One observable gateway
Standardize model access through an OpenAI-compatible interface while capturing model, provider, token, cost, and latency context.
Detakai uses OpenRouter as a unified inference layer, then adds the telemetry, routing policy, cost allocation, and operational controls required for dependable LLM applications.
See our approach ↓We design the controls, integrations, ownership model, and measures needed to make the technology useful in production.
Standardize model access through an OpenAI-compatible interface while capturing model, provider, token, cost, and latency context.
Design provider preferences and model fallback chains around workload quality, availability, data policy, and price.
Connect generation metadata and optional traces to your observability stack for production debugging and evaluation.
Attribute token consumption to applications, teams, customers, and use cases so AI cost becomes an accountable unit metric.
Designed around your environment.
Establish request IDs, trace context, model metadata, token and cost measures, redaction, retention, and ownership.
Configure approved models, provider policies, fallback behavior, price ceilings, and data-handling requirements.
Create dashboards and alerts for spend, latency, errors, fallback rates, quality signals, and customer-level unit economics.
Applications gain controlled access to multiple model and provider options through a consistent interface.
Trace cost and performance from the product request through routing to the final model response.
Manage AI features against explicit targets for latency, availability, quality, and cost per outcome.