Technical writing on LLM routing, cost optimization, evaluation pipelines, and production AI infrastructure — from the team at Inference Labs.
Tracing LLM calls in production requires more than logging prompts and responses. This guide covers the data model, ingestion path, privacy controls, and operational tradeoffs behind an llm trace collection production system.
Read more →Cloud AI API costs usually grow faster than traffic because prompt size, retries, and model defaults drift over time. This guide shows how senior engineers can systematically reduce spend with routing, caching, observability, and policy controls.
Read more →Model routing should be treated as policy engineering, not ad hoc prompt logic. This guide shows how to design, implement, and evaluate selection policies that balance quality, latency, cost, and operational risk.
Read more →LLM observability in 2026 is no longer just request logging. Teams need end-to-end traces, evaluation-linked telemetry, and actionable SLOs that connect model behavior to product and infrastructure outcomes.
Read more →An automated AI quality assurance pipeline turns model evaluation from an occasional project into a repeatable engineering system. This guide covers architecture, metrics, gating, and operational patterns for production AI teams.
Read more →Retries and model fallback are essential for production LLM systems, but naive implementations often amplify cost, latency, and incident blast radius. This guide covers failure classification, retry budgets, fallback graphs, and observability patterns that hold up under real traffic.
Read more →Choosing between GPT, Claude, and Gemini is less about brand loyalty and more about workload shape, failure modes, and operational constraints. This guide maps common production use cases to model behavior so engineering teams can make routing decisions with fewer surprises.
Read more →Inference spend grows fast once AI features move from prototype to product. This guide covers practical AI inference cost optimization strategies senior engineers can implement without sacrificing reliability or quality.
Read more →A reliable llm evaluation pipeline for production needs more than benchmark scores. This guide covers dataset design, automated grading, online monitoring, and release gates for real systems.
Read more →Vendor-neutral AI routing is an architectural pattern for sending AI workloads to the best available model or provider without hard-coding a single vendor into your application. For backend and ML teams, it improves resilience, cost control, and migration flexibility, but only if routing policies, telemetry, and interfaces are designed carefully.
Read more →A multi model llm routing architecture lets teams balance cost, latency, quality, and reliability instead of hard-coding a single provider. This guide covers control-plane design, routing policies, observability, and rollout patterns for production systems.
Read more →Reducing LLM spend in production requires better routing, stricter token control, and measurement at the request level. This guide covers concrete engineering patterns that lower cost without degrading reliability or user experience.
Read more →