LLM tracing and instrumentation
Instrument LLM apps with the `@observe` decorator or wrapper, third-party integrations, or OpenTelemetry, then inspect traces, spans, and threads across Python, TypeScript, and other supported languages.
Confident AI
RevendiquerConfident AI is an AI quality platform to trace, test, monitor, and improve AI systems with evaluation, datasets, prompt workflows, and production observability.
Confident AIWhat Confident AI does
Confident AI is an AI quality platform for engineers, QA teams, and product leaders who need a shared way to test, monitor, and improve AI systems. The product combines LLM tracing, evaluation, dataset management, prompt workflows, and production monitoring in one place.
Its tracing layer captures traces, spans, and threads from AI applications so teams can evaluate behavior in development, CI/CD, and live traffic. The platform is positioned to help teams keep quality, latency, and regressions visible across the lifecycle rather than relying on ad hoc checks or separate tooling.
Core capabilities
Evaluation across the workflow
Run online and retrospective evaluations on live traces, spans, threads, prompts, and datasets to measure quality as the application changes.
Dataset management and curation
Create, edit, and version datasets in the cloud, auto-curate them from traces, schedule recurring dataset runs, and generate synthetic goldens for targeted testing.
Prompt management workflows
Version prompts, label them by environment, require pre-commit evals, and use Git-style branching, pull requests, and approvals for prompt changes.
Production monitoring and alerting
Monitor production quality, latency, and cost, then route alerts and trace data with filters, tags, metadata, user IDs, and project separation.
Collaboration and review tools
Support review and team workflows with human annotation queues, custom criteria, custom forms, downstream observability workflows, and project/integration hooks.
Common ways teams use it
Debug production AI behavior
Engineers can trace LLM calls, tool calls, latency, token usage, and agent behavior to understand what happened in a live request and where a failure originated.
Build regression test coverage
QA and product teams can turn traces into datasets, curate goldens, and run recurring evaluations so new releases are checked against the same quality bar.
Stress-test conversational workflows
Teams working on chat or agent products can simulate multi-turn conversations to test behavior before release and inspect where a conversation drifts or fails.
Manage prompt changes safely
Product and engineering teams can version prompts, require evaluation before release, and use branching or approvals to manage prompt changes across environments.
Monitor live traffic and close the loop
Ops or support teams can monitor quality, latency, and alerts in production, then route failures into annotations and downstream review workflows.
Pros and Cons
Pros
- Covers tracing, evaluation, dataset management, prompt workflows, and production monitoring in one platform.
- Supports multiple instrumentation paths, including a decorator/wrapper, third-party integrations, and OpenTelemetry.
- Offers both development-time testing and live-traffic evaluation, which helps teams connect pre-release checks with production feedback.
- Includes collaboration features such as annotation queues, custom review criteria, and Git-style prompt workflows.
- Has a free tier plus paid plans and custom enterprise options, making it usable from individual exploration through larger deployments.
Cons
- The public sources are stronger on platform capabilities than on workflow depth for every module, so some implementation details are not fully documented here.
- Pricing and plan names are clear, but some enterprise features are described at a high level rather than with published limits or technical specifics.
FAQ
Can I switch plans later?
Yes. The pricing page states that you can switch plans at any time. Upgrades are prorated for the rest of the billing cycle, and downgrades take effect on the next billing date.
What happens if I exceed my plan limits?
The pricing page says you will receive alerts as you approach plan limits. It also notes that overage charges are documented by plan tier, and you can upgrade if you need more room.
Is there a trial or free option?
The Free tier is available forever, and paid plans can be started from the free tier without a credit card. The pricing page describes Starter and Team/Enterprise paths for users who want more capability or custom terms.
How do teams connect their applications?
The docs show several ways to instrument an app for tracing, including the `@observe` decorator or wrapper, third-party integrations such as OpenAI, LangChain, Pydantic AI, and Vercel AI SDK, and OpenTelemetry for language-agnostic setups.
Who is Confident AI for?
Confident AI is positioned for engineers, QA teams, and product leaders who need tracing, evaluation, dataset management, and monitoring in one platform. The pricing and home pages also show plans for individual users, teams, and enterprise deployments.
Quick Facts
- Category
- AI quality platform
- Primary users
- Engineers, QA teams, and product leaders
- Core workflows
- LLM tracing, evals, dataset management, prompt versioning, monitoring
- Instrumentation options
- Decorator/wrapper, third-party integrations, OpenTelemetry
- Pricing structure
- Free tier, paid starter plan, team and enterprise options
- Website
- confident-ai.com