LLM Observability Stack Selection

The 2026 observability market splits into two categories. Development platforms (LangSmith, Langfuse, Comet Opik) bundle monitoring with evals, prompt management, session replays. Gateway/instrumentation tools (Helicone, SigNoz, OpenLLMetry, Phoenix) focus on telemetry. Langfuse is MIT-licensed core with strong OSS balance (50K events/month free cloud). Phoenix is OpenTelemetry-native under Elastic License 2.0 — excellent for drift/RAG visualization, not a persistent production backend. Arize AX uses zero-copy Iceberg/Parquet integration claiming 100x cheaper than monolithic observability. LangSmith leads for LangChain/LangGraph, $39/user/mo, self-host in Enterprise only. Helicone is proxy-based with 15-30 min setup, 100K req/mo free, but less depth on agent traces. Common production pattern: Gateway (Helicone/Portkey) + eval platform (Phoenix/TruLens) glued by OpenTelemetry.

In this lesson

01Learning Objectives
02The Problem
03The Concept
04Use it
05Exercises
06Key Terms
07Further Reading

About 1200 words of reading, with code, 6 quiz questions and the tutor.

The full lesson opens with an access code

You can see what this lesson covers and the milestone it sets. The full text, code, quizzes, the tutor and the AI reviews come with the K850 practice, along with weekly build sessions and code review.

Book the K850 practice Read an open lesson

Milestone forA production labour-compliance assistant

See every request

Trace each request through role filter, retrieval, rerank, synthesis and guards with timings, tokens, cost and the faithfulness score. Build a weekly view of retrieval quality and alert on a 5% drop.

Done when

Spans for role filter, retrieval, rerank, synthesis and guards on every request
A dashboard of ZMW cost, p95 latency and faithfulness
An alert that fires on a simulated 5% retrieval-quality drop

Stretch: Sample 5% of traffic into a human review queue

Paste your evidence: the README section, results tables, numbers, test output

Reviews open with an access code.

Interview practice

Questions on this topic that AI engineering interviews ask, with the companies reported to ask them. Answer the way you would out loud; Jev scores it and DeepSeek tells you what to add.

Your answer

Scoring opens with an access code. You can still read the questions and prepare.

Adapted from open-source work: AI Engineering from Scratch by Rohit Ghumare (MIT, lesson text, code and quizzes); Awesome LLM Apps by Shubham Saboo (Apache-2.0, starter code); AI Engineering Interview Questions by Outcome School (Apache-2.0, interview questions). Capstones, milestones, data packs and Zambian context by Zambrite.