Guide
PostHog for AI observability: traces, errors, and session replay in one place
Use PostHog to debug LLM features end to end: model traces, errors, and session replay in one tool. How it compares with Langfuse, Sentry, and Datadog.
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observability
7 guides on observability — architecture patterns, rollout strategy, and production integration for engineering teams.
Guide
LLM observability beyond Langfuse — the full production stack
Langfuse covers traces and evals. Here is what else production teams need: structured logging, OpenTelemetry metrics, quality signals, sampling, canaries, and when to add Braintrust, Phoenix, or your existing APM.
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Monitoring LLM costs in production: tokens, tenants, and alerts
A practical guide to LLM cost observability: structured logging, Langfuse dashboards, OpenTelemetry metrics, per-tenant budgets, and the unit economics finance actually needs.
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Eval pipelines for LLM features — what they are and how to build one
A practical guide to golden sets, property-based scoring, and CI gates — so prompt and retrieval changes do not silently break production copilots.
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Langfuse for LLM observability — where it fits in your middleware stack
How to trace model calls, debug prompts, and run evals with Langfuse — integrated into server-side LLM middleware, not bolted onto a frontend demo.
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LLM middleware: what it is, why you need it, and how to implement it
A practical guide to the server-side layer between your app and the model — auth, rate limits, routing, logging, and the patterns that keep AI features production-ready.
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What production-ready LLM integration actually means
A practical checklist for engineering leaders — beyond the demo and before you call an AI feature shipped.
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