UpTrain

What is UpTrain?

UpTrain is an LLM evaluation and improvement platform for teams shipping AI apps that scores outputs, compares prompt or model changes, and catches regressions before production. It combines Diverse evaluations, Faster and Systematic Experimentation, Automated Regression Testing, and Root Cause Analysis, with Google sign-in and an open-source core that supports self-hosting. The platform says it has evaluated more than 1,000,000 responses and handles 100, 10k, or million rows without failures.

Last verifiedMay 17, 2026How we evaluate

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At a glance

Best for: UpTrain is best for teams shipping LLM apps who need reliable evaluation, testing, and monitoring before production.

What does UpTrain do?

UpTrain runs LLM evaluation and improvement workflows from one pipeline: it scores outputs with diverse evaluations, compares prompt or model changes through systematic experimentation, and catches regressions before they reach production. The platform also supports automated regression testing, root cause analysis, and enriched datasets so teams can turn production logs and edge cases into better test coverage. At scale, UpTrain says it has evaluated more than 1,000,000 responses, offers 20+ predefined metrics, and can handle 100, 10k, or million rows without failures. Its high-quality evals are designed to reach >90% agreement with humans, and the core framework is open-source with self-hosting available on your own cloud. The product is built for production LLMOps workflows, with Google sign-in and a demo playground for trying the experience before rollout.

Why use UpTrain?

Its open-source core and self-hosting option help teams keep evaluation workflows inside their own cloud environment.
The platform combines evaluation, experimentation, regression testing, and monitoring in one workflow instead of separate tools.
Its scoring approach is designed for high agreement with humans, which reduces the gap between automated checks and manual review.
It can process everything from 100 rows to million-row workloads, so teams can use the same system as volume grows.
Root cause analysis and enriched datasets help teams move from spotting failures to understanding and fixing them.

Who is UpTrain for?

LLM developers who want to replace manual review with repeatable evaluation workflows.
Product managers who need confidence in prompt changes and production behavior.
AI platform teams who need regression testing and monitoring across many releases.
Business leaders who want clearer visibility into LLM performance and failure patterns.

What are UpTrain's key features?

Diverse evaluations

Run evaluations with 20+ predefined metrics and >90% agreement with humans to compare outputs across many quality dimensions.

Faster and Systematic Experimentation

Test prompts and model changes against 100, 10k, or million rows, so teams can compare variants before shipping.

Automated Regression Testing

Catch quality drops by rerunning evaluation suites on new model versions, using the same metrics and datasets to spot regressions early.

Root Cause Analysis

Inspect failures with enriched datasets and evaluation traces to identify which prompts, inputs, or outputs caused a bad result.

Single-line integration

Connect UpTrain with Google in one line to start logging and evaluating model responses without building a custom pipeline.

Open-source

Use the open-source product with self-hosting support when you need control over evaluation data and deployment.

monitoring

Monitor production responses continuously and evaluate more than 1,000,000 responses to track quality over time.

What does UpTrain integrate with?

Google

What are UpTrain's use cases?

LLM regression testing

LLM developers use UpTrain to replace ad hoc checks with repeatable evaluation workflows, using Automated Regression Testing and Diverse evaluations to catch prompt or model regressions before they reach users. They can compare outputs across releases and keep quality stable as the system changes.

Prompt change confidence

Product managers use UpTrain to validate prompt updates and production behavior, using Faster and Systematic Experimentation and Root Cause Analysis to see what changed and why. That helps them ship prompt tweaks with clearer confidence in user-facing outcomes.

Production monitoring for AI teams

AI platform teams use UpTrain to monitor many releases at once, combining monitoring with Automated Regression Testing to spot failures early across deployed LLM workflows. They can trace issues back to likely causes and reduce the risk of silent quality drift.

Performance visibility for leaders

Business leaders use UpTrain to get clearer visibility into LLM performance and failure patterns, relying on monitoring and Root Cause Analysis to understand where quality breaks down. That makes it easier to prioritize fixes that protect customer experience.

How does UpTrain work?

Connect your first data source or LLM workflow with Single-line integration, then start capturing prompts, responses, and evaluation signals without rebuilding your stack.
Choose the right checks from Diverse evaluations or High quality Evals, and tailor them to the behaviors you want to measure across your application.
Run Faster and Systematic Experimentation to compare prompt or model variants, using Enriched Datasets to organize examples and surface meaningful differences.
Turn on Automated Regression Testing and monitoring so every new release is checked against prior behavior, helping you catch quality drops before users do.
Use Root Cause Analysis to inspect failures, identify patterns in bad outputs, and keep improving with Open-source workflows that fit your team's process.

Frequently asked questions

What is UpTrain?

What is UpTrain used for? Who is it for?

UpTrain is used for Diverse evaluations, Faster and Systematic Experimentation, and Automated Regression Testing. It's built for LLM developers, Product managers, and AI platform teams.