Every Redis call costs you 0.5-3ms in network round-trip time. Cachee puts an AI-powered L1 memory layer in front of Redis that serves reads in 1.5 microseconds. No migration. No infrastructure changes. Deploy in under 5 minutes.
Redis is fast. The network between you and Redis is not.
Every Redis GET or SET command requires a network round-trip: serialize the request, send it over TCP, wait for Redis to process it, receive the response, and deserialize. Even on the same VPC, that round-trip costs 0.5-3ms. On cross-AZ deployments, it climbs to 3-5ms. That sounds small until you realize a single API response often requires 5-15 cache lookups. Five lookups at 3ms each means 15ms of pure network wait time before your application logic even starts.
At scale, this compounds into a meaningful portion of your total request latency. Redis itself processes commands in microseconds. The bottleneck is not Redis. The bottleneck is the network between your application and Redis. Pipelining and connection pooling help, but they do not eliminate the fundamental cost of leaving your process to fetch data from another machine.
The solution is not a faster Redis. It is eliminating the network hop entirely for your hottest reads. That is what Cachee does.
A typical API endpoint makes 5 sequential cache lookups to build a response. Here is what that looks like with standard Redis versus Cachee's in-process L1 layer.
With a 99.05% hit rate, fewer than 1 in 100 lookups ever reaches Redis. The rest are served from local memory in 1.5µs. Even your cache misses are faster because Redis only handles the long tail of cold reads, reducing contention on your Redis cluster. See the full benchmark methodology for verified numbers.
Cachee runs an in-process L1 memory cache inside your application. Machine learning models predict which keys will be accessed next and pre-warm them from Redis before your code asks. The result: most reads never leave your process.
The ML prediction layer runs native Rust inference agents in 0.69 microseconds per decision. It learns your access patterns in under 60 seconds and continuously adapts. No TTLs to configure, no eviction policies to tune, no cache warming scripts to maintain. The AI handles all of it autonomously.
Deep dive into the ML pipeline: predictive caching architecture. Full technical walkthrough: how it works.
Cachee is not a Redis replacement. It is a layer that makes your existing cache infrastructure faster. Drop-in RESP proxy mode or native SDK integration. No vendor lock-in, no migration, no data movement.
See how Cachee stacks up against other solutions in our comparison guide.
Independently verifiable benchmarks. No synthetic workloads. These numbers reflect production-realistic access patterns with mixed read/write ratios and variable key distributions.
| Metric | Before (Redis Direct) | After (Cachee + Redis) |
|---|---|---|
| Read Latency (P50) | ~1ms | 1.5µs |
| Read Latency (P99) | 3-5ms | <2µs |
| Hit Rate | 60-80% | 99.05% |
| Throughput (per node) | ~100K ops/sec | 660K+ ops/sec |
| Redis Load Reduction | Baseline | 60-80% fewer calls |
| Infrastructure Cost | Baseline | 40-70% reduction |
The cost savings come from two places: higher hit rates mean fewer origin database calls, and reduced Redis load means you can downsize your Redis cluster. Most customers drop at least one ElastiCache node within the first month. Full benchmark data and methodology: benchmarks. Cost analysis: cut ElastiCache costs.
Start with the free tier. No credit card required. See your Redis latency drop from milliseconds to microseconds in under 5 minutes.