Vs the field
How Crowkis compares with vector databases, gateways and other caches. 44 articles.
Subscribe with RSSSemantic cache vs vector database: they solve different problems
A vector database is built for large-scale retrieval. A semantic cache is built for safe answer reuse. Using one for the other's job is where teams get burned.
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Crowkis vs Weaviate, Qdrant, and Milvus: stop assembling your cache from parts
Every DIY semantic cache is a vector database, a Redis, a cron job, and a prayer. Crowkis is the version where the parts were designed for each other.
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Crowkis vs pgvector: your database deserves better than your cache traffic
pgvector is a lovely extension for storing embeddings next to your data. Routing every LLM query through Postgres is how lovely things die.
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Crowkis vs Momento: your cache shouldn't bill like the thing it's saving you from
Serverless caches meter every operation. A cache that charges per request in front of an API that charges per request is a strange kind of savings.
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Crowkis vs ElastiCache: managed Redis is still Redis
AWS will happily run an exact-match cache for you at any scale. It will miss your LLM traffic at any scale, too.
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Crowkis vs Memcached: a beautiful fossil meets a new workload
Memcached is the purest cache ever written, and purity is exactly the problem when your keys are sentences.
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Crowkis vs Dragonfly, Valkey, and KeyDB: faster exact-matching is still exact-matching
The new Redis-compatibles race each other on throughput. On LLM traffic they all hit the same wall at full speed: the keys never repeat.
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Crowkis vs OpenAI prompt caching: a discount is not a cache
Provider prompt caching discounts your repeated prefixes. You still call the model, still wait, and still pay, just slightly less. There's a bigger idea available.
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Crowkis vs Anthropic prompt caching: cache writes that bill you are telling you something
Anthropic's prompt caching is excellent at its actual job, cheap long contexts. It was never designed to be your response cache, and the pricing says so.
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Crowkis vs Gemini context caching: renting memory by the hour
Google bills cached context per token per hour, a parking meter for your own prompts. Compare that with a cache you simply own.
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Crowkis vs vLLM prefix caching: different layers, different physics
vLLM's prefix caching saves GPU work inside one inference server. Crowkis saves the inference itself. You probably want both, but only one cuts the bill to zero on a hit.
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Crowkis vs LangSmith: tracing the waste vs deleting it
LangSmith shows you every span of every chain, beautifully. The spans are still billed. There's a component whose job is making the spans not happen.
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Crowkis vs Cloudflare AI Gateway: the edge is the wrong place for trust decisions
Cloudflare's gateway adds caching at the CDN layer, exact-match, eventually-evicted, on someone else's network. Useful plumbing; not a reuse brain.
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Crowkis vs Kong AI Gateway: plugins are not engines
Kong added AI plugins to a great API gateway. A semantic-cache plugin in a proxy is a feature; a semantic cache engine is a product. The difference shows in production.
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Crowkis vs building it yourself: a love letter to the repo you'll abandon
Every team builds the in-house semantic cache once. The prototype takes a week. The production version takes the year you didn't budget. We know, we budgeted it.
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Crowkis vs framework caches: your framework should not own your memory
LangChain, LlamaIndex, and Semantic Kernel all offer cache hooks. Framework caches live and die with the framework. Infrastructure shouldn't.
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Crowkis vs AWS Bedrock prompt caching: the cloud's cache serves the cloud
Bedrock's caching cuts repeated-prefix costs inside one cloud's model garden. Your cache strategy deserves a longer horizon than a vendor's feature page.
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Crowkis vs LangChain InMemoryCache: the default that quietly costs the most
One import gives you LangChain's in-memory exact cache. It's the caching equivalent of a sticky note, gone on restart, blind to paraphrase, local to one process.
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Crowkis vs Upstash: pay-per-request caching meets the request firehose
Serverless Redis with per-request pricing is elegant for occasional workloads. An LLM cache is the opposite of an occasional workload.
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Crowkis vs the dedup script: the cron job that thinks it's a cache
Somewhere in your repo is a script that hashes prompts and skips duplicates. It's doing its best. Here's everything it can't see.
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Crowkis vs Chroma: the prototype's best friend meets the production path
Chroma is wonderful for getting embeddings working before lunch. The qualities that make it great for prototypes are the ones a cache in production can't keep.
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Crowkis vs doing nothing: the most expensive cache is no cache
The default strategy, every query goes to the model, has a precise cost. It's on your invoice, itemized as everything.