On a cache miss for key A, Cachee predicts keys B, C, D will be needed within 50ms and fetches them from L2 simultaneously. By the time your app asks, they're in L1.
A cache miss never happens alone. Your app requests key A, misses, fetches from L2 or the database, then immediately requests key B. Another miss. Then key C. Another miss. Each miss adds 1–5ms of latency. A 5-miss cascade turns a 2ms read into a 15ms disaster — and your users feel every millisecond of it.
Cachee's ML model learns access sequences from historical patterns. When key A misses, it identifies the most likely next keys and issues parallel L2 fetches. All arrive in L1 within ~1ms. Your app's next 3–5 reads are L1 hits at 1.5µs instead of L2 misses at 1–5ms.
Cachee observes every cache access and builds a probabilistic model of access sequences. The model tracks three signals: temporal sequences (key B is accessed 3ms after key A in 92% of requests), co-occurrence frequencies (keys A, B, C appear in the same request 87% of the time), and structural patterns (keys sharing a namespace prefix are 4x more likely to be accessed together).
The model runs entirely in-process. Prediction latency is sub-microsecond — negligible compared to the milliseconds saved by avoiding L2 round-trips. It converges within minutes of deployment and continuously adapts as access patterns change.
There are no dependency declarations, no manual key mappings, and no application code changes. The system learns entirely from observed access patterns. You do not need to tell it that product pages load reviews — it learns that from traffic. You do not need to declare that auth leads to permissions — it sees the sequence thousands of times per second.
When a prediction is wrong, the pre-fetched value simply occupies L1 temporarily and is evicted normally. There is no correctness risk. A wrong prediction is a few wasted L2 reads — the cost of a single normal miss. The model continuously refines, so accuracy improves and wasted fetches decrease over time.
A typical request that touches 5 cache keys. Without speculative pre-fetch, every miss is serial. With it, one miss triggers parallel pre-fetch of the rest.
Both use ML. Both predict what your app needs. They operate at completely different scales of time.
| Dimension | Predictive Warming | Speculative Pre-Fetch |
|---|---|---|
| When It Runs | Before the session starts | Within the request, on every cache miss |
| Time Scale | Minutes to hours ahead (pre-market, trending) | Sub-50ms lookahead within a single request |
| What It Predicts | Which data sets to pre-load for a workload | Which specific keys follow a given miss |
| What It Eliminates | Cold starts — empty cache at session begin | Warm misses — cascading misses in active cache |
| Granularity | Session-level / workload-level | Per-miss, per-key, per-request |
They compose. Predictive warming ensures your cache is loaded with the right data before the workload begins. Speculative pre-fetch ensures that even the misses that slip through are resolved in parallel, not serially. Together, they produce an L1 cache with near-zero miss cascades at any scale. Learn more about predictive warming.
Speculative pre-fetch is a within-request cache optimization that predicts which keys your application will need next based on historical access patterns. When a cache miss occurs for key A, the system identifies the 3–5 most likely next keys (B, C, D) and issues parallel fetches from L2 storage simultaneously. By the time your application requests those keys, they are already loaded in L1 and served at sub-microsecond latency instead of incurring additional L2 misses.
Predictive warming operates at the session level, pre-loading data before a user or workload begins — such as pre-market trading data or trending product catalogs. Speculative pre-fetch operates within a single request, reacting to real-time cache misses and predicting the next keys needed in that same request flow. Predictive warming eliminates cold starts. Speculative pre-fetch eliminates warm misses.
Cachee achieves 85%+ prediction accuracy on typical workloads. The ML model learns from temporal access sequences, co-occurrence frequencies, and structural patterns in your key namespace. Because most applications follow predictable access patterns — view product then check inventory, authenticate then load permissions — the model converges quickly and maintains high accuracy with minimal overhead.
No. Speculative pre-fetch is entirely automatic. The ML model learns access patterns from your workload and begins predicting within minutes of deployment. There are no dependency declarations, no manual key mappings, and no application code changes. The system observes which keys are accessed together and applies predictions automatically on every cache miss.
If a predicted key is not actually needed, the pre-fetched value simply occupies L1 space temporarily and is evicted normally through standard cache eviction policies. There is no correctness risk — a wrong prediction is equivalent to a standard cache write that goes unused. The overhead is minimal: a few extra L2 reads that would not have occurred. The model continuously refines its predictions, so accuracy improves over time and wasted fetches decrease.
Speculative pre-fetch turns 5 serial misses into 1 miss and 4 instant hits. No configuration. No application code changes. Your cache just gets smarter.