Practical reads to help you spend less on AI.
Guides, benchmarks and deep dives on semantic caching, agent memory, LLM cost and LLM classification, from the team building Crowkis and Curva.
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Confidence scoring on every hit: how it works and when to use it
Confidence scoring on every hit, returns a per-hit confidence score (a geometric mean of similarity, freshness, trust, validation, and domain accuracy) so you decide the bar reuse must clear. Here's how Crowkis does it and why it matters for cost and safety.
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You might not need a vector database
Pinecone, Qdrant, Weaviate, excellent tools, genuinely. But a lot of teams reach for a whole vector database to do something a meaning-aware cache already does, with less to operate.
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Why self-hosted, zero-egress AI infra is winning
Why self-hosted, zero-egress AI infra is winning. A practical, Crowkis-grounded take, no hype, just what actually moves cost, latency, and safety.
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Freshness control (TTL + webhooks): how it works and when to use it
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.
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Every semantic cache calls OpenAI to understand a question it already answered. Ours doesn't.
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.
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Cutting your OpenAI bill without cutting quality
Cutting your OpenAI bill without cutting quality. A practical, Crowkis-grounded take, no hype, just what actually moves cost, latency, and safety.
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Smart semantic eviction: how it works and when to use it
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.
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Semantic caching, explained without the jargon
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.
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The hidden cost of chain-of-thought reasoning
The hidden cost of chain-of-thought reasoning. A practical, Crowkis-grounded take, no hype, just what actually moves cost, latency, and safety.
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Anti-poisoning write pipeline: how it works and when to use it
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.
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The 3am bill: how a runaway agent loop quietly torches your LLM budget
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.
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Cache invalidation for AI answers, done right
Cache invalidation for AI answers, done right. A practical, Crowkis-grounded take, no hype, just what actually moves cost, latency, and safety.
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Reasoning reuse (cache the chain of thought): how it works and when to use it
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.
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Prompt caching vs semantic caching: what the provider feature doesn't cover
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.
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Multi-tenant AI infrastructure without leaks
Multi-tenant AI infrastructure without leaks. A practical, Crowkis-grounded take, no hype, just what actually moves cost, latency, and safety.
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Long-term agent memory: how it works and when to use it
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.
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How to cut your OpenAI bill by 60% without touching your prompts
No prompt engineering, no model downgrade, no accuracy trade. Just stop sending the model questions it has already answered in slightly different words.
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PII and GDPR in your LLM cache
PII and GDPR in your LLM cache. A practical, Crowkis-grounded take, no hype, just what actually moves cost, latency, and safety.
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Contradiction-aware memory consolidation: how it works and when to use it
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.
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GPTCache proved the idea. We went and rebuilt the engine underneath.
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.
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Prompt injection: detection at the infrastructure layer
Prompt injection: detection at the infrastructure layer. A practical, Crowkis-grounded take, no hype, just what actually moves cost, latency, and safety.
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Knowledge-graph memory: how it works and when to use it
Knowledge-graph memory, stores subject-relation-object edges you can traverse multi-hop, so 'who works at the customer's company?' is a graph walk, not a guess. Here's how Crowkis does it and why it matters for cost and safety.
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LangChain's cache is exact-match. Here's the two-line upgrade.
LangChain's built-in caches are great until a user rephrases the question. Swap in a semantic cache and the paraphrases start hitting, without changing a line of your chains.
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Freshness vs speed: the LLM cache tradeoff
Freshness vs speed: the LLM cache tradeoff. A practical, Crowkis-grounded take, no hype, just what actually moves cost, latency, and safety.