Cache: expensive computations, frequently accessed data, slow API responses, static content. Avoid caching: highly dynamic data, large datasets, sensitive data without proper security.

How do I handle cache invalidation?

Common approaches: time-based expiry (TTL), event-based invalidation (clear when data changes), version-based keys. "Cache invalidation is one of the two hard problems in computer science."

Should I cache AI model responses?

Depends on: determinism (same input = same output?), acceptable staleness, cost savings. Cache: embeddings, classification results, common queries. Be careful with: creative generation, personalised responses.

How do I size my cache?

Monitor: hit rate, miss rate, evictions. Start conservatively, observe patterns. Cache most frequently accessed items. Consider cost of cache miss vs. cost of larger cache.

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Caching

Storing copies of data or computed results in faster storage to reduce latency and load on the original data source.

In-Depth Explanation

Caching stores frequently accessed data in fast storage (memory, CDN) to speed up retrieval and reduce load on primary data sources. Effective caching dramatically improves performance and reduces costs.

Types of caching:

Application cache: In-memory cache within application
Distributed cache: Shared cache across instances (Redis, Memcached)
CDN cache: Edge caching for static content
Database query cache: Cache query results
API response cache: Cache API responses

Cache strategies:

Cache-aside: Application checks cache first, loads from source if miss
Read-through: Cache automatically loads from source on miss
Write-through: Writes go to cache and source simultaneously
Write-behind: Write to cache, async sync to source

Cache considerations:

Expiration/TTL (time to live)
Invalidation (when data changes)
Cache size and eviction policies
Consistency vs. performance trade-offs

Business Context

Caching can reduce latency by 10-100x and dramatically reduce infrastructure costs by offloading expensive operations.

How Clever Ops Uses This

We implement caching strategies for Australian business AI systems, caching embeddings, model responses, and frequently accessed data to reduce costs and latency.