Performance

AIS Performance: Building a Fast, Durable State Layer

The AIS foundation combines fast indexed access with durable records, recovery and integrity controls rather than treating performance and persistence as separate problems.

Editorial disclosure: AI-assisted drafting or research support was reviewed, edited and approved by the named human author before publication.

Adaptive Intelligence Storage began with a practical performance problem: applications repeatedly retrieve the same information, rebuild the same state and wait on storage layers that were not designed around persistent machine intelligence.

The project evolved into a native C++ storage platform built around indexed access, durable append-only records, crash recovery, integrity checking and adaptive caching. The aim is to keep useful state close to the applications and AI systems that need it while preserving enough durable history to reconstruct what happened.

Measured development results

Controlled AIS service tests have demonstrated durable 4 KiB appends around 100 microseconds, exact queue-block reads with sub-millisecond p99 latency, and state materialization around 9 milliseconds. Earlier integrity work also demonstrated multi-gigabyte-per-second CRC processing under controlled benchmarks.

These figures describe the development environment in which they were measured; they are not presented as universal performance guarantees. Hardware, workload, record size, access patterns and configuration can materially change real-world results.

Durability without rebuilding everything

The append-oriented design allows changes to be recorded quickly while maintaining a recoverable history. On restart, applications can restore durable state instead of reconstructing every operational detail from unrelated sources.

Adaptive caching instead of one fixed policy

AIS is also being developed around adaptive caching behavior. Smaller or lower-traffic workloads can keep more information immediately available, while larger workloads can move toward selective caching, page-demand behavior and batching as pressure increases.

The important requirement is that those changes remain measurable, deterministic, observable and reversible. Performance improvements are useful only if they preserve correctness and make the system easier—not harder—to reason about.