OpenTelemetry-native tracing, evaluation, and monitoring for LLM apps
Advisor score
Documented editorial evaluation
The decision
Traceloop earns a shortlist for OpenTelemetry-oriented teams wanting portable LLM observability. Validate access semantics, redact at instrumentation, calibrate evaluators against humans, and model cost from spans rather than requests.
Best for
OpenTelemetry LLM teams; Portable tracing and evaluation.
Choose something else if
Sensitive telemetry without redaction; Fine-grained basic-plan RBAC needs
Evidence
Editorial research pending · 8.0/10
Pricing checked
See current vendor pricing · 2026-09-02
Free access is available, with limits.
Yes. Hosted Free includes 50,000 monthly spans, and OpenLLMetry can be used independently.
Community choice
Loading weekly votes…
One vote per verified account per tool, counted for seven days. Return after your vote expires to support again. Community votes influence homepage trends, never editorial ratings. Voting does not subscribe you to emails.
Traceloop combines OpenLLMetry with hosted or private observability, but span volume, trace sensitivity, access, and evaluator validity determine fit.
Traceloop earns a shortlist for OpenTelemetry-oriented teams wanting portable LLM observability. Validate access semantics, redact at instrumentation, calibrate evaluators against humans, and model cost from spans rather than requests.
Trace 500 synthetic requests across environments with PII decoys, retrieval, tools, failures, sampling, and two exporters. Measure span multiplication, redaction, isolation, evaluator-human agreement, alert precision, deletion, portability, latency, and cost.
Personal Recommendation
Traceloop earns a shortlist for OpenTelemetry-oriented teams wanting portable LLM observability. Validate access semantics, redact at instrumentation, calibrate evaluators against humans, and model cost from spans rather than requests.
Try the recommendation
Open-source OpenLLMetry
Overall Score
Editorial Review Framework
Overall Score
8.0
Ease of Use
7.6
AI Quality
8.0
Features
8.2
Speed
7.8
Integrations
8.2
Value for Money
8.0
Customer Support
7.4
Learning Curve
7.2
Recommended Because…
Open-source OpenLLMetry
Scores use a 0-10 editorial scale: 9–10 exceptional, 8–8.9 strong, 7–7.9 capable with material tradeoffs, 6–6.9 limited fit, and below 6 not recommended. Scores publish only when a documented evaluation supports them.
Reusable trial worksheet
Turn this review’s buyer test into evidence. Your entries autosave only in this browser and are never added to shared shortlist links.
Confirm the tool meets every must-have workflow and stakeholder requirement.
Review starting point: OpenTelemetry LLM teams; Portable tracing and evaluation; Private deployment requirements
Run the same representative work you would use in production; do not score a polished demo.
Review starting point: Complete three to five representative tasks with known acceptable outcomes and compare them with your current process.
Calculate the effective cost per accepted result, including usage, review, corrections, and required add-ons.
Review starting point: The current page lists Free Forever at $0 for up to 50,000 spans monthly and 24-hour retention. Higher production volume is enterprise-led. OpenLLMetry is Apache-2.0 licensed and exports to supported backends. Reviewed September 2, 2026.
Define an acceptance threshold, test known answers and edge cases, and record every correction.
Review starting point: Editorial quality signals: features 4.1/5; AI quality 4.0/5. Validate these signals in your own work.
Verify what data enters the product, who can access it, how long it is retained, and whether it trains models.
Review starting point: Use approved low-risk data first. Check roles, consent, deletion, subprocessors, model-training settings, and the contract—not only the marketing page.
Test the real handoffs, permissions, failure states, and export path your team depends on.
Review starting point: OpenTelemetry, Python, TypeScript, Datadog, Honeycomb, Grafana
Record training, governance, reliability, accessibility, ownership, and change-management risks before rollout.
Review starting point: Span counts grow quickly; Telemetry may expose content; Advanced deployment is enterprise-led
Loading saved worksheet… · private to this device or your optional account
Evaluation
Research-based
Price posture
freemium
Reviewed
2026-09-02
No authentic product screenshot is published for this review. DiscoverAI does not use generated interface images as product evidence.
Editorial freshness
Pricing and material product claims were checked September 2, 2026.
Community evidence
Structured, editor-moderated experience—not star ratings. This complements our independent review and never changes its score.
No approved community evidence yet. Be the first verified user to contribute.
Yes. Hosted Free lists 50,000 spans monthly, and OpenLLMetry is open source.
A span is one operation within a trace, such as a model, retrieval, or tool call; one request can create many.
Official docs describe hybrid and full private deployments under enterprise arrangements.
Not necessarily. OpenLLMetry exports OpenTelemetry data to multiple supported backends.
Keep Deciding
Best AI Coding Tools
AI coding assistants ranked by real developer workflow fit, not demo magic.
Best AI Research Tools
Research tools for cited answers, academic papers, market research, and knowledge synthesis.
Best AI Tools for Researchers (2026)
The best AI research tools for literature discovery, source-grounded synthesis, citation checking, controlled document analysis, and clearer academic writing.
Code & Development
AI coding assistants, code generators, and developer tools.
Material changes only
Get an occasional email when something decision-relevant changes. This is separate from the weekly newsletter.
See how similar tools stack up
Open-source tracing, evaluation, prompt management, and metrics for LLM applications
Langfuse unifies traces, costs, prompts, datasets, and evaluation with cloud and self-hosted options, but telemetry sensitivity, retention, operational load, and fast-rising plan costs demand a scoped pilot.
Trace, replay, and evaluate AI agent sessions
AgentOps is an observability platform for recording agent sessions, tool calls, model costs, errors, latency, replays, and evaluation signals.
Trace, evaluate, and experiment on AI applications
Arize Phoenix is an open-source observability and evaluation platform for tracing AI applications, scoring outputs, managing prompts, and running experiments.