Multimodal caching (image + text): how it works and when to use it
Multimodal caching (image + text), caches image-plus-text lookups, so a repeated vision question is a hit instead of an expensive re-run. Here's how Crowkis does it and why it matters for cost and safety.
Production LLM traffic is deeply repetitive, and repetition is exactly what a bill is made of. Multimodal caching (image + text) is how Crowkis caches image-plus-text lookups, so a repeated vision question is a hit instead of an expensive re-run.
How it works
Crowkis caches image-plus-text lookups, so a repeated vision question is a hit instead of an expensive re-run. It runs inside one Redis-compatible engine, so it composes with semantic caching, agent memory, and the other intelligence layers instead of being a separate service you wire together.
Why it matters
Repetitive LLM workloads are where the money is, and semantic caching can cut costs up to 60-70% on repetitive workloads. Multimodal caching (image + text) is part of what makes that reuse safe rather than reckless, the difference between a cache you trust in production and one you audit after every incident. Runs self-hosted with zero egress, nothing leaves your machine.
Infrastructure earns the critical path one boring, verifiable feature at a time.