Comet: The Open-Source Powerhouse for LLM and ML Experiment Tracking

As machine learning and LLM development accelerate, the need for robust experiment tracking, model evaluation, and production monitoring has never been greater. Yet most MLOps platforms—Weights & Biases, MLflow (hosted), Neptune, and others—lock your data and workflows into proprietary clouds, restrict integrations, and charge steep fees for advanced features. Comet is changing the game. With a modern, open-source platform and a growing suite of tools like Opik for LLM evaluation, Comet empowers teams to manage the entire ML lifecycle—on their terms.

Website: https://www.comet.com/site/

Why Choose Comet?

In a landscape dominated by closed, SaaS-first MLOps tools, Comet stands out with a fundamentally different approach:

• Open Source, End-to-End From LLM evaluation (Opik) to experiment tracking, model registry, and production monitoring, Comet’s open-source stack gives you full transparency and control.
• LLM Evaluation with Opik Run automated, reproducible evaluations on your LLM applications—track prompt engineering, compare model responses, and optimize before and after deployment.
• Experiment Management Log every training run, hyperparameter, metric, and artifact in a single system of record. Reproduce, compare, and share results with ease.
• Production Monitoring Detect data drift, monitor model performance, and set real-time alerts for degradation—ensuring your models stay reliable in the wild.
• Model Registry & Artifacts Version, store, and promote models and datasets with full lineage and auditability. Integrate with any cloud or on-prem storage.
• Easy Integration Add a few lines of code to your favorite ML frameworks—PyTorch, TensorFlow, Hugging Face, LangChain, LlamaIndex, and more.
• Flexible Deployment Use Comet’s managed cloud or self-host on your own infrastructure for maximum data privacy and compliance.

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Spotlight on Key Features

1. Opik: Open-Source LLM Evaluation

• Track and visualize prompt engineering and LLM chains • Automated, reproducible evaluation of LLM responses • Integrations with LangChain, LlamaIndex, OpenAI, and more • Compare model outputs, trace errors, and optimize before production

2. Experiment Management

• Log code, hyperparameters, metrics, and predictions • Visualize and compare training runs in real time • Reproduce experiments with a single click • Share results and collaborate across teams

3. Model Registry & Artifacts

• Centralized repository for all model versions • Track training data, code, and environment for each model • Promote models to production with webhooks and audit trails • Dataset versioning for full reproducibility

4. Model Production Monitoring

• Monitor input/output data drift and model performance • Set custom alerts for performance degradation • Real-time dashboards for deployed models • Integrate with any deployment environment (cloud, on-prem, hybrid)

5. Seamless Integrations

• Native support for PyTorch, TensorFlow, Keras, Scikit-learn, XGBoost, and more • LLMOps integrations with LangChain, LlamaIndex, OpenAI, and Hugging Face • API and SDKs for custom workflows • Jupyter, Colab, and notebook-friendly

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Comet vs. Closed-Source MLOps Platforms

How does Comet compare to the big names in experiment tracking and LLMOps? Here’s a side-by-side look:

Beyond the Feature Matrix

• No Vendor Lock-In: Comet’s open-source core means you’re never trapped by pricing changes or feature gating.
• Transparency: Audit every step of your ML pipeline, from data to deployment.
• Custom Workflows: Build exactly what you need—integrate with any stack, automate with APIs, and extend with plugins.
• Privacy & Compliance: Self-host for full control over sensitive data and regulatory requirements.

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Getting Started in Minutes

Comet is designed for effortless integration and deployment:

☁️ Cloud (Quickest Start)

• Sign up for a free account at comet.com
• Integrate with your favorite ML frameworks in minutes
• Access dashboards, model registry, and monitoring out of the box

🐳 Self-Hosting

• Clone the

• Follow the

for Docker or manual setup

• Connect your ML pipelines and start tracking experiments and LLM evaluations

💻 Code Integration

Add a few lines to your Python scripts or notebooks:

from comet_ml import Experiment

experiment = Experiment(project_name="YOUR PROJECT")
experiment.log_metric("accuracy", 0.95)

Or for LLM evaluation with Opik:

from opik import track

@track
def llm_chain(user_question):
    # Your LLM logic here
    return response

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Real-World Success Stories

"Comet has become an indispensable part of our ML workflow. We can seamlessly compare and share experiments, debug, and stop underperforming models." — Carol Anderson, Staff Data Scientist

"Comet offers the most complete experiment tracking solution on the market. It’s brought significant value to our business." — Olcay Cirit, Staff Research and Tech Lead

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Practical Applications

LLMOps & Prompt Engineering

Track, evaluate, and optimize LLM chains and prompt engineering workflows with full traceability and reproducibility.

Research & Experimentation

Log every experiment, compare results, and collaborate across teams—accelerating research cycles and ensuring reproducibility.

Model Deployment & Monitoring

Monitor models in production for drift, performance, and reliability—catching issues before they impact users.

Regulatory Compliance

Maintain full audit trails of data, code, and model lineage for compliance with industry standards and regulations.

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Join the Comet Community

With thousands of users and a growing open-source ecosystem, Comet is rapidly evolving:

• Contribute Code: Help enhance features or fix bugs via GitHub
• Suggest Features: Share your ideas on GitHub Issues or the Comet forum
• Join Discussions: Connect with users and developers on Discord and community channels
• Support Development: Sponsor the project or spread the word

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Final Thoughts

MLOps and LLMOps shouldn’t be a source of lock-in, hidden costs, or data privacy headaches. Comet represents a new era of open, flexible, and developer-centric machine learning operations. Whether you’re a researcher, data scientist, or enterprise ML team, Comet offers a compelling alternative to closed, SaaS-first platforms—putting you back in control of your models, data, and workflows.

Ready to take your ML and LLM workflows to the next level? Explore comet.com or dive into the code on GitHub.