Remote caching is simple in concept: reuse build and test results across developers and CI. At scale, it becomes an operating problem: correctness, consistency, performance under concurrency, and predictable cost as artifacts grow.
EngFlow Remote Caching is the caching layer of Build Management at AI Scale: fast iteration you can trust, stable performance under load, and cost control as volume grows.
Low hit rates because cache keys drift (flags, env, toolchain differences).
Slow or inconsistent downloads as artifacts grow.
Cache storms under high concurrency.
Opaque misses: “Why didn’t we hit?” is hard to answer.
Unbounded storage and egress costs.
Risky rollouts: small configuration changes invalidate large portions of the cache.
If you cannot trust the cache, developers stop relying on it—and you lose the compounding gains.
A build produces outputs and metadata.
Results are stored in the remote cache.
Subsequent builds reuse those results when inputs match—across any developer machine or CI runner.
Telemetry shows what was reused, what wasn’t, and why.
The system improves over time: cleaner cache keys, higher hit rates, and fewer wasted cycles.
Run the cache as a service: predictable performance, capacity that scales with load, and retention policies you set.
Storage and egress become governable, with the levers to tune cost without slowing engineers.
