents.
Quick reference for the most popular AI API providers.
| Provider | SDKs | Auth | Rate Limits |
|---|---|---|---|
| OpenAI | Python, Node.js, REST | Bearer token (API key) | TPM + RPM limits (tier-based) |
| Anthropic | Python, TypeScript, REST | x-api-key header | TPM + RPM limits (tier-based) |
| Google (Gemini) | Python, Node.js, REST | API key or OAuth | QPM limits (free tier + paid) |
| OpenRouter | OpenAI-compatible | Bearer token | Per-model limits |
| Groq | OpenAI-compatible | Bearer token | Model-specific TPM |
All providers support SSE streaming. OpenRouter and Groq use OpenAI-compatible APIs, so existing OpenAI code works with a base URL change.
Five areas where most AI integrations break in production, and how to avoid each one.
How you handle API keys determines whether your app stays secure.
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Don't
AI APIs fail sometimes. Your code should handle it gracefully.
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Don't
Streaming shows output as it generates, reducing perceived latency dramatically.
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Don't
Every provider has rate limits. Plan for them from day one.
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Don't
Build for the inevitable: API outages, model deprecations, and price changes.
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Don't
A quick decision tree for picking the right SDK.
See detailed endpoint documentation, pricing, and model availability for each provider.
OpenAI and Anthropic have the best developer experience. Both offer well-documented SDKs, clear error messages, and generous free trial credits. If you want access to multiple providers through one API, OpenRouter is the easiest starting point.
Implement exponential backoff with jitter. When you receive a 429 response, wait the duration specified in the retry-after header before retrying. Use a request queue with concurrency limits to prevent hitting rate limits in the first place. Most providers offer higher limits at higher payment tiers.
For user-facing chat interfaces, always use streaming. It makes the response feel 5-10x faster because users see tokens as they generate. For batch processing, background tasks, or structured output (JSON), non-streaming is simpler and works fine.
Use an abstraction layer. Options include OpenRouter (drop-in API compatibility), LiteLLM (Python library), or build your own thin wrapper. The key is keeping provider-specific code behind an interface so swapping models only requires changing a config value.