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Confident AI Review

Confident AI is an all-in-one LLM evaluation platform built by the creators of DeepEval.

shipped Jul 3, 2026aifreemium
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Confident AI — product screenshot

Why it matters

1Evaluates each step of an AI agent's execution with over 50 research-backed metrics.
2Built upon DeepEval, an open-source framework with over 12,000 GitHub stars and 3 million monthly downloads.
3Offers HIPAA Compliant (BAA available) and SOC 2 Type II Compliant data processing.
4Includes a free tier and a Starter Plan at $19.99 per seat per month.

About Confident AI

Business Model
Subscription SaaS
Usage Pricing
$0.038 per eval per cost
Headquarters
San Francisco, USA
Team Size
50-100
Funding
Bootstrapped
Platforms
Web, API
Target Audience
AI developers, product managers, QA teams

Pricing Plans

Free Trial
Free
  • • Access to all features for a limited time
  • • No credit card required
Pro Plan
$500/mo
  • • Full access to all features
  • • 7/24 Support
  • • Advanced monitoring tools

Cost Examples

  • • Generate 1 eval: ~$0.038

Leadership

Not SpecifiedNot Specified
API DocsGitHubOpen Source

Specs

API Docs

View Documentation →

GitHub

View Repository →

API Available

Yes, public API

overview

What is Confident AI?

Confident AI is an AI quality platform developed by Jeffrey Ip and Kritin Vongthongsri that enables engineering, QA, and product teams to evaluate, observe, and improve Large Language Model (LLM) applications. It is built upon DeepEval, a widely adopted open-source LLM evaluation framework. The platform provides a robust suite of tools for evaluating LLM outputs using quantitative and qualitative metrics, benchmarking model performance across datasets, and optimizing LLM applications for reliability. It also supports monitoring production LLMs in real-time, red-teaming AI applications to align with standards like OWASP Top 10 and NIST AI RMF, and applying guardrails. Confident AI facilitates collaboration among technical and non-technical teams for pre- and post-deployment evaluations and observability. Notable customers include Booking, Accenture, Cisco, Toyota, Microsoft, AstraZeneca, AXA, and Boston Consulting Group (BCG).

features

Key Features of Confident AI

Confident AI provides a comprehensive set of features designed for the full lifecycle of LLM application development and deployment, from prototyping to production monitoring and governance.

  • LLM Evaluation with over 50 research-backed metrics for quantitative and qualitative assessment.
  • LLM Observability and real-time monitoring of production LLMs for ongoing performance, safety, latency, and cost.
  • AI Red Teaming capabilities for identifying and mitigating unsafe behavior, including alignment with OWASP Top 10 and NIST AI RMF.
  • AI Governance features for compliance, guardrail application, and responsible AI development.
  • Evaluation of AI agent execution steps, including tool calls, reasoning, retrieval, and planning.
  • Dataset management for creating, curating, and versioning evaluation datasets.
  • Tracing capabilities for debugging and performance analysis of LLM applications.
  • Continuous evaluation integration into CI/CD pipelines to prevent regressions and ensure consistent quality.
  • HIPAA Compliant (BAA available) and SOC 2 Type II Compliant data processing for enterprise-grade security and privacy.

use cases

Who Should Use Confident AI?

Confident AI is tailored for various technical and product roles involved in the development, deployment, and maintenance of Large Language Model applications.

  • ML engineers and LLM engineers for evaluating and benchmarking LLM applications for quality, relevance, and performance.
  • QA teams for integrating continuous evaluation into CI/CD pipelines to prevent regressions and ensure consistent quality.
  • AI platform teams and product managers for monitoring production LLMs for ongoing performance, safety, latency, and cost, and applying guardrails.
  • AI researchers for validating RAG pipelines, benchmarking customer support assistants, and assessing content generation.
  • Engineering teams for red-teaming AI applications and applying guardrails to catch vulnerabilities and unsafe behavior.

how to use

How to Use Confident AI

Getting started with Confident AI involves integrating its evaluation framework into your development workflow and leveraging its cloud platform for advanced features. The platform supports both open-source integration and cloud-based services.

  • 1Sign up for a Confident AI account, utilizing the free tier for initial exploration and basic evaluation needs.
  • 2Integrate the DeepEval open-source framework into your LLM application development workflow for local evaluation scripts.
  • 3Define evaluation metrics from the platform's library of over 50 research-backed options or create custom evaluators.
  • 4Connect your LLM application to Confident AI for tracing and real-time monitoring of production performance and behavior.
  • 5Utilize AI Observability Workflows to automatically convert live production traces into test cases for continuous validation.
  • 6Conduct AI red teaming exercises within the platform to identify vulnerabilities and apply guardrails to mitigate risks.

pricing

Confident AI Pricing & Plans

Confident AI offers a tiered pricing structure, including a free plan and various paid options designed to scale with organizational needs, from individual developers to large enterprises. Tracing costs are usage-based, complementing the subscription tiers.

