Comet vs EvalCore

Comet is an AI developer platform covering two connected needs: traditional ML experiment tracking and management, and LLM and agent observability through its Opik product. On the MLOps side, it lets data scientists track and compare training runs, version models and datasets, and monitor production models, with support for frameworks like PyTorch, TensorFlow, Hugging Face, and scikit-learn. Opik, Comet's LLM observability and evaluation platform, adds tracing across 60+ integrations, automatic error detection with an AI assistant called Ollie that recommends fixes, test suites with LLM-as-a-judge evaluation, and production monitoring dashboards, including cost tracking for coding agents like Claude Code. Opik's core feature set is available as a free, self-hostable open-source download in addition to Comet's hosted cloud plans.

EvalCore is an open-source developer tool that lets AI engineering teams know when a change to a prompt, model, or dependency makes their LLM-powered app worse. Instead of forcing you into a proprietary SDK or test harness, it wraps around anything that speaks HTTP or shell. You describe an evaluation as a YAML file plus a JSONL dataset, point it at your target, then stack scorers on top to define what "good" means. The core innovation is the cassette: on the first live run, EvalCore calls the real model and records every request and response into a local SQLite cache keyed by a hash of the canonical request. That cassette can be committed to your repository. In CI, EvalCore replays the recording entirely offline with zero network calls, zero API keys, and zero cost, producing deterministic verdicts. The `--baseline main` flag runs a comparison against main and exits nonzero only on regressions, making it a natural pre-merge gate. The tool is designed to be lightweight and language-agnostic. It ships as a single dependency-free binary available via `cargo install` or prebuilt binaries for macOS, Linux, and CI runners. It works with OpenAI-compatible APIs, vLLM, Ollama, REST endpoints, shell commands, and OTel/OpenInference traces. With Apache-2.0 licensing, no server, no signup, and no telemetry, EvalCore positions itself as a simple, auditable way to ship AI changes confidently.

• Covers both classic ML experiment tracking and modern LLM and agent observability under one company.
• Opik's open-source option gives teams a genuinely free, self-hosted path with the full feature set.
• Broad framework support (PyTorch, TensorFlow, Hugging Face, scikit-learn) for the MLOps side.
• Cost intelligence for coding agents like Claude Code is a distinctive feature for teams managing AI spend.

• Zero-cost, keyless CI replays after the initial live recording
• Truly language-agnostic — anything speaking HTTP or shell can be a target
• No SDK, test harness, server, signup, or telemetry required
• Deterministic byte-for-byte replay eliminates flaky LLM test verdicts

• Having two related but distinct products, classic MLOps and Opik, can be confusing when first evaluating the platform.
• Free cloud tiers cap data volume, such as 25k spans/month, requiring a paid plan for production-scale usage.
• Enterprise features like SSO and compliance certifications are reserved for the custom-priced Enterprise tier.

• Manual YAML/JSONL configuration only; no visual eval builder or dashboard is described.
• Cassettes must be committed to the repository, which can increase repo size as datasets grow.
• No Windows prebuilt binaries are mentioned; only macOS, Linux, and CI runners are covered.
• Eval quality is fully dependent on the scorer definitions and datasets you write; EvalCore does not generate test cases for you.