Trace production agents, discover recurring failures, and turn them into monitored signals
Advisor score
Documented editorial evaluation
The decision
Latitude earns a shortlist for AI teams that want open-source observability organized around recurring agent failures instead of isolated traces. Pilot its signal discovery against a labeled failure set, calibrate monitors with reviewers, and establish redaction and retention before broad ingestion.
Best for
Finding recurring production-agent failures; Open-source observability teams.
Choose something else if
Teams without meaningful production traffic; Unredacted sensitive telemetry
Evidence
Editorial research pending · 8.0/10
Pricing checked
From $99 · 2026-09-04
Free access is available, with limits.
Yes. Starter Cloud lists 20,000 credits monthly, and the MIT-licensed platform can be self-hosted.
Community choice
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Latitude connects traces, semantic failure discovery, human annotations, evaluations, and regression tests, but teams still need representative traffic, calibrated labels, and careful telemetry controls.
Latitude earns a shortlist for AI teams that want open-source observability organized around recurring agent failures instead of isolated traces. Pilot its signal discovery against a labeled failure set, calibrate monitors with reviewers, and establish redaction and retention before broad ingestion.
Instrument one consequential agent with redacted telemetry and seed 100 known failures across tool errors, refusals, hallucinations, jailbreaks, memory drift, and incomplete tasks. Compare discovered behaviors with human labels; measure trace completeness, cluster precision, missed cases, alert noise, regression reproducibility, query latency, storage growth, and credits per session.
Personal Recommendation
Latitude earns a shortlist for AI teams that want open-source observability organized around recurring agent failures instead of isolated traces. Pilot its signal discovery against a labeled failure set, calibrate monitors with reviewers, and establish redaction and retention before broad ingestion.
Try the recommendation
Coherent observe-understand-refine workflow
Overall Score
Editorial Review Framework
Overall Score
8.0
Ease of Use
7.8
AI Quality
8.0
Features
8.2
Speed
8.0
Integrations
8.0
Value for Money
8.0
Customer Support
7.6
Learning Curve
7.6
Recommended Because…
Coherent observe-understand-refine workflow
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: Finding recurring production-agent failures; Open-source observability teams; Turning incidents into regression datasets
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: Latitude lists Starter Cloud as free with 20,000 monthly credits, 30-day retention, and unlimited seats. Pro is $99 monthly with 100,000 credits, 90-day retention, unlimited seats, and $20 per additional 10,000 credits. Enterprise is custom and includes deployment, retention, RBAC, SAML, and support options. Self-hosted software is MIT licensed;…
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, TypeScript, Python, MCP, Slack, GitHub
Record training, governance, reliability, accessibility, ownership, and change-management risks before rollout.
Review starting point: Failure discovery depends on representative traffic; Telemetry can contain sensitive content; Automated clusters and evaluations require calibration
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Evaluation
Research-based
Price posture
From $99/month
Reviewed
2026-09-04
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 4, 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. Latitude says the platform is MIT licensed and supports self-hosting from a single host to a cluster.
Yes. Starter Cloud currently lists 20,000 credits per month, 30-day retention, and unlimited seats.
The published Pro price is $99 monthly with 100,000 credits and $20 per extra 10,000 credits.
It is a named, trackable recurring failure pattern built from traces, evaluations, automated checks, and human annotations.
Keep Deciding
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Latitude connects traces, semantic failure discovery, human annotations, evaluations, and regression tests, but teams still need representative traffic, calibrated labels, and careful telemetry controls.
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