LLMOps Eval Platform
Production-grade LLM/RAG evaluation platform with UI-driven configuration, multi-provider support, and comprehensive metrics.
The Problem
After building an LLM application, teams struggle with:
- Weeks spent building custom evaluation frameworks from scratch
- Complexity requiring expertise in NLP metrics, embeddings, and LLM behavior
- Inconsistent testing across different projects and teams
- Skipped evaluations due to implementation difficulty
- Unreliable deployments without proper quality gates
The Solution
LLMOps Eval is a no-code evaluation platform that lets you:
Define Projects → Upload Datasets → Configure Endpoints → Select Metrics → Run Evaluations → View Results
All through a UI — no custom code needed.
Key Features
Core Capabilities
- Multi-Tenant Architecture — Organizations, projects, and team-based access control
- Dataset Management — Create, import (CSV/JSON), and version test datasets
- LLM Provider Support — OpenAI, Anthropic, Azure OpenAI, AWS Bedrock, Google Vertex AI, Custom APIs
- 20+ Evaluation Metrics — Traditional NLP, RAG-specific, and LLM-as-Judge
- Parallel Execution — Fast evaluation with automatic retry handling
- CI/CD Integration — API keys, webhooks, GitHub/GitLab integration
- Cost & Token Tracking — Monitor usage and costs across evaluations
- Regression Detection — Compare runs and detect quality degradation
Supported Metrics
| Category | Metrics |
|---|---|
| Traditional NLP | BLEU, ROUGE, Exact Match, Levenshtein, BERTScore |
| RAG-Specific | Faithfulness, Answer Relevancy, Context Precision, Context Recall |
| LLM-as-Judge | Relevance, Coherence, Fluency, Toxicity, Custom criteria |
| Performance | Latency, Token Count, Cost |
Technology Stack
| Component | Technology |
|---|---|
| Backend API | Spring Boot 3.x (Java 21) |
| Evaluation Engine | FastAPI (Python 3.11) |
| Frontend | Next.js 14 (React 18) |
| Database | PostgreSQL 16 |
| Cache | Redis 7 |
Next Steps
- Quick Start — Get up and running in minutes
- Architecture — Understand how it all fits together
- Metrics Reference — Explore all available metrics