Comet vs Weights & Biases

MLOps for teams who've outgrown Weights & Biases

Predatory pricing, inflexible deployments, and unreliable performance makes WandB unusable for growing ML teams. Comet offers a more robust, reliable MLOps platform, with support for highly custom deployments, at half the price.

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Trusted by the most innovative ML teams

Olcay Cirit

Senior Staff Research Scientist, Tech - Uber

"Comet offers the most complete experiment tracking solution on the market. It’s brought significant value to our business."

Anuradha U.

Director Machine Learning - Search, Ads Monetization, Personalization - Shipt

"We rely on Comet to measure performance and upgrade models into production. It gives us a very robust process for model deployment and training."

Andy McMahon

Head of MLOps - NatWest

"Comet serves as the central system of record for over 180 data scientists and 400 data engineers, providing them with full visibility into the model development process and enabling seamless collaboration."

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In-Depth Feature Breakdown: Comet vs Weights & Biases

Explore why data scientists and Machine Learning teams are choosing Comet over Weights & Biases

Feature			Category
Custom Visualizations	Build custom visualizations with Python and Javascript	Limited customization with Vega	Experiment Tracking
Experiment Run Diffing	Diff Training Runs side-by side to find out why training runs are performing differently	No Diffing mode	Experiment Tracking
Robust Auto-Logging	Seamless integrations with popular ML frameworks and platforms	Requires manual code to log data to platform	Experiment Tracking
OR Run Filtering	Filter training runs that meet specific conditions	Only support AND filtering	Experiment Tracking
Multiple Dashboards	Ability to generate multiple views for a project for different stakeholders	Only one dashboard per project	Experiment Tracking
Model Auditing	Create formal approval processes for model promotion	Limited governance capabilities	Model Registry
Data Drift Detection	Automatic data drift detection in production on features and output	No production monitoring	Model Monitoring
Model Fairness Monitoring	Uncover model bias across different data segments	No production monitoring	Model Monitoring
Alerts for Model Performance Degradation	Instant alerting when model performance starts to deteriorate	No production monitoring	Model Monitoring

In-Depth Feature Breakdown: Comet vs Weights & Biases

Explore why data scientists and Machine Learning teams are choosing Comet over Weights & Biases

Feature

Custom Visualizations

Build custom visualizations with Python and Javascript

Limited customization with Vega

Category

Experiment Tracking

Feature

Experiment Run Diffing

Diff Training Runs side-by side to find out why training runs are performing differently

No Diffing mode

Category

Experiment Tracking

Feature

Robust Auto-Logging

Seamless integrations with popular ML frameworks and platforms

Requires manual code to log data to platform

Category

Experiment Tracking

Feature

OR Run Filtering

Filter training runs that meet specific conditions

Only support AND filtering

Category

Experiment Tracking

Feature

Multiple Dashboards

Ability to generate multiple views for a project for different stakeholders

Only one dashboard per project

Category

Experiment Tracking

Feature

Model Auditing

Create formal approval processes for model promotion

Limited governance capabilities

Category

Model Registry

Feature

Data Drift Detection

Automatic data drift detection in production on features and output

No production monitoring

Category

Model Monitoring

Feature

Model Fairness Monitoring

Uncover model bias across different data segments

No production monitoring

Category

Model Monitoring

Feature

Alerts for Model Performance