Okahu provides an AI observability platform that automatically discovers and monitors all components of generative AI workloads—such as LLM APIs, LangChain pipelines, Nvidia Triton servers, and cloud services—offering a unified, real‑time view of execution flow, latency, and resource health. The platform correlates telemetry to pinpoint performance or reliability issues, predicts the impact of code or configuration changes, and delivers actionable recommendations for debugging, cost optimization, and reliability improvements.
Funding
$20K raised to dateRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
POPAZVFounders
Product
Problem
Generative AI applications are built from many heterogeneous components—LLMs, prompt pipelines, inference servers, and cloud infrastructure—making it difficult for engineers to monitor performance, reliability, and cost in production. Without unified observability, issues such as latency spikes, model drift, or mis‑configured services remain hidden until they impact end users.
Solution
Okahu delivers an AI observability platform that automatically discovers and tracks all components of a generative AI workload, including LLM APIs, LangChain pipelines, Nvidia Triton inference servers, and cloud resources. The system correlates telemetry across these pieces to provide a single, real‑time view of execution flow, model inference, and infrastructure health. Built‑in analytics surface reliability and performance issues, predict the impact of code or configuration changes, and suggest remediation actions. Engineers can explore the workload hierarchy, organize it according to team structures or business goals, and receive actionable recommendations without writing custom integrations or parsing logs. The platform is offered as a cloud‑hosted service with a free tier for basic monitoring and paid plans for advanced testing, evaluation, and FinOps/DevOps features.
Target Audience
Okahu is aimed at AI engineers, ML‑Ops teams, and developers building production generative AI agents who need proactive monitoring, debugging, and cost‑control capabilities.
Features
- Automatic discovery of AI components (LLMs, LangChain workflows, Triton servers, cloud services) without manual instrumentation
- Unified dashboard that visualizes end‑to‑end execution, model inference latency, and resource utilization
- Correlation engine that links observations across components to pinpoint root causes of performance or reliability problems
- Predictive impact analysis that evaluates how changes to model versions, code, or infrastructure will affect key metrics
- Actionable recommendations for debugging, cost optimization, and reliability improvements
- Support for major generative AI stacks, including OpenAI, LangChain, Nvidia Triton, and Microsoft Azure
- Integration with existing CI/CD pipelines and monitoring tools via APIs and Python SDK