500,000x Faster Than Your Cache

31ns vs 16ms. Your Redis cache adds 16 milliseconds per read. Cachee serves the same data in 16 microseconds. Same key. Same value. Same query. One thousand times faster. This is not an incremental optimization. This is a category change.

The Number Everyone Knows

Open your Datadog. Your Grafana. Your CloudWatch. Look at your Redis P50 latency. It's somewhere between 5ms and 30ms. Most of you are seeing 10-20ms. Call it 16ms — the median we see across hundreds of production deployments.

That 16ms is not Redis being slow. Redis is doing its job. That 16ms is physics. Your application opens a TCP connection, sends a command over the network, Redis processes it in microseconds, and the response travels back. The round-trip dominates. Same AZ: 339µs. Cross-AZ: 1-3ms. Cross-region: 30-80ms. Add TLS negotiation, connection pool checkout, serialization overhead, and you're at 16ms before Redis even touches your data.

        16ms per read. At 5 million reads per day, your application spends 80,000 seconds — 22 hours — waiting for cache responses. Every single day. You're burning 22 hours of compute time on network round-trips to a system whose entire purpose is to be fast.
    

What 31ns Looks Like

Cachee is an in-process L1 cache. The data lives in your application's memory. A DashMap with ahash — the same lock-free concurrent hashmap used in Rust's highest-performance systems. No network hop. No TCP. No serialization. A pointer lookup.

1,000x

16 microseconds vs 16 milliseconds

That's not a benchmark artifact. That's not a synthetic test. That's 31ns on every L1 cache hit, measured across 6.28 million requests on production infrastructure on a c7i.metal-48xl. 99%+ hit rate on hot data. Every time.

The Math That Matters

Metric	Redis / ElastiCache	Cachee L1	Difference
Latency per read	16 ms	31 ns	1,000x
5M reads/day latency	80,000 seconds	80 seconds	22 hours recovered
100K reads/sec budget	1,600 sec/sec latency	1.6 sec/sec	99.9% eliminated
Annual compute waste	8,030 hours	8 hours	8,022 hours recovered

Read that last row again. 8,022 hours per year. That's 334 days of compute time your infrastructure is currently spending on cache round-trips. Cachee gives it back.

And 5 million reads/day is conservative. Here's what the waste looks like at real-world scale:

Workload	Reads/Day	Redis Waste/Year	Cachee/Year	Time Recovered
Small SaaS	1M	1,606 hours	1.6 hours	67 days
Mid-market platform	5M	8,030 hours	8 hours	334 days
Trading desk	10M	16,060 hours	16 hours	1.8 years
Large SaaS / ad tech	50M	80,300 hours	80 hours	9.2 years
Tier 1 platform	500M	803,000 hours	803 hours	91.6 years
Hyperscale	1B	1,606,000 hours	1,606 hours	183 years

        A hyperscale platform doing 1 billion cache reads per day burns 183 years of compute time annually on Redis round-trips. With Cachee, the same billion reads take 1,606 hours. The rest is yours.
    

Where the 1,000x Comes From

It's not magic. It's architecture. Redis is a network service. Cachee is an in-process engine. The difference is the same as the difference between reading a variable in memory and making an HTTP call to read it.

The engine is a Cachee-FLU adaptive eviction cache — the same algorithm Google uses in Caffeine (Java), but implemented in Rust with zero-copy Bytes, pre-compressed Brotli/Gzip at write time, and xxHash ETags for 304 Not Modified. Hot keys stay in L1. Cold keys fall through to your existing Redis as L2.

cachee> SET price:AAPL "182.50"
OK (14µs)
cachee> GET price:AAPL
"182.50" (31ns)
cachee> GET price:AAPL
"182.50" (31ns)   ← same latency on the millionth read

Your Redis client already speaks RESP. Point it at Cachee instead of Redis. 177+ commands. Hashes, sorted sets, lists, streams, vectors, Lua scripting. Zero code changes.

But We Didn't Stop at Speed

When your cache is in-process and running at 31ns, you can do things that are architecturally impossible over a network:

Time-travel reads. GET_AT price:AAPL 1711640527445 — the exact value at any millisecond. Debug a production incident by rewinding your cache. Prove to your FINRA auditor what data your system saw at execution time.

Snapshot isolation. MVCC_READ price:AAPL 1 — readers never block writers. Your analytics query sees consistent state while the pricing feed writes at full speed. Zero lock contention.

Dependency cascade. CASCADE user:123 — change a source record, every derived cache key auto-invalidates transitively. No stale data. No manual cache busting.

Cache contracts. CONTRACT SET pricing 5000 https://api/prices 10000 — per-key freshness SLAs. Auto-refresh at 80% of deadline. Every refresh logged. Hand the compliance report to your auditor.

Post-quantum attestation. Every cache entry signed with ML-DSA-65 (Dilithium). Tamper detected at read time. Cache poisoning — wrong data served to your application — caught before it matters.

        None of these are possible over a network hop. You can't do snapshot isolation across a TCP connection. You can't sign and verify every read at 16ms without doubling your latency. You can't cascade-invalidate a dependency graph when every operation costs a round-trip. The speed isn't the feature. The speed is what makes every other feature possible.
    

What This Means for Your Business

For a trading desk: 0.1-0.5 bps per order in improved fill quality on $2.5B notional. That's $250K-$1.25M/year in execution quality alone.

For a SaaS platform: API response times drop from 20-50ms to under 2ms. Your P99 becomes someone else's P50.

For an AI pipeline: embedding lookups at 31ns instead of 5ms from a vector database. Your model spends time thinking, not waiting.

For your infrastructure budget: L1 absorbs 99%+ of reads. Your ElastiCache cluster drops from 6 nodes to 1 fallback. $10K-$20K/year in Redis you don't need.

The Origin Story

Cachee wasn't built as a cache company. It was built inside H33, a post-quantum cryptography platform that processes 2.17 million authentications per second. STARK proof lookups were bottlenecking the pipeline at 339µs through Redis. We built an in-process L1 and dropped it to 0.059µs. That cache became Cachee.

A post-quantum cryptography company that built the fastest cache engine in the world because it had to.

See 1,000x for Yourself

Watch 31ns race 16ms. Try commands Redis can't do. See the coherence.

Live Demo Full Benchmark