# Context Cache API Projects at AI Tinkerers

> Canonical HTML: https://aitinkerers.org/technologies/context-cache-api
> Markdown URL: https://aitinkerers.org/technologies/context-cache-api.md
> Technology record last updated: 2026-03-10T14:09:22Z
> Generated: 2026-09-20T21:48:06Z

Context Caching reduces latency and costs by persisting frequently used model context across multiple API requests.

Google's Context Cache API optimizes long-context workflows by storing massive datasets (like 100,000 lines of code or hour-long videos) in a temporary cache for Gemini models. Instead of re-sending and re-processing the same tokens for every query, developers pay a one-time storage fee (typically $0.01 per 1M tokens per hour) to keep the data warm. This architecture slashes Time to First Token (TTFT) and cuts input costs by up to 90% for repetitive tasks like document analysis or multi-turn chat sessions.

- Official technology site: https://ai.google.dev/gemini-api/docs/caching
- Public AI Tinkerers demos and talks: 1
- Result page: 1 of 1

## Recent Public Talks and Demos

### [Skipping RAG: Gemini Long Context Performance Analysis](https://montreal.aitinkerers.org/talks/rsvp_3urb7RAFgWI)

This project demonstrates how Google's Gemini Context Caching API can be effectively managed through a two-layer caching system to improve performance and reduce costs. The benchmark results are compelling: a 74% cost reduction (from $4.31 to $1.12) and 17% faster response times (21.6s vs 26.3s average) with just a 1-minute TTL over 5 test runs. The implementation features a Redis + Google cache architecture where Redis stores metadata (Google cache IDs, MD5 hashes of content, expiration times) while Google's servers store the actual context content. The system handles cache invalidation automatically through Redis TTL and content change detection via MD5 hashing, creating a seamless developer experience - developers simply pass their content and a cache key, and the system manages everything else. Technical highlights include: Automatic cache invalidation via Redis TTL and MD5 content hashing RESTful integration with Google's Context Cache API Detailed benchmarking system measuring latency, token usage, and costs Comparison of cached vs uncached approaches with real-world data Ruby implementation with minimal dependencies (just Redis and net/http)

- Event context: AI Tinkerers Montreal - December 2024 Meetup at IVADO Labs — 2024-12-03 — Montreal
- Public talk page: https://montreal.aitinkerers.org/talks/rsvp_3urb7RAFgWI

## Related Technologies

- [Gemini](https://aitinkerers.org/technologies/gemini) ([Markdown](https://aitinkerers.org/technologies/gemini.md)) — 188 public demos
- [MD5](https://aitinkerers.org/technologies/md5) ([Markdown](https://aitinkerers.org/technologies/md5.md)) — 1 public demo
- [net/http](https://aitinkerers.org/technologies/net-http) ([Markdown](https://aitinkerers.org/technologies/net-http.md)) — 1 public demo
- [Redis](https://aitinkerers.org/technologies/redis) ([Markdown](https://aitinkerers.org/technologies/redis.md)) — 23 public demos
- [Ruby](https://aitinkerers.org/technologies/ruby) ([Markdown](https://aitinkerers.org/technologies/ruby.md)) — 1 public demo