API Design for AI-First Applications: Patterns That Scale
Your traditional REST API isn't going to cut it anymore.
AI-first applications—chatbots, code assistants, document analyzers—behave fundamentally differently than CRUD apps. They need streaming responses. They demand massive context payloads. They produce unpredictable load patterns. And they fail in ways your users have never seen before.
I've watched teams bolt AI features onto existing REST APIs and regret it within weeks. The latency is brutal. The error handling is a mess. And every new AI feature requires architectural gymnastics because the API wasn't designed for this.
Here's what actually works.
The Problem With Standard REST for AI
Traditional REST assumes request-response cycles measured in milliseconds. You POST data, you GET a response, done. Clean and predictable.
AI endpoints don't work like this. A single LLM call might take 5-30 seconds. Token generation happens progressively—waiting for the entire response before returning anything creates a terrible user experience. Your users sit staring at spinners wondering if anything is actually happening.
Worse, AI applications need context. Lots of it. A code assistant needs to see multiple files, their relationships, the project structure, recent changes. A document analyzer needs the full document plus metadata plus related documents. These context payloads are massive—often hundreds of KB to several MB.
Trying to shove this through traditional REST endpoints creates problems:
Timeouts everywhere. Your API gateway gives up after 30 seconds. Your load balancer kills connections. Your frontend assumes something broke.
Memory pressure. Buffering entire responses before sending them means every in-flight request holds massive amounts of memory. Scale this to 100 concurrent users and your servers fall over.
Poor observability. When an LLM call fails halfway through token generation, how do you even log that? Your existing error handling treats it as a generic 500, losing all context about what actually broke.
Stream Everything (Seriously)
Server-Sent Events (SSE) or WebSockets aren't optional for AI-first APIs—they're the baseline architecture.
Streaming solves the UX problem immediately. Users see tokens appearing in real-time. They know the system is working. They can even abort requests early if they realize the AI misunderstood.
But streaming introduces new failure modes. What happens if the connection drops mid-stream? With REST, you retry the request. With streaming, you've already sent partial data. The client needs to track how much it received and potentially request a continuation.
Pattern: include a sequence_id in each chunk. If the stream breaks, the client can resume with POST /api/ai/chat/stream/resume including the last sequence_id it received.
AI applications are context-hungry. Every endpoint needs to efficiently handle large context payloads without grinding to a halt.
The naive approach: accept massive JSON bodies in POST requests. This works until you hit payload size limits (many API gateways default to 1-10MB), and it's inefficient—you're re-sending the same context repeatedly.
Better pattern: context references with server-side caching.
# First, upload context and get a reference
POST /api/ai/context
{
"files": [...],
"symbols": [...],
"metadata": {...}
}
Response:
{
"context_id": "ctx_abc123",
"expires_at": "2024-01-20T10:30:00Z",
"size_bytes": 524288
}
# Then reference it in subsequent requests
POST /api/ai/chat
{
"context_id": "ctx_abc123",
"message": "Explain this function"
}
The server maintains context in a fast cache (Redis, Memcached) with reasonable TTLs. Clients can reuse context across multiple queries without re-uploading. You can even implement smart diffing—clients send only what changed since last time.
This is where something like Glue becomes valuable. When you're building AI features that need code context, Glue already indexes your entire codebase—files, symbols, API routes, relationships. Instead of implementing your own context extraction and caching layer, you query Glue's index. It knows which files are related, what changed recently, who owns what code. Your AI endpoints get better context without reinventing the wheel.
Rate Limiting That Accounts for Cost
Traditional rate limiting counts requests per minute. For AI APIs, this is meaningless.
One request might consume 100 tokens. Another might consume 100,000 tokens. The cost and resource consumption differ by 1000x, but your rate limiter treats them identically.
You need token-aware rate limiting:
class TokenBucketLimiter:
async def check_limit(
self,
user_id: str,
estimated_tokens: int
) -> bool:
current = await redis.get(f"tokens:{user_id}")
if current and int(current) + estimated_tokens > LIMIT:
return False
await redis.incrby(
f"tokens:{user_id}",
estimated_tokens
)
await redis.expire(f"tokens:{user_id}", 3600)
return True
@app.post("/api/ai/analyze")
async def analyze(request: AnalyzeRequest):
# Estimate tokens before making LLM call
estimated = estimate_tokens(
request.context_size + request.query_length
)
if not await limiter.check_limit(
request.user_id,
estimated
):
raise HTTPException(429, "Token limit exceeded")
# Proceed with actual LLM call
Track actual token consumption and adjust limits dynamically. If a user consistently underestimates, tighten their estimates. If they're conservative, let them burst more.
Idempotency Isn't Optional
AI calls are expensive and slow. Users will spam retry when they get impatient. Without idempotency, you'll waste money and resources re-running identical requests.
Standard idempotency keys work, but AI adds a twist—you need to handle partial completion.
@app.post("/api/ai/generate")
async def generate(
request: GenerateRequest,
idempotency_key: str = Header(...)
):
# Check if we've seen this request
cached = await redis.get(f"idem:{idempotency_key}")
if cached:
result = json.loads(cached)
if result['status'] == 'complete':
return result['data']
elif result['status'] == 'in_progress':
# Return 409 with progress indicator
raise HTTPException(
409,
detail={
'status': 'in_progress',
'progress': result['progress']
}
)
# Mark as in-progress
await redis.setex(
f"idem:{idempotency_key}",
3600,
json.dumps({
'status': 'in_progress',
'progress': 0
})
)
# Do the actual work...
When the client retries a request that's still processing, return 409 with progress info instead of starting over. Saves money, reduces server load, and gives users feedback.
Error Messages That Don't Suck
LLM errors are often cryptic. "Context length exceeded." "Invalid request." "Rate limited." None of this helps developers fix the problem.
AI-first APIs need error responses that explain why something failed and how to fix it:
{
"error": {
"code": "context_too_large",
"message": "Context size (156,000 tokens) exceeds model limit (128,000)",
"details": {
"tokens_requested": 156000,
"tokens_limit": 128000,
"tokens_over": 28000
},
"suggestions": [
"Reduce context by removing older messages",
"Use a model with larger context window",
"Split request into multiple chunks"
]
}
}
Include enough detail that developers can debug without digging through logs. Make errors actionable.
Versioning for Rapid Model Changes
AI models change frequently. GPT-4 gets updated. Claude releases new versions. Your fine-tuned models improve. Each change potentially breaks existing behavior.
Traditional API versioning (v1, v2) doesn't work well here because you need multiple dimensions:
API schema version (what fields exist)
Model version (which LLM)
Feature version (which capabilities are enabled)
Pattern: explicit model selection with version pinning:
Let clients specify exact model versions when they need stability. Provide fallback chains for when that version isn't available. Feature flags let you gradually roll out capabilities.
This is another area where Glue helps teams avoid chaos. When you're adding new AI endpoints across your codebase, Glue maps them to features automatically and shows you the full API surface. You can see which endpoints use which models, identify inconsistencies, and spot gaps in your versioning strategy before they become production fires.
Health Checks That Actually Check Health
Standard health checks ping your database and return 200. For AI APIs, this is useless.
Your database might be fine, but if your LLM provider is down or throttling you, the API is effectively dead. Check the things that matter:
Track cost per request. You'll quickly see which features burn money and which endpoints need optimization.
The Architecture Matters
AI-first APIs aren't traditional REST with extra features bolted on. They're fundamentally different beasts that need streaming, context management, token-aware rate limiting, and proper error handling from the start.
Build these patterns in early. Retrofitting them after you've shipped is painful—I know because I've done it. Twice.
And when you're building AI features that interact with your codebase, don't reinvent context extraction. Use tools like Glue that already index and understand your code. Your AI endpoints will be smarter, your architecture will be cleaner, and you'll ship faster.