Akamas is a configuration optimization platform that uses reinforcement learning to systematically explore large configuration search spaces and converge on settings that satisfy defined performance objectives at minimum resource cost. Given a goal (latency target, throughput, or cost) and a search space (JVM heap flags, Kubernetes resource limits, HPA thresholds, GC parameters), Akamas runs controlled experiments across thousands of permutations.
Two operating modes: pre-production load test mode drives automated experiments in staging; live shadow mode observes production traffic while running parallel optimization without a staging environment. The output is a concrete configuration diff validated against the target SLO — not a list of suggestions.
Enterprise-only pricing; all tiers require sales engagement. Integrates with Dynatrace, Datadog, and New Relic for baseline collection and performance validation.
