Cache Haystack in your RAG document search with Crowkis
Building RAG document search on Haystack? Add a semantic cache so the same questions re-running retrieval over the same corpus stop costing full price.
Notes from the nest · 980 posts
Engineering notes written by the people building Crowkis. Comparisons, use cases, economics, internals, security, operations, and nothing written just to rank.
Building RAG document search on Haystack? Add a semantic cache so the same questions re-running retrieval over the same corpus stop costing full price.
Why self-hosted, zero-egress AI infra is winning. A practical, Crowkis-grounded take, no hype, just what actually moves cost, latency, and safety.
Freshness control (TTL + webhooks), expires answers by query-type TTL, webhook invalidation, and version-aware recompute, so a cached price or status never goes quietly stale. Here's how Crowkis does it and why it matters for cost and safety.
The dirty secret of most semantic caching setups: to save you a model call, they make an embedding API call, sending every prompt off-box and billing you for the privilege. Crowkis does the understanding locally.
Building voice assistants on Dify? Add a semantic cache so latency-sensitive, repetitive spoken queries stop costing full price.
Building sales enablement tools on the Gemini SDK? Add a semantic cache so reps asking the same product questions stop costing full price.
Building SQL generation tools on the Vercel AI SDK? Add a semantic cache so the same schema questions and query shapes stop costing full price.
Building internal copilots on Haystack? Add a semantic cache so employees asking overlapping questions of the same knowledge base stop costing full price.
Cutting your OpenAI bill without cutting quality. A practical, Crowkis-grounded take, no hype, just what actually moves cost, latency, and safety.
Smart semantic eviction, scores what to keep by recency, frequency, isolation, and compute cost, so an expensive reasoning answer outranks a cheap, recently-hit triviality. Here's how Crowkis does it and why it matters for cost and safety.
If you're paying for an LLM and haven't met semantic caching yet, this is the five-minute version. No math, no buzzwords, just why it saves money and how it works.
Building email drafting tools on Dify? Add a semantic cache so similar drafts requested over and over stop costing full price.
Building knowledge base assistants on the Gemini SDK? Add a semantic cache so the same lookups across a team all day stop costing full price.
Building devops copilots on the Vercel AI SDK? Add a semantic cache so the same runbook and incident questions stop costing full price.
Building chatbots on Haystack? Add a semantic cache so high-volume conversational traffic that repeats constantly stop costing full price.
The hidden cost of chain-of-thought reasoning. A practical, Crowkis-grounded take, no hype, just what actually moves cost, latency, and safety.
Anti-poisoning write pipeline, scores every write through five stages (coherence, content policy, source trust, tenant isolation, neighbourhood anomaly) before it can ever be served. Here's how Crowkis does it and why it matters for cost and safety.
Agents don't fail loudly. They loop, politely, expensively, and you find out on the invoice. A budget wall that's enforced before the spend, not discovered after it.
Building code review bots on Dify? Add a semantic cache so the same review patterns across pull requests stop costing full price.
Building research assistants on the Gemini SDK? Add a semantic cache so overlapping literature and summary questions stop costing full price.