  • Free Plan: Includes 2 seats, 1 project, 5 test runs per week, and 1 week of data retention.
  • Starter Plan: Starts at $19.99 per seat per month, including cloud datasets and unlimited traces. Tracing is priced at an additional $1 per GB-month.
  • Team Plan: Custom pricing, offering unlimited projects and designed for scalable usage-based pricing.
  • Enterprise Plan: Custom pricing for unlimited advanced features, including enterprise-grade compliance (HIPAA, SOC 2), multi-data residency, RBAC controls, and a 99.9% uptime SLA.

Pros

  • +Comprehensive evaluation with over 50 research-backed metrics for multi-step AI agent execution.
  • +Framework-agnostic design, providing flexibility and avoiding vendor lock-in for LLM applications.
  • +Native AI red teaming capabilities, including alignment with OWASP Top 10 and NIST AI RMF standards.
  • +Strong compliance posture with HIPAA Compliant (BAA available) and SOC 2 Type II certification.
  • +Built upon the widely adopted open-source DeepEval framework, indicating community trust and robustness.
  • +Cross-functional workflows enable non-technical teams (PMs, QA) to run evaluation cycles independently.

Cons

  • −Production traces and evaluation datasets are kept in separate silos, requiring manual steps to convert production failures into regression tests, unlike some competitors.
  • −Specific numerical API rate limits are not explicitly detailed in public documentation, which may impact high-volume users.
  • −While framework-agnostic, users already deeply embedded in specific ecosystems (e.g., LangChain) might experience initial integration overhead.
  • −The platform is exclusively focused on LLM quality, whereas some competitors offer broader machine learning model monitoring capabilities.
  • −The Starter Plan pricing increased from $9.99 to $19.99 per seat per month, which may impact smaller teams or startups.

Policies

Free Tier

Vendor website advertises a free tier.

Pricing Page

View Pricing→

Similar Tools

Confident AI vs Competitors

Confident AI positions itself as an evaluation-first observability platform, designed for cross-functional teams and framework-agnostic use, differentiating it from tools that primarily focus on trace capture or are tightly integrated with specific ecosystems.

1
Arize AIOn StorkCompare

An ML monitoring platform that has extended its capabilities to comprehensive LLM observability and evaluation, with a focus on operational metrics and drift detection.

While Confident AI is evaluation-first with 50+ built-in metrics, Arize AI's evaluation layer is secondary to its monitoring core, often requiring custom evaluator development for deeper analysis. Arize AI excels in production monitoring for both traditional ML and LLMs, whereas Confident AI focuses exclusively on LLM quality.

2
LangSmithOn StorkCompare

Provides comprehensive LLM development and evaluation, with deep integration for applications built using the LangChain framework.

LangSmith excels at tracing, debugging, and evaluating LangChain workflows, supporting various evaluator types including human-in-the-loop. Confident AI is framework-agnostic and offers native AI red teaming, which is not a primary focus for LangSmith.

3
Galileo AI↗

Specializes in real-time LLM evaluation and production monitoring using purpose-built Luna-2 models for consistent, cost-effective, and fast evaluation, along with runtime guardrails.

Galileo AI focuses on lightweight live-traffic safety checks and hallucination detection at high volume, with a strong eval-to-guardrail lifecycle. Confident AI offers broader metric coverage for complex multi-step agent workflows and integrates red teaming capabilities.

4
LangfuseOn StorkCompare

An open-source, self-hostable LLM engineering platform providing observability, evaluation, and prompt management, prioritizing data ownership and infrastructure control.

Langfuse offers strong tracing and prompt management but leaves evaluation depth to custom implementation, requiring teams to build and maintain their own evaluation pipelines. Confident AI provides 50+ research-backed metrics out-of-the-box and cross-functional workflows for evaluation, which Langfuse lacks.

5
BraintrustOn StorkCompare

Integrates evaluation directly into the observability workflow, enabling automated scoring, CI/CD gates, and the ability to convert production failures into permanent test cases.

Braintrust connects production traces, evaluations, and prompt iteration in a single system, with features like automated prompt optimization and 80x faster trace data queries. Confident AI keeps production traces and eval datasets in separate silos, requiring manual steps to turn production failures into regression tests.

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