Alert on monitored traces
Inspect every trace in production, monitor quality and latency over time, and get notified immediately when regressions or incidents occur.
Upcoming webinar: close the loop from traces to evalsWhere AI Quality is Standardized.
Where AI Quality is Standardized.
Not Improvised.
Standardize how different teams turn live traces into test cases, validate with evals, and catch vulnerabilities before they ship.
TRUSTED BY 500+ LEADING AI COMPANIES
Evals ran to date[ 0+ ]
THE ROI
One eval standard. Enforced across every team.
Align every team to the same evals and quality bar — no matter who ships the release.
“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
HOW TEAMS WORK
Where product, QA, and engineering align.
One platform that gives engineers, product owners, and QA teams a shared source of truth.
LLM Tracing
Trace UUID 6d63ad3c-8083-fa75-93dd-82e36b52996a
“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
WHO WE SERVE
For AI that has to be safe. Not just useful.
Purpose built for industries where a perfectly functional AI is not good enough.
“Confident AI increased our speed to market by 300%. For us, compliance and trust aren’t optional—they’re required. Confident AI helps us deliver both.”
Sean AustinChief AI Officer, Humach
THE PLATFORMTrace, annotate, evaluate.
Trace, annotate, evaluate.
Complete the AI quality loop.
ENTERPRISE
Built for the world’s most regulated industries.
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
us_west_1us_east_1eu_central_1uk_south_1jp_east_1ca_central_1au_southeast_1
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 confidentai 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.
SDKs in Python and TypeScript, OpenTelemetry, and 20+ framework and gateway integrations feed straight into traces and evals.
COMMUNITY
The future of quality AI depends on you.
Join the largest and fastest growing community on AI evaluation.
THE FRONTIER
New Features Every Week.
A new release lands every week. Here's what shipped in the last eight.
- 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
- Cost and EffectProject Cost Analysis · GitLab for PR Gate · Default Report Templates · Golden CRUD Endpoints · API Key Rotation, Grace Periods & Expiry · Audit Logs to Datadog · Admin Actions in Audit Logs · Governance on Metric Data & Annotations · Classification Charts Move to Traces · Fetch Trace Returns Everything
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.