Your database is fast, but not fast enough. Every query adds milliseconds. Cachee sits between your application and your database, learns query patterns, and pre-loads results. Reduce database load by 95% and turn 15ms queries into 1.5µs cache hits.
Side-by-side: every step of a database query. Watch the latency stack up without Cachee, then see it vanish with L1 cache hits.
Watch data packets flow through the pipeline. 99.05% of queries never reach your database. Only cache misses fall through to the origin store.
At a 99.05% hit rate, only 1 in ~105 requests reaches your database. The effective average latency drops from 15ms to approximately 16µs. Your database goes from being the bottleneck to being a rarely-consulted origin store. Learn more about the full architecture and predictive caching engine.
Select your database to see the exact improvement Cachee delivers. Every database is different, but the result is the same: microsecond reads.
Every database query adds latency to your application. Even optimized queries on indexed tables take 5-50ms when you factor in connection overhead, query parsing, execution planning, disk I/O, and network round-trips. At scale, these milliseconds compound into the bottleneck that limits your entire system.
The solution is not a faster database. It is a smarter layer in front of it. A database caching layer that understands your query patterns, knows which results are still valid, and serves them in microseconds instead of milliseconds. Explore how API latency optimization and cache miss reduction work together to eliminate the bottleneck.
Simulated production traffic showing how Cachee handles different query types. Green = L1 cache hit. Yellow = cache miss (falls to DB). Gray = write passthrough.
Cachee sits between your application and your database. It learns which queries are repeated, predicts what data will be needed next, and pre-loads results before your application asks. No query rewriting. No schema changes. No migration.
Cachee's ML engine monitors your query traffic and builds a real-time model of your access patterns. It identifies which queries follow user workflows, which are triggered by background jobs, and which are seasonal or event-driven.
Within 60 seconds of deployment, the model is producing accurate predictions. Within minutes, it is pre-warming the cache with data your application will need before the request arrives. Read more about predictive caching.
The hardest problem in caching is knowing when data is stale. Cachee tracks write patterns per table and per key, dynamically adjusting TTLs based on observed mutation frequency. High-churn data gets short TTLs. Stable reference data stays cached longer.
This eliminates the two failure modes of manual caching: serving stale data (TTL too long) and unnecessary cache misses (TTL too short). The ML layer finds the optimal balance automatically. See how cache miss reduction works.
When 95% of reads never reach your database, you need less database. Fewer read replicas, smaller instances, lower RDS bills. The savings compound as you scale.
Cachee operates at the application layer, caching query results regardless of the underlying database engine. SQL, NoSQL, document stores, key-value stores — if your application queries it, Cachee can cache it.
Different workloads require different caching strategies. Cachee's AI automatically selects the optimal pattern per query, but understanding the tradeoffs helps you get the most from your caching layer.
Read-Through Caching is the most common database caching pattern. When your application requests data, Cachee checks its L1 cache first. If the data exists (a cache hit), it returns immediately in 1.5µs. If the data is missing (a cache miss), Cachee fetches it from your database, stores the result in cache, and returns it to your application. Subsequent requests for the same data are served from cache. This pattern works exceptionally well for read-heavy workloads where the same data is requested repeatedly, such as user profiles, product catalogs, configuration tables, and session state. Cachee achieves a 99.05% hit rate using this pattern because the AI layer predicts which data will be requested and pre-warms the cache before the request arrives.
Write-Through Caching ensures data consistency by writing to both the cache and the database simultaneously. When your application updates a record, Cachee updates the cached copy at the same time. This eliminates the risk of stale data at the cost of slightly higher write latency. Cachee's ML engine automatically identifies which queries benefit from write-through versus cache-aside, based on the read-to-write ratio for each table. High-read, low-write tables like product catalogs use write-through. High-churn tables like analytics counters use cache-aside with short TTLs.
Cache-Aside (Lazy Loading) gives your application full control over what gets cached. The application checks the cache first, falls back to the database on a miss, and populates the cache after reading from the database. This is the most flexible pattern and works well for complex queries where the cache key depends on multiple parameters. Cachee supports cache-aside natively through the cache.wrap() API, which handles key generation, TTL management, and invalidation automatically.
Database query optimization goes beyond simple caching. Cachee analyzes query patterns across your entire application to identify redundant queries, N+1 problems, and hot-spot tables. The ML engine groups related queries into prefetch chains, so when a user loads a page, all subsequent queries are already pre-warmed in the cache. This is particularly impactful for API latency optimization where each endpoint triggers a predictable sequence of database queries. Combined with edge caching, the entire data path from user to database becomes sub-millisecond.
Install the SDK, wrap your database calls, and let the AI layer handle the rest. No cache key design, no TTL tuning, no invalidation logic.
cache.wrap(). Cachee handles cache key generation, TTL optimization, and invalidation automatically.See the full integration guide in our documentation. The free tier includes 100K operations/month, no credit card required. Check pricing for details.
Deploy a database caching layer in under 5 minutes. Free tier, no credit card required. See your database load drop by 95% on your own workload.