Agent graph view
Visualize every tool call, handoff, and decision branch in your agent workflows. Debug complex chains without reading logs line by line.
Eval-First LLM Observability. Not Another APM.
Auto-evaluate every trace. Detect prompt drift. Auto-curate datasets from production — and alert your team the moment quality drops. Not just observability. A feedback loop.
TRUSTED BY 500+ LEADING AI COMPANIES
Evals ran to date[ 0+ ]
HOW IT WORKS
Your users shouldn't be your QA team.
- 01
Instrument with two lines of code.
Drop in our SDK or use OpenTelemetry, LangChain, or any major framework. Full traces in minutes.
- 02
Evaluate every trace automatically.
Run eval metrics on 100% of traces — no sampling. See exactly what changed across versions.
- 03
Know the moment quality drops.
Set thresholds on any metric. Get notified the moment quality drops — before users do.
- 04
Let your next eval dataset builds itself.
Production traces auto-curate into eval datasets — filtered, tagged, ready to regress against.
Online Evaluations
Metrics auto-evaluated on every ingested trace.
Collection Library Scores
Single-TurnMulti-Turn
ThresholdInclude ReasonStrict ModeSample Rate
End Agent Execution
Task Completion
0.51
Step Efficiency
0.51
SaveReset
Generator Metrics
New Collection
Reference-Based
Configure Trace Alerts
This alert will ring when the number of trace count per hour falls below 30
Edit Try Alert Pause
Preview
See how the alert graph will look based on your selected alert settings.
CustomTodayYesterday7D30D3M12M
Trace Count
53.9040.4326.9513.4800.00
Feb 3Feb 9Feb 15Feb 21Feb 27
Dataset Auto-Curation
Production traces flow into evaluation datasets — filtered, tagged, and ready.
Filterquality > 0.8
Tagauto-classify
Datasetgolden_v3
InputOutputTags
How can I improve my credit score?Focus on payment history and utilization…
creditadvisory
What are the risks of variable-rate mortgages?Variable rates expose borrowers to market…
mortgagerisk
Explain dollar-cost averaging.DCA reduces impact of volatility by invest…
investing
Rows Curated1,247
Unique Tags18
Last Sync2m ago
PLATFORM
LLM tracing that closes the loop.
Trace annotations
Leave feedback directly on any trace or span. Flag hallucinations, tag edge cases, and build institutional knowledge right where the data lives.
Model endpoint, cost, & latency tracking
Track spend and response times across models, prompts, and endpoints. Know exactly where your budget is going and what's slowing things down.
Live alerting
Get notified the moment eval scores drop, latency spikes, or error rates climb. Slack, PagerDuty, email — wherever your team already lives.
User-level analytics
See which users are getting the worst experiences. Break down quality, latency, and errors by user so you fix what matters most first.
BUILT TO SCALE
$1/GB tracing. No retention surprises.
Other platforms advertise big storage tiers, then silently expire your traces in 14-30 days. We're $1/GB — one of the lowest in the market — and you choose how long your data lives.
TESTIMONIALS
Trusted by companies that take AI seriously.
FinomBefore Confident AI, a single improvement cycle took 10 days — I'd create a task, assign it to an engineer, wait for availability, and go back and forth. Now the same cycle takes three hours, and our product managers can run it themselves.
Igor Kolodkin,Head of AI Quality, Finom
Confident AI saves us 480+ hours of manual AI evaluation every month — and gives us the data to defend every quality decision in front of engineering, product, and leadership.
Anoop Mahajan,Director of QA, Amdocs
Confident AI gave our team one place to turn production failures into datasets, align metrics, and keep regressions out of releases without waiting on custom engineering work.
SD
Senior Director of Engineering,Fortune 500 medical device company
HumachWe run a lot of large-scale, multi-turn simulations, and Confident AI made it far easier to design scenarios and execute those tests without piecing together external tools.
Sean Austin,Chief AI Officer, Humach
Thanks to Confident AI, we were able to move to a fine-tuned model and cut our LLM costs by 80%. This opens up whole new use cases now to generate better output with more targeted LLM calls.
John Lemmon,AI Lead, Supernormal
FAQ
Have a Question?
Checkout our FAQs below, or talk to a human. They won't hallucinate.
Track latency, cost, token usage, error rates, and response quality in real time. Set up alerts for anomalies — like latency spikes or sudden drops in quality scores — so you catch issues before your users do.
Yes — no matter how deep the nesting goes. Every step in your agent's chain — LLM calls, tool invocations, retrieval steps, handoffs, function calls — is captured in a nested trace. Drill into any step to see inputs, outputs, and timing, whether it's a simple chain or a multi-agent orchestration with dozens of hops.
Almost certainly. We integrate with LangChain, CrewAI, OpenAI Agents SDK, LlamaIndex, and more — plus native SDKs for Python and TypeScript and full OpenTelemetry support. Regardless of your stack, setup is a few lines of code and you get the exact same tracing functionality across every integration.
Tracing is billed at $1 per extra GB ingested or retained — one of the lowest rates on the market. Most teams start on our free tier and scale without surprises.
Email, Slack, Discord, and Microsoft Teams today. Webhook support is coming early Q2 so you can pipe alerts into any system you use.
Your data is yours. We provide full APIs to export any trace at any time — no hoops, no restrictions. Between that and our OpenTelemetry support, you're never locked in.
Yes. Run eval metrics directly on production traces to continuously score your app's real-world performance. Use that data to build golden datasets from actual user conversations and feed them back into your testing pipeline.