Cloudflare's global network is enormous, but as the company notes, it is not limitless. In a new engineering post, Cloudflare details how a small optimization effort turned into a large win: by applying math and Rust, the team reduced the RAM footprint of a Pingora-based service, reclaiming roughly 100TB of memory fleet-wide. The change came not from rewriting the service, but from rethinking how it tracks state and allocates memory at scale.
The key insight was that much of the memory was being spent on metadata and bookkeeping that could be represented statistically rather than exactly. Instead of storing precise per-connection or per-metric state, the team used probabilistic data structures and sampling, accepting a bounded, well-understood error in exchange for dramatically lower memory use. Rust's ownership model and lack of a garbage collector made those savings predictable and safe to reason about across millions of concurrent operations.
- Replaced exact per-item counters with probabilistic data structures, trading a small, bounded error rate for large memory savings.
- Moved hot-path state handling into Rust, leveraging its deterministic memory layout and lack of GC pauses.
- Applied statistical sampling to reduce the volume of tracked records while preserving useful observability.
- Tuned allocations inside the Pingora-based service, cutting resident memory across the fleet.
- Validated tradeoffs so accuracy remained within acceptable bounds for production traffic.
For developers, the takeaway is that memory optimization is often a measurement and modeling problem, not just a coding one. Asking whether data needs to be exact can unlock savings that compound across a large fleet. Rust, meanwhile, continues to prove itself as a practical tool for services where predictable memory behavior matters at scale.
Source: https://blog.cloudflare.com/saving-100-tb-of-ram-with-math/