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.
Running a Fleet of Firecracker microVMs for eve.dev Agents, Part 7: Fleet Operations
The final part of the series. We make the fleet operable — event-driven host autoscaling with lifecycle-hook draining, a reconciliation loop that reschedules agents off a dead host, per-agent logs and metrics rolled up across the fleet, and the FinOps view that turns packing density into cost per agent.
A 101 Guide: Running an Eve Agent in a Firecracker microVM on AWS
A beginner's walkthrough of provisioning bare-metal AWS infrastructure with CDK TypeScript, then booting a Firecracker microVM to self-host a Vercel eve agent.
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.