Run expensive computation once. Never again.
Cachee turns repeated computation into instant retrieval — including STARK verification, ZK proofs, and high-cost workloads that traditional caches cannot touch. Drop-in Redis replacement. Zero migration.
Post-quantum caching eliminates repeated computation in systems where STARKs, zero-knowledge proofs, fully homomorphic encryption, and verifiable computation make verification the bottleneck. A proof is verified once. The result becomes a reusable, portable asset — served in microseconds or nanoseconds. Computation becomes an asset, not a cost.
Traditional caching saves values — the output of a database query, an API response, a rendered page. The value has no computational weight. It is data at rest.
Cachee saves the result of computation itself. That means:
Run it once. Serve it forever.
Modern systems are shifting to cryptographic computation as the core of their trust model:
All of them share one problem: verification and computation are expensive — and they get repeated constantly.
Cachee removes that repetition. A proof is verified once. Then it becomes a reusable, portable result — served in microseconds or nanoseconds. Not faster compute. No compute.
Computation is a recurring cost
Every request re-verifies
Latency scales with proof complexity
Throughput capped by verification speed
Computation is a one-time investment
Every request retrieves a cached result
Latency is constant: 1.5 microseconds
Throughput limited only by memory bandwidth
Redis works for simple key-value storage. It breaks when:
Cachee handles:
Verify a proof once. Serve the result at 1.5 microseconds. Orders of magnitude faster than re-verification.
Homomorphic computation is expensive. Cache the decrypted result. Never re-run the pipeline for the same input.
Pricing, auth, risk scoring, fraud detection. The decision is computed once. Every subsequent check is a cache hit.
A proof verified in one service is valid everywhere. Cachee makes it portable across your entire architecture.
140+ Redis-compatible commands. Same interface. Different class of system.
For cached proof verification, the speedup is not 10x or 20x. It is orders of magnitude. A STARK verification that takes 25 microseconds uncached becomes 0.085 microseconds cached — a 294x speedup. The verification is not faster. It is eliminated.
This is not a faster cache. It eliminates the need to recompute entirely.
Cachee is built for systems where:
Cachee is the execution layer behind verifiable systems. It does not merely store results. It stores proven results.
Every cached result in Cachee carries four system guarantees that make it not a cache entry, but a computation artifact:
Every value carries a deterministic identity: input hash, computation definition, parameter set, engine version. Two identical outputs from different computations are different cache entries.
Explicit state machine: Active, Superseded, Revoked, Expired, Deprecated. No silent eviction. State transitions are recorded. Truth claims are never silently dropped.
Every read returns value + computation fingerprint + verification status + signatures + provenance + lifecycle state. This is what makes Cachee not Redis.
Configurable per tenant: always verify, trust cached, probabilistic sampling, age-weighted. Regulators get full verification. Internal services get cached trust.
SET mykey result FP <computation_fingerprint>
GETVERIFIED mykey # returns full trust envelope
INVALIDATE mykey REASON "superseded by v2"
SUPERSEDE mykey mykey_v2
STATE mykey # returns lifecycle stateH33 produces verifiable computation. Cachee makes it reusable at scale.
Proof once. Use everywhere.
Post-quantum caching is not about storing data faster. It is about eliminating repeated computation in systems where computation is the bottleneck.
Once a result is proven, it should never need to be recomputed again.
Cachee makes that possible.
brew tap h33ai-postquantum/tap && brew install cachee
cachee init && cachee startEvery page in the verifiable computation infrastructure. Proven computation, not cached data.