AI Tool
Confident AI pricing, features, company info, and alternatives
A factual product page for Confident AI as an AI quality platform for evaluation and tracing.
Last updated April 2026 · Pricing and features verified against official documentation
Pricing
Current public pricing tiers on file for Confident AI, last verified Apr 26, 2026.
Free
$0 / month
Includes 2 seats, 1 project, 5 test runs per week, 1 GB-month of trace spans, and 1 week of retention.
Starter
From $19.99 / user/month
Includes 1 seat, 1 project, 1 GB-month of trace spans, 5k online eval metric runs per month, and unlimited retention adjustable to usage limits.
Premium
From $49.99 / user/month
Adds chat simulations, no-code evaluation workflows, real-time performance alerting, and full API access.
Team
Custom
Adds custom roles and permissions, SSO, HIPAA, SOC 2, and a dedicated support channel.
Enterprise
Custom
Adds dedicated on-prem deployment, infosec review, on-demand penetration testing, and 24x7 support.
What You Can Do With It
The main capabilities that shape how people use Confident AI today.
Combines offline evals, online evals, prompt versioning, trace ingestion, and test reports in one platform.
Provides a REST Evals API for datasets, prompts, traces, annotations, and remote evaluation runs.
Supports self-hosted deployments where data stays inside the customer's cloud account and private network.
Adds no-code workflows, chat simulations, alerting, and role controls on paid plans.
Best For
Who Confident AI is most clearly built for.
Teams shipping LLM applications that need evaluation and observability in the same workflow.
Engineering and QA groups that want automated evals in CI/CD plus production trace review.
Organizations that need self-hosted or private-network deployment for sensitive AI workloads.
Platforms
Where you can use Confident AI today.
Web
REST API
Self-hosted
Integrations
Notable connected tools and ecosystem hooks for Confident AI.
OpenTelemetry
GitHub Actions
Slack
OpenAI
Azure OpenAI
Anthropic
Privacy Notes
Publicly stated data-handling notes that matter when evaluating Confident AI.
The self-hosted security docs say there is no public internet ingress by default and users access the product through the customer's VPN, peering, or private connectivity setup.
The same docs say all application data is stored in the customer's cloud account and that Confident AI does not maintain its own user database for self-hosted deployments.
API keys are project-scoped, revocable, and audited according to the self-hosted security docs.
Compliance
Public compliance or enterprise-governance signals we found for Confident AI.
SOC 2 Type II
HIPAA
GDPR
Access
How to integrate or build around Confident AI.
Public API
Yes
Docs
Available
Alternatives
Other tools worth considering alongside Confident AI.
Framework-agnostic platform for observability, evaluation, and deployment of AI agents and LLM apps.
AI observability and evaluation platform for tracing, scoring, and improving production AI applications.
AI agent tracing, evaluation, and error analysis platform.
Open-source AI observability and evaluation platform for tracing, prompt iteration, and experiments.
Product Snapshot
Confident AI is an AI quality platform for evaluating and tracing LLM applications. Its public product surface covers development-time evals, prompt iteration, and production monitoring, with a separate self-hosted path for teams that need private-network deployment.
What You Can Do With It
- Run automated evals on prompts, datasets, traces, and test cases through the web app or Evals API.
- Track production traces, spans, tokens, and quality signals across live AI workflows.
- Version prompts, annotate results, and compare changes before shipping to production.
- Deploy the platform inside your own cloud account when you need private networking, SSO, and customer-controlled data storage.
Why It Stands Out
It combines prompt evaluation, production tracing, and a documented self-hosted security model in one product instead of splitting those functions across separate tools.
Tradeoffs To Know
- Self-serve pricing is structured around seats, projects, trace-span storage, and online evaluation volume, so cost planning is more involved than a flat monthly plan.
- The strongest privacy and network-isolation guarantees in the public docs are described for self-hosted deployments rather than the default cloud product.
- Teams that only need one narrow function, such as prompt versioning or trace capture, may not need the full platform surface.