Detakai implements a unified control plane built entirely on open-source standards. By linking OpenObserve for high-scale observability, OpenOps for automated FinOps workflows, and OpenRouter for intelligent model routing, we engineer resilient infrastructure your teams fully own.
Rust-native log, metric, and trace consolidation running at 140x lower resource cost than legacy SaaS.
Governed workflow engine evaluating cloud cost policies, incident routing, and GitOps runbooks.
Unified AI gateway routing model requests by latency, cost, and availability with full trace capture.
Click on the steps to see how metrics flow from systems, trigger AI-assisted recommendations, go through human reviews, and execute via GitOps.
A modern, cloud-native observability engine written in Rust. OpenObserve stores logs, metrics, and traces with standard SQL querying, running at up to 140x lower resource requirements than Datadog or Elasticsearch. It supports standard OpenTelemetry protocols and runs on any cloud or bare-metal Kubernetes cluster.
Reference docs ↗The operational orchestrator of the control plane. OpenOps ingests anomaly telemetry and cloud cost exports, then executes policy evaluations, incident routing, notifications, and pull requests. It ensures every action remains governed by the appropriate approvals and audit trails.
Reference docs ↗A unified API gateway for AI operations. OpenRouter routes prompts to the most optimal LLM (such as Gemini, Claude, or GPT) based on speed, cost, and availability. It aggregates token analytics, enforces cost caps, and streams complete execution traces back into the observability layer.
Reference docs ↗SaaS monitoring and automation tools create hidden, scaling costs that lock your operational model to a single provider. Detakai builds systems you own.
Retain complete ownership of your orchestration and telemetry pipelines. Replace, adjust, or migrate elements of the control plane without re-engineering your applications.
Fully compatible with AWS, Google Cloud, Azure, on-premises private clouds, or hybrid edge setups. Runs wherever standard Kubernetes is supported.
Move away from proprietary SaaS licensing models that bill per-metric or per-log-volume. Control infrastructure resource sizing directly on-premises or via your own cloud subscription.
Every automated optimization runs through git-based version controls and is validated against pre-change and post-change telemetry to audit exact financial outcomes.