UpTrain

What it does

UpTrain helps developers assess and improve LLM applications. Its evaluation framework provides scores for more than 20 predefined checks, including response relevancy, hallucination, retrieval quality, toxicity, and guideline adherence. Developers can also define custom evaluations using its extensible framework and 40-plus operators.

The platform analyzes failed evaluations for patterns and possible root causes, supports regression tests across prompt or code changes, and can enrich datasets with production edge cases. Its Python quickstart installs the uptrain package, supplies question/context/response data and configures a model-provider key. Dashboards can run locally, and customer-cloud deployment is supported on AWS, GCP and other clouds. A managed version is mentioned, but its hosting arrangement and entitlements require confirmation.

Developers can use the open-source framework to inspect LLM application quality and compare changes. Running it requires suitable Python infrastructure and evaluation-model access; external provider charges and operating resources are separate from the code. Confirm managed-service prices and technical requirements with UpTrain.

What it can do

• Evaluate LLM applications with 20+ predefined checks
• Define custom evaluations using an extensible framework and 40+ operators
• Analyze failure cases and identify patterns and root causes
• Run automated regression tests for prompt, configuration, and code changes
• Create diverse test sets and enrich existing datasets with production edge cases
• Visualize evaluation results in locally run dashboards

Works with

• OpenAI
• Azure
• Claude
• Mistral
• Together AI
• Anyscale
• Ollama

Before you choose

• Evaluate sensitive data under the terms of the configured model provider. External model and infrastructure costs may apply.
• Customer-cloud deployment is documented; managed-service hosting and feature entitlements require confirmation.
• Confirm maintained Python/package versions and operating requirements before rollout.

Listed under

• Coding
• MCP and agent tools
• Developers
• LLM evaluation
• Llmops
• Observability
• Open source
• RAG

Get started

The company site also links to an Evals Playground.

1. Install the uptrain Python package in your environment.
2. Prepare question, context and response examples, and configure the evaluation-model provider key.
3. Choose evaluation checks, run EvalLLM evaluations and inspect results or the local dashboard.

Request the applicable price, billing basis and deployment costs before purchase.

We couldn’t find published prices for UpTrain.

Sources for this overview

Product pages, documentation, and pricing checked 1 Nov 2026. Each source supports part of the overview; availability and prices can change.

• Introduction - UpTrain ↗docs.uptrain.ai
• Quickstart - UpTrain ↗docs.uptrain.ai
• UpTrain | Full-Stack LLMOps Platform ↗uptrain.ai

Company website

uptrain.ai: product, pricing and policy pages

Hacker News

Launch posts and discussion

Human review

Checked by a person

manual_review

Last checked 1 Nov 2026. Every field keeps its source and date; unknown means we couldn’t confirm it.