Akamas runs reinforcement-learning experiments to find configurations that meet SLOs at minimum cost.
Akamas defines an optimization study, a goal (latency, throughput, cost), and a search space across JVM flags, Kubernetes resource limits, HPA thresholds, or GC parameters. It then drives load tests across thousands of permutations in pre-production, or shadows live traffic in production, and converges on a configuration that no human would have tried. The output is a concrete config diff, not a dashboard suggestion.
Who it's for. Performance engineering and SRE teams running latency-sensitive Java or Kubernetes workloads at companies with 50 or more engineers. A typical scenario: a payments service is missing its p99 SLA because of GC pauses, and Akamas tries 40,000 heap and CPU permutations in staging until it finds settings that cut p99 from 800ms to 200ms while reducing CPU requests by 30 percent.
Tradeoffs. Enterprise-only with no published pricing. The optimizer needs a representative pre-production environment to produce trustworthy results, which most teams do not have. The mental model (studies, goals, constraints) takes weeks to internalize and is not the kind of tool you turn on and walk away from.
Compare: StormForge, CAST AI, Densify, Zesty
