Stop paying for the
same answer twice.
Cachly Semantic Cache serves LLM responses based on what a prompt means — not whether it matches character-for-character. pgvector HNSW similarity search, sub-100ms lookups, GDPR-compliant on German servers.
No credit card · €0 to start
Why semantic beats exact-match
A traditional cache only helps when the input is byte-identical. Real prompts never are.
Match on meaning, not strings
Two prompts that mean the same thing get the same cached answer. Embeddings + pgvector HNSW find the nearest prior response above your similarity threshold.
Every hit is a token you don't pay for
Repeated and near-duplicate prompts are the bulk of most LLM bills. Serve them from cache and cut spend on your most expensive model calls.
Sub-100ms lookups
HNSW approximate-nearest-neighbour search returns a hit in single-digit milliseconds — far faster (and cheaper) than a fresh completion.
GDPR by default
Cached prompts and completions stay on German Hetzner servers. Client-side PII redaction keeps sensitive strings out of the cache entirely.
How it works
Point your client at Cachly
Wrap your existing LLM call with the Cachly SDK or route through the MCP server. No prompt changes.
We embed & search
Each prompt is embedded and compared against prior prompts with pgvector HNSW. Above threshold → cache hit.
Serve or store
A hit returns the stored completion in milliseconds. A miss calls your model, then caches the answer for next time.
Pricing that scales with your cache
Start free, upgrade for more storage and Dragonfly-backed sub-millisecond recall. Same account, same dashboard as every Cachly product.
Dev
- 200 MB cache storage
- pgvector HNSW similarity search
- Sub-100ms lookups
- €15/mo billed annually
Pro
Popular- 900 MB cache storage
- pgvector HNSW similarity search
- Sub-100ms lookups
- €39/mo billed annually
Speed
- 900 MB cache storage
- pgvector HNSW similarity search
- Dragonfly · sub-ms recall
- €63/mo billed annually
Need a production HA cluster? See all plans →
Already caching? Give your team a shared Brain.
The same platform that caches your LLM calls can remember what your whole team has learned. Team Brain turns every fix, decision, and gotcha into auditable shared memory your AI tools recall automatically.
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