Alert on monitored traces
Inspect every trace in production, monitor quality and latency over time, and get notified immediately when regressions or incidents occur.
Improve AI quality as a team.
Not as individuals.
Monitor in production, review traces, and turn expert feedback into repeatable evals—all in one workspace for engineering, product, QA, and domain experts.
TRUSTED BY 500+ LEADING AI COMPANIES
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PRODUCTION MONITORING
Detect silent issues your users never tell you about.
Most agent failures never get reported. Confident AI evaluates every production trace, groups failures into issues, and alerts your team before they spread.
“We hit a point where every AI team was building their own eval stack. That’s fine for one product. With five, ten, fifteen AI initiatives across the portfolio, it’s never going to live up to our high standards of AI governance.”
Richard JarvisChief Technology Officer, RLDatix
CLOSE THE LOOP
Turn every failure into an eval so it doesn't happen again.
Failing traces become test cases, and every release is checked against them before it ships.
From Trace to Metric
Flag a bad response once, and every release is checked for it.
“Before 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 KolodkinHead of AI Quality, Finom
HUMAN IN THE LOOP
Where domain experts annotate alongside engineers.
Subject-matter experts and PMs review traces right next to engineers, and every note they leave becomes a test case or an eval. No CSVs, no tickets.
Structured Feedback
Every review rolls up into weekly rating trends.
“Our annotators can now work directly in Confident AI alongside engineers. That means no more CSVs, no more scattered spreadsheets—just one centralized workflow where everyone contributes.”
Dezaray HammondVP of Training & Development, Humach
THE PLATFORMThe whole AI quality loop.
The whole AI quality loop.
Finally, all in one platform.
No exporting traces to Google Forms or Excel to annotate, then stitching the results back together. Tracing, annotation, datasets, and evals all live in one place.
ENTERPRISE
Built to handle your AI’s most sensitive outputs.
Deploy in your cloud, choose where your data lives, and control who can access it.
Self-host or use Confident AI's cloud.
On-prem · AWS · Azure · GCP
Your data. Your region.
Multi-region residency · HIPAA · GDPR
Granular permissions.
RBAC · Project isolation · Data masking
99.9% uptime SLA.
Enterprise-grade availability
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AUTOMATIONS
APIs for the entire pipeline.
Every part of Confident AI is exposed as an API. Version prompts, build datasets, ingest traces, provision projects, and enroll them into governance policies — wire it into whatever your team already runs on.
from confident_ai import ConfidentAI confident_ai = ConfidentAI() # Create a dedicated project for a new agent or customerproject = confident_ai.projects.create(name="support-bot") # Route that agent's traces with its own Project API Keyprint(project.project.id)print(project.api_key.value)confident-aifinished
Project created · API key issued
INTEGRATIONSStay in your stack.
Stay in your stack.
We'll meet you there.
OTel-native, 30+ integrations, and SDKs in Python and TypeScript that feed straight into traces and evals.
CHANGELOGNew features every week.
New features every week.
Always on the frontier.
A new release lands every week. Here's what shipped in the last eight.
- Note-WorthyRevamped Annotation Analytics · Auto-Annotate Hot Keys · Recurring Queue Ingestion Tasks · Validations for Metric Alignment · Error Analysis Visualizations & Filters · EvalMode & Decision Models for Every Metric · Trajectory Custom Metrics · Span Filters for Traces, Signals & Dashboards · LiveKit Audio Recordings on Traces & Spans · Persona Metadata & Background Noise · Findings Alerts for Regressions, Anomalies & Signal Spikes · Snowflake Export Integration · Multiple Credentials per Project for Bedrock & Azure OpenAI · OAuth 2.0, mTLS & Client Secret Auth for AI Connections
- Search and RescueSearch for Traces & Spans · Annotation-Triggered Workflows · Item Status on Annotation Queues · V2 API with 200+ Endpoints · Live Threads with Configurable Idle Time · Latency Scatterplot · Export Schedules for Annotations & Test Runs · AI-Generated Dashboard Widgets
- Bye Bye DeepEval, Hello OTelConfident Trace · Native GenAI & GCP OpenTelemetry Ingestion · Confident Tracing & Confident OTel Skills · Code Scanning for GitHub & GitLab Eval Gates · Flaky Metric Detection · Multi-Reviewer Custom Form Responses · Metric Alignment in Annotate · Error Analysis in Annotate · Bring Your Own SMTP for On-Prem
- By the BookGovernance Policies from Your Documents · Multi-Repository Eval Gate · Thread Token & Cost Totals · Async Exports with Notifications · Email Verification & SSO Account Linking · Native Streaming for Experiments, Arena & Test-Run Summaries · Provider Validation on Credential Save
- Reporting for DutyAgentic Report Generation · Risk Assessments in the Eval Gate · Full Prompt Support for AI Connections · Simulation Model Reliability Benchmarks · Simulation Models on Red Teaming Test Cases · From 75 to 219 MCP Tools
- Test Runs: Brand New PageTest Runs Overview Page · Public Model Configuration Routes · Personas (Beta) · Full Audit Log Export · MCP Context on AI Connections · Hyperparameter Keys in AI Connection Payloads · Latest Models on Bedrock & Vertex
- Heat of the MomentAttack Heatmap · Refusal Decay Graph · Test Runs in Custom Dashboards · Saved Views on Test Runs · Governance Policy Inheritance · Control Resource Filters · IAM Role Access for Bedrock · Simulation Model Settings · Command-K Search · Guided Tutorials · Metric DAG Builder · MCP Server for On-Prem · OAuth for MCP · SSE Payload Types for AI Connections · Vertex AI Global & Anthropic Support · Graph Tooltips Behave
- Don't Cry WolfAlert Priorities & Priority Filtering · More MCP Tools · Code Vulnerability Scanning (Beta) · Model Providers Policy · LLM Span Endpoints · Trace Export as JSONL · Report Templates Out of Beta
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.
Confident AI is the AI quality platform built by the creators of DeepEval. It gives engineering, QA, and product teams a single place to evaluate, observe, and improve LLM applications — from prototyping through production.
DeepEval is our open-source evaluation framework for running LLM tests locally or in CI. Confident AI is the cloud platform that layers on top — adding collaboration, dataset management, tracing, real-time monitoring, and dashboards so the whole team can work together.
Yes. Every LLM call is captured as a trace with full context — inputs, outputs, tool calls, latency, token cost, and metadata. You can drill into any production request, set up alerts on quality degradation, and monitor trends over time without building custom logging.
Yes. Confident AI offers a fully self-hosted deployment option alongside the managed cloud. You can run the entire platform in your own VPC or on-prem infrastructure, keeping all data within your network. Self-hosting is available on our Enterprise plan — book a demo to get started.
Most teams are up and running in under 15 minutes. Install the SDK, add a few lines of code to log traces or run evals, and results show up in the platform immediately.
Yes. DeepEval integrates directly into your CI pipeline so you can run regression tests on every pull request. If quality drops below thresholds you define, the build fails — no bad prompts make it to production.
Confident AI is SOC 2 Type II compliant and offers both cloud and on-prem deployment. All data is encrypted in transit and at rest, and we never use your data to train models.