What should I use for retries, caching, and failover in production AI apps?

In production AI apps, a good rule is:

• Retries for transient failures
• Caching for repeated/expensive requests
• Failover for provider or region outages

Here’s a practical stack that works well.

1) Retries

Use retries for:

• timeouts
• rate limits
• 5xx errors
• temporary network failures

Best practices:

• Exponential backoff with jitter
• Limit retries to 2–3 attempts
• Retry only idempotent operations where possible
• Respect Retry-After headers if the provider sends them

Common tools:

• Python: tenacity, backoff
• Node.js: p-retry, async-retry
• Go: cenkalti/backoff, custom retry middleware

Avoid:

• retrying blindly on validation errors or auth failures
• long retry storms that increase latency and cost

2) Caching

Use caching for:

• repeated prompts
• embeddings
• retrieval results
• static tool outputs
• expensive model responses that don’t need to be fresh

Layers to consider:

• In-memory cache for very fast local reuse
• Redis/Memcached for shared distributed caching
• CDN for public/static AI outputs or assets

Good patterns:

• Semantic caching for similar prompts/queries
• Exact prompt caching for identical requests
• Cache embeddings and retrieval results
• Set clear TTLs and invalidation rules

Common tools:

• Redis is the default choice for most production systems
• For semantic cache:
  • Redis + vector search
  • GPTCache
  • custom similarity cache in your vector DB

3) Failover

Use failover for:

• model provider outages
• regional failures
• quota exhaustion
• severe latency degradation

Good approaches:

• Multi-provider fallback: primary model, then secondary model
• Multi-region deployment: route to another region on failure
• Circuit breakers to stop sending traffic to a failing dependency
• Health checks + automatic routing

Common tools/patterns:

• Circuit breaker libraries
  • Python: pybreaker
  • Java: resilience4j
  • Node: opossum
• API gateways/load balancers
  • AWS ALB/NLB, API Gateway
  • Cloudflare, Fastly
  • Kubernetes ingress + service mesh
• Model routing layer
  • a thin abstraction that can switch providers/models dynamically

Recommended production setup

If you want a simple, reliable default:

• Retries: exponential backoff + jitter, 2–3 attempts
• Caching: Redis
• Failover: secondary model provider + circuit breaker
• Observability: metrics, logs, traces, and alerting

A solid architecture

1. Check cache
2. If miss, call primary model
3. On transient error, retry with backoff
4. If primary keeps failing, trip circuit breaker
5. Route to fallback provider/model
6. Cache successful results
7. Monitor latency, error rates, and cache hit rate

If you want one recommendation per category

• Retries: tenacity / p-retry
• Caching: Redis
• Failover: resilience4j / opossum + a second model provider

If you want, I can also give you:

• a production-grade reference architecture
• code examples in Python/Node/Go
• a vendor comparison for Redis, circuit breakers, and model gateways