The Cost-Efficient AI Stack: Ship AI Features Without the Runaway Bill
Most teams overpay for AI by routing every request to a frontier model. This is the architecture we build instead — hybrid cloud+local routing, self-hosted inference, agent orchestration, and cost-per-request observability — and the single principle that ties it together: send each unit of work to the cheapest model that can do it well.
Observability for LLM Applications on Kubernetes: Tokens, Traces, and Cost per Request
How to instrument self-hosted and hybrid LLM workloads with OpenTelemetry, Prometheus, and Langfuse — tracking time-to-first-token, tokens per second, GPU utilization, and unit economics down to the individual request.