Every millisecond of API latency costs you users, revenue, and search ranking. Cachee predicts API access patterns, pre-warms responses in L1 memory, and serves cached results in microseconds instead of milliseconds. No backend rewrites. No infrastructure migration.
See the latency breakdown side by side. A standard database-backed API call takes 20ms. With Cachee's predictive L1 layer, the same call completes in 1.02ms — a 95% reduction before you change a single line of backend code.
The database round-trip and Redis auth check are the two biggest contributors to API latency. Cachee's ML engine predicts which user profiles, auth tokens, and related data will be requested next, pre-loading them into in-process L1 memory. When the request arrives, the data is already waiting — no network hop, no query execution, no serialization overhead. Learn more about how the predictive caching engine works under the hood.
Cachee is protocol-agnostic. The ML prediction layer operates on key-value access patterns, not wire formats. Select a protocol to see the latency breakdown and integration code.
API latency optimization is not just about REST endpoints. Modern applications use GraphQL for flexible data fetching and gRPC for high-performance microservice communication. Cachee's protocol-agnostic ML engine learns access patterns regardless of wire format, delivering consistent sub-2µs cache hits across all three protocols. For deeper optimization strategies, see our guides on edge caching and cache miss reduction.
Real-world measurements from production APIs running on PostgreSQL and MySQL. These numbers are from sustained load tests, not peak-second snapshots. Cachee's predictive caching layer eliminates the majority of database round-trips before they happen.
The 95% reduction in database queries directly translates to lower infrastructure spend. When 99% of API requests are served from L1 memory, your database replicas drop from load-bearing necessities to standby redundancy. Most teams see 60-70% cost reduction within the first billing cycle. Read about real-world cost savings in our benchmark documentation.
Every API endpoint benefits from predictive caching, but the magnitude varies by data access pattern. High-read, low-write endpoints see the most dramatic improvement. These measurements reflect L1 cache hits at the 99th percentile.
| Endpoint Type | Without Cachee | With Cachee | Improvement |
|---|---|---|---|
| User profile lookup | 0ms | 0µs | 12,000x |
| Product catalog | 0ms | 0µs | 16,667x |
| Dashboard aggregation | 0ms | 0µs | 57,143x |
| Auth token verify | 0ms | 0µs | 5,333x |
| Config / feature flags | 0ms | 0µs | 3,333x |
"Without Cachee" reflects typical latency for database-backed API calls including network overhead, query execution, and serialization. "With Cachee" reflects L1 memory cache hits at the 99th percentile. Dashboard aggregation endpoints see the largest raw improvement because they typically trigger multiple JOINs and sub-queries that Cachee pre-computes and caches as a single L1 entry. See full methodology in our benchmark suite.
A typical API request travels from your server to the database, waits for query execution, serializes the response, and sends it back. That round-trip takes 10-50ms on a good day. Under load, it gets worse. Your users notice. Your search rankings suffer.
The fix is not faster databases. The fix is not hitting the database at all for data that has not changed. Predictive caching intercepts API requests before they reach the origin, serving responses from memory in microseconds. This applies equally to REST API caching strategies, GraphQL resolver optimization, and gRPC microservice latency reduction.
Cachee sits between your API and its data sources. ML models predict which responses will be requested next and pre-warm them in L1 memory. When the request arrives, the response is already there — no round-trip, no query, no wait.
API calls are not random. A user who loads a dashboard will request 5-10 related endpoints in sequence. Cachee learns these sequences and pre-loads the next likely responses before the client requests them. This is the core of predictive caching — anticipating demand rather than reacting to it.
This eliminates cold-start latency spikes. Instead of the first request in a sequence being slow (cache miss) and subsequent ones fast (cache hit), every request in the predicted sequence hits L1 memory. The result is consistent sub-2µs response times with near-zero variance.
Static TTLs are a tradeoff between stale data (too long) and cache misses (too short). Cachee's ML layer monitors write frequency per key and adjusts TTLs dynamically. A frequently-updated product price gets a 5-second TTL. A rarely-changed user profile gets hours. This is how cache miss reduction works at scale.
The result: data freshness guarantees without sacrificing hit rate. You get 99% cache hits without serving stale responses. Traditional TTL-based caching forces you to choose between freshness and performance. Cachee gives you both.
Cachee deploys as an intelligent L1 overlay on top of your existing infrastructure. No migration, no data movement. Your database stays exactly as-is while API response times drop by an order of magnitude.
Cachee connects to your existing data sources for cache population and invalidation. On a cache miss, it fetches from the origin, caches the response, and pre-warms related keys. On a cache hit, the origin is never contacted. Whether your backend runs on PostgreSQL, MySQL, MongoDB, DynamoDB, or Redis, the database caching layer optimizes access patterns identically. For distributed architectures, edge caching extends L1 performance to 450+ global locations.
Traditional API caching strategies rely on static TTLs and manual cache invalidation. Cachee takes a fundamentally different approach — predictive intelligence that learns your traffic patterns and optimizes automatically.
Most REST API caching relies on HTTP cache headers — Cache-Control, ETag, Last-Modified. These work for simple GET requests but break down for authenticated endpoints, personalized content, and complex query parameters. Cachee's ML engine handles all of these cases automatically by learning which URL+header combinations map to which data, and pre-warming responses before clients request them.
The result: REST APIs that consistently respond in under 2ms regardless of query complexity, authentication state, or personalization requirements. No manual cache-key derivation. No TTL tuning. No stale data.
GraphQL introduces unique caching challenges. Clients construct arbitrary queries, making URL-based caching useless. Two queries requesting the same data with different field selections generate different cache keys. Nested resolvers create N+1 query patterns that cascade into database bottlenecks under load.
Cachee solves this with field-level cache normalization. Each resolver field is cached independently, so a query requesting { user { name } } and one requesting { user { name, email } } both benefit from the cached "name" field. Partial cache hits serve resolved fields from L1 while only fetching unresolved fields from the database.
In microservice architectures, a single API call can fan out to 5-10 downstream services. Each hop adds 3-15ms of network + processing latency. A request that touches auth, user, billing, and notification services accumulates 40ms+ before the client sees a response. Cachee's L1 cache sits in-process on each service, eliminating cross-service network hops for cached data. Combined with predictive pre-warming, the fan-out pattern becomes invisible to users.
APIs serving global users face an additional challenge: geographic latency. A user in Tokyo hitting an API server in Virginia adds 150ms of network round-trip before any application logic executes. Cachee's edge caching deploys L1 cache nodes at 450+ global PoPs, serving cached API responses from the nearest location. Combined with predictive pre-warming, edge nodes are populated with the right data before requests arrive — even for personalized, authenticated endpoints.
Three steps to 10-20x faster API response times. No infrastructure changes, no database migration, no configuration tuning.
Start with the free tier. No credit card required. Deploy in under 5 minutes and measure the latency reduction on your own API endpoints.