Cache the Vercel AI SDK in your API documentation bot with Crowkis
Building API documentation bots on the Vercel AI SDK? Add a semantic cache so the same endpoint questions from every developer 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 API documentation bots on the Vercel AI SDK? Add a semantic cache so the same endpoint questions from every developer stop costing full price.
Building AI search on Haystack? Add a semantic cache so popular queries hit again and again stop costing full price.
Cache invalidation for AI answers, done right. A practical, Crowkis-grounded take, no hype, just what actually moves cost, latency, and safety.
Reasoning reuse (cache the chain of thought), stores a chain-of-thought trace as a reusable step graph and replays it for the next query that shares its shape, at roughly 15% of the original token cost. Here's how Crowkis does it and why it matters for cost and safety.
OpenAI and Anthropic added prompt caching, and it's genuinely useful. But it only discounts the prefix you repeat verbatim. The moment the wording changes, you pay full price again.
Building data analysis agents on Dify? Add a semantic cache so repeated tool calls and the same analytical questions stop costing full price.
Building contract analysis tools on the Gemini SDK? Add a semantic cache so the same clause questions across documents stop costing full price.
Building customer support bots on LiteLLM? Add a semantic cache so repeat questions from every customer, all day stop costing full price.
Building ecommerce assistants on Haystack? Add a semantic cache so the same product and policy questions across shoppers stop costing full price.
Multi-tenant AI infrastructure without leaks. A practical, Crowkis-grounded take, no hype, just what actually moves cost, latency, and safety.
Long-term agent memory, gives agents durable, per-(agent, user) memory recalled by relevance blended with recency, so an assistant remembers across sessions. Here's how Crowkis does it and why it matters for cost and safety.
No prompt engineering, no model downgrade, no accuracy trade. Just stop sending the model questions it has already answered in slightly different words.
Building HR assistants on Dify? Add a semantic cache so the same policy questions from every employee stop costing full price.
Building onboarding assistants on the Gemini SDK? Add a semantic cache so every new hire asking the same first questions stop costing full price.
Building coding assistants on LiteLLM? Add a semantic cache so the same explanations and boilerplate reasoning, dozens of times a day stop costing full price.
Building healthcare Q&A assistants on Haystack? Add a semantic cache so recurring policy and triage questions stop costing full price.
PII and GDPR in your LLM cache. A practical, Crowkis-grounded take, no hype, just what actually moves cost, latency, and safety.
Contradiction-aware memory consolidation, retires a fact when a new one contradicts it (kept for history, never deleted), so recall returns the current answer, not all the old ones. Here's how Crowkis does it and why it matters for cost and safety.
Credit where it's due: the first semantic caches showed the world this works. Then we asked what a production-grade version would look like if you owned every layer.
Building IT helpdesk bots on Dify? Add a semantic cache so the same tickets and fixes, endlessly stop costing full price.