What should I use for caching and rate limits on AI traffic?

For AI traffic, a good rule is:

• Cache: use a semantic cache for repeated or near-duplicate prompts/responses
• Rate limit: use a distributed token-bucket or sliding-window limiter backed by Redis or your API gateway
• For LLM-specific traffic: measure and limit by tokens, not just requests

Caching options

1) Prompt/response caching

Best when users ask the same or very similar questions.

Use when:

• prompts repeat often
• responses are deterministic-ish
• you can tolerate serving an earlier answer

Tools:

• Redis for fast key-value caching
• LLM semantic cache libraries/providers for embedding-based similarity matching

Good patterns:

• exact match cache: hash(model + system_prompt + user_prompt + params)
• semantic cache: compare embeddings and reuse if similarity is high

2) Response caching at the gateway

If you have a stable API and identical requests happen often, cache at:

• CDN
• API gateway
• Redis

Useful for:

• system prompts
• tool results
• retrieval results
• expensive pre-processing

3) Retrieval / embedding cache

If your app uses RAG, cache:

• embeddings
• document chunks
• retrieval query results

This can save a lot more money than caching final completions alone.

Rate limiting options

Best practice: token-aware rate limits

For AI APIs, limit:

• requests per minute
• tokens per minute
• concurrency per user/org/API key

Why:

• one request may be cheap
• another may be extremely expensive

Common algorithms

• Token bucket: best general choice
• Leaky bucket: good for smoothing bursts
• Sliding window: good for fairness and accuracy

Backends

• Redis: most common for distributed rate limiting
• API gateway / edge proxy: Cloudflare, Kong, Envoy, NGINX, Apigee
• Managed services: if you want less ops

Practical recommendation

If you want a simple, solid setup:

• Cache: Redis + optional semantic cache layer
• Rate limit: Redis-backed token bucket
• Enforce limits on:
  • requests/minute
  • tokens/minute
  • concurrent generations
• Set separate limits for:
  • per user
  • per API key
  • per organization
  • per model tier

If you want an architecture that works well

• Edge/API gateway: auth + basic request rate limiting
• Application layer: token-aware rate limiting and caching
• Redis: shared state for both
• LLM middleware: prompt normalization, semantic cache, token accounting

Quick picks by need

• Fastest to implement: Redis + token bucket
• Best for repeated prompts: semantic cache
• Best for controlling cost: token-based limiting
• Best for production scale: gateway + Redis + token-aware policies

If you want, I can recommend a specific stack for:

1. Python/FastAPI,
2. Node.js, or
3. Cloudflare/Kong/Envoy.