UpTrain
UpTrain is a full-stack LLMOps platform designed for managing large language model (LLM) applications. It provides enterprise-grade tooling to facilitate evalua...
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What is UpTrain?
UpTrain is a full-stack LLMOps platform designed for evaluating, experimenting with, monitoring, and testing large language model (LLM) applications. It provides enterprise-grade tooling that helps teams eliminate guesswork and scale AI confidently by offering diverse evaluations, systematic experimentation, automated regression testing, root cause analysis, and enriched dataset creation.
Key features include 20+ predefined evaluation metrics (covering task understanding, context awareness, language features, and safeguards like jailbreak detection), custom metric definitions within an extendable framework, quantitative scoring with >90% human agreement, automated testing for prompt/config/code changes, prompt versioning with rollback capabilities, root cause analysis that isolates error cases and finds patterns, and a self-hosted dashboard that requires no code. The platform integrates via a single API call in under 5 minutes and can be hosted on your own cloud (AWS, GCP) for data governance compliance.
UpTrain is built for developers, product managers, and business leaders working on production-grade LLM applications. Developers can build, debug, and improve LLM apps without tedious manual reviewing while collaborating with product teams. Managers can trust prompt changes, enjoy systematic experimentation, and provide feedback by highlighting cases. The core evaluation framework is open-source (backed by YCombinator), while the managed version offers enterprise features.
UpTrain pricing
Pricing model: Free
Free (Open-Source): $0, includes the full open-source evaluation framework and a limited free tier on the managed cloud platform for testing evaluations. Team: Price not publicly listed, includes higher evaluation volumes, dashboards, and team collaboration features. Enterprise: Price not publicly listed, includes custom usage limits, dedicated support, and self-hosting options. Exact pricing for paid plans is not available on the website. Teams interested in Team or Enterprise tiers should contact UpTrain directly or book a demo at uptrain.ai. Pricing model: Book Demo / Request Quote.
UpTrain pros
- 20+ predefined evaluation metrics covering diverse needs
- Custom metric definitions within extendable framework
- >90% agreement with human scores for high-quality evals
- Single API call integration in under 5 minutes
- Self-hosted on your cloud (AWS, GCP) for data governance
- Automated regression testing for prompt/config/code changes
- Prompt versioning with hassle-free rollback capabilities
- Root cause analysis isolating error cases and patterns
- Enriched datasets creation capturing edge cases from production
- No-code web-based dashboard for evaluations
- Handles 100 to million rows without failures
- Cost-efficient scoring at a fraction of alternative costs
- Open-source core evaluation framework
- Backed by YCombinotor (YC W23)
- Guardrails including jailbreak and prompt injection detection
- 20+ evals with 40+ operators for custom ones
- Collaboration features for team feedback
UpTrain cons
- Team and Enterprise pricing not publicly listed
- Requires OpenAI API key for running evaluations (costs apply)
- Limited free tier on managed cloud only for testing
- Self-hosting requires technical setup effort
- Dashboard runs on localhost:3000 (local machine only)
- No annual billing option publicly mentioned
- Support details for paid plans require booking demo
- Primarily focused on LLM/RAG applications, not general ML
Frequently asked questions about UpTrain
How does UpTrain evaluations work?
UpTrain evaluates LLM applications by taking your data (question, context, response) and running predefined or custom evaluation checks. You create an EvalLLM instance with your OpenAI API key, then call the evaluate method with your data and selected checks (like CONTEXT_RELEVANCE, FACTUAL_ACCURACY, RESPONSE_COMPLETENESS). The platform generates quantitative scores with >90% human agreement, providing objective metrics to compare prompts and configurations.
Do I need to pay for OpenAI costs for running UpTrain evaluations?
Yes, UpTrain evaluations require an OpenAI API key and you pay for OpenAI usage costs. The platform uses OpenAI's models to generate evaluation scores, so OpenAI API costs apply separately from any UpTrain pricing. The core evaluation framework is open-source, but managed cloud evaluations consume OpenAI API calls.
How long does it take to integrate UpTrain?
UpTrain can be integrated in less than 5 minutes with a single API call. You install it via pip (pip install uptrain), import the EvalLLM and Evals libraries, and you're ready to run evaluations. The single-line integration makes it fast to add to existing production pipelines.
Can I try UpTrain before purchasing?
Yes, UpTrain offers a limited free tier on the managed cloud platform for testing evaluations. The core evaluation framework is completely free and open-source ($0). You can start with the open-source version to test evaluations before contacting UpTrain for Team or Enterprise tiers.
What is the difference between open-source and managed version?
The open-source version includes the full evaluation framework with 20+ pre-configured evals and 40+ operators for custom ones, available at $0. The managed cloud version adds dashboards, team collaboration features, higher evaluation volumes (Team tier), custom usage limits, dedicated support, and self-hosting options (Enterprise tier). Open-source requires self-hosting; managed version offers cloud hosting.
What is UpTrain?
UpTrain is an open-source full-stack LLMOps platform to evaluate and improve LLM applications. It provides scores for 20+ pre-configured evals covering language, code, and embedding use-cases, performs root cause analysis on failure cases, gives insights on resolving them, and enables continuous iteration and improvement. It's backed by YCombinator and designed for production needs from evaluation to experimentation to improvement.
Who is the ideal user for UpTrain?
UpTrain is built for developers, product managers, and business leaders building production-grade LLM applications. Developers use it to build, debug, and improve LLM apps without manual reviewing while collaborating with product teams. Managers use it to trust prompt changes, enjoy systematic experimentation, and provide feedback. ML practitioners and data scientists observing models in production also benefit from its observability and refinement capabilities.
How difficult is it to setup UpTrain?
UpTrain setup is straightforward and takes minutes. You install via pip (pip install uptrain), clone the GitHub repository for the dashboard, run bash run_uptrain.sh, and access http://localhost:3000. The single API call integration for evaluations and no-code dashboard make it accessible. Self-hosting on your cloud (AWS, GCP) requires basic technical setup but ensures data governance compliance.
Does UpTrain collect my data?
UpTrain is data-secure and compliant with data governance needs. You can host it on your own cloud environment (AWS, GCP, or others), ensuring your data stays within your infrastructure. The platform integrates seamlessly with existing production pipelines without requiring data to leave your environment when self-hosted.
What kind of support and community resources are available?
UpTrain offers community support through GitHub (where you can submit new issues or request features) and a Slack channel for feature requests. For Team and Enterprise tiers, dedicated support is included. The open-source nature means active community contributions, and the platform is backed by YCombinator ensuring ongoing development and support infrastructure.