Confident AI

Confident AI is an AI Quality platform for evaluating AI application changes during development and monitoring systems in production. Teams can experiment with prompts, models, and parameters, run regression evaluations in CI/CD, and assess results with more than 50 metrics. For production monitoring, it traces executions and records inputs, outputs, latency, and tokens; live alerts can notify teams when quality declines. Red-team testing addresses safety vulnerabilities and adversarial attacks. Integrations include Python and TypeScript SDKs, OpenTelemetry, and more than 20 framework and gateway connections. APIs cover prompt versioning, datasets, trace ingestion, project provisioning, and governance policies. The platform is offered on web and through APIs, or can be self-hosted in a customer’s AWS, Azure, or GCP cloud using Docker; setup typically takes 1–2 weeks. The free plan provides two seats, one project, five test runs a week, and 1 GB-month of trace spans. Paid plans start at $200 per month. Free-tier test-run and tracing data are retained for 14 days.

Confident AI combines development evaluations with production tracing, alerts, and safety testing. The free plan has specific usage limits, while paid plans start at $200 per month.

It is designed for engineers, QA teams, product managers, subject-matter experts, and annotators working on AI quality, evaluation, or production monitoring.

Confident AI is an AI quality platform for teams building and operating AI applications. It is best suited to engineering and product groups that want development evaluations, safety checks, and production monitoring in one system. Its breadth is compelling, but the free plan’s five weekly test runs and paid plans starting at $200 per month set clear limits on who can use it at scale.

Overview

Confident AI brings prompt and model experimentation, automated regression checks, red teaming, and production tracing together. That makes it a practical choice for teams that want to connect pre-release evaluation with live quality monitoring instead of managing those workflows separately. Its APIs cover prompt versioning, dataset building, trace ingestion, project setup, and governance policies, giving engineering teams programmatic access across the platform.

Production traces capture inputs, outputs, latency, and tokens, while live alerts notify teams when quality degrades. The evaluation toolkit includes more than 50 metrics, with options for custom metrics, LLM-as-a-judge, safety evaluations, and human review workflows. This range serves both automated checks and human assessment, although the free tier’s small run allowance makes it better for an initial trial than sustained evaluation.

Key features

Evaluation and safety

Teams can experiment with prompts, models, and parameters, then run automated regression evaluations in CI/CD. Red-team testing targets safety vulnerabilities and adversarial attacks. Together, these features suit teams that need to assess both output quality and safety as their applications change.

Tracing and integrations

Spans record execution inputs, outputs, latency, and token usage for production monitoring. Python and TypeScript SDKs, OpenTelemetry, and more than 20 framework and gateway integrations give teams several ways to connect their stack. Project integrations can send evaluation-completion notices to Slack, Discord, Teams, or email.

Access and deployment

The platform supports API, web, and self-hosted use. Customers can deploy it in AWS, Azure, or GCP using Docker; setup typically takes one to two weeks, so self-hosting offers control at the cost of a meaningful deployment effort. API keys support organization-level and project-level authentication. Confident AI states that it encrypts data at rest, uses TLS in transit, and is SOC II and HIPAA compliant; it also offers SSO and customizable roles and permissions.

Pricing

The free plan costs $0 forever and includes two user seats, one project, five test runs per week, and 1 GB-month of trace spans. That is enough for a small evaluation or limited pilot, but the low run cap and 14-day retention for free-tier test-run and tracing data constrain ongoing use.

Starter costs $200 per month and includes unlimited user seats, five projects, and 5 GB-months of trace spans. Additional trace storage costs $1 per GB-month; input tokens cost about $0.05 per million and output tokens about $0.40 per million. It removes the free tier’s seat and run restrictions from the listed plan terms, making it the entry point for a team adopting the platform in regular workflows, while the project and trace allowances still require attention.

Team costs $2,000 per month, with unlimited seats and projects and 75 GB-months of trace spans. Additional trace storage and token usage are priced at the same stated rates as Starter. This tier fits organizations managing multiple projects and higher trace volumes; its substantial monthly price is difficult to justify for a narrow use case.

Enterprise has custom pricing and includes unlimited seats, projects, and trace spans, plus unlimited online evaluation metric runs per month. It is aimed at organizations that need those expanded limits. There is no free trial.

Platforms

Confident AI is available through API, web, and self-hosted deployments. The self-hosted option runs in a customer's AWS, Azure, or GCP cloud using Docker and typically takes one to two weeks to set up.

Who it's for

Engineers and QA teams can use the CI/CD evaluations and production traces to track changes from testing into operation. Product managers, subject-matter experts, and annotators are also among its intended users, with human review workflows supporting evaluation beyond automated metrics. Teams seeking a single platform for those roles will find the broad workflow useful; individuals or small teams with occasional test needs may find the free tier adequate, while its weekly cap makes it a poor fit for regular evaluation.

Pros and cons

• Pros: Development evaluations, CI/CD regression checks, red teaming, and production tracing share one platform, helping teams connect testing to monitoring.
• Pros: More than 20 framework and gateway integrations, SDKs for Python and TypeScript, and OpenTelemetry support offer multiple integration paths.
• Pros: API access spans prompt versioning, datasets, traces, projects, and governance policies, supporting programmatic workflows.
• Cons: The free plan allows only five test runs per week, with one project, two seats, and 14-day retention for test-run and tracing data.
• Cons: Starter begins at $200 per month, and Team costs $2,000 per month, which may be more than a small team needs for evaluation alone.
• Cons: Self-hosting typically takes one to two weeks to set up, adding deployment work for teams choosing that option.

Alternatives

Braintrust is a freemium option for teams that want a free plan with unlimited users, projects, and datasets, 1 GB of processed data, 10,000 scores, and 14-day retention.

DeepEval is a free, Apache 2.0-licensed open-source evaluation framework with a local and CI/CD test runner, a focused alternative for teams that want that framework rather than a broader hosted platform.

Galileo offers a Pro plan at $100 per month billed yearly, including 50,000 traces per month, standard RBAC, advanced analytics and insights, and dedicated Slack support; consider it when those stated plan features fit your needs.

Giskard has a free open-source library with local deployment, a basic LLM vulnerability scan using adversarial techniques, and a basic RAG evaluation report.

HELM is a free, open-source Python framework with no paid plans.

Maxim AI offers a free Developer plan with up to three seats, one workspace, up to 10,000 logs per month, three-day data retention, and email support; its free trial may also appeal to teams weighing paid usage.

Parea AI starts with a free plan for up to two team members, 3,000 logs per month with one-month retention, 10 deployed prompts, and Discord community support. Its listed Team plan costs $150 per month.

Promptfoo is a freemium option for teams looking for red-team probes and LLM evaluation features in a plan that supports local or self-hosted use.

For further options, browse LLM Evaluation Tools, AI LLM Observability Tools, AI Red Teaming Tools, AI LLM Evaluation Tools, and LLM Observability Tools.

Verdict

Confident AI is a strong fit for teams that need evaluation, safety testing, and production monitoring to work across the same AI applications. Its broad API and integration support are the main reasons to choose it; the free plan’s tight limits and the $200-per-month starting price are the reasons smaller teams or evaluation-only users should look elsewhere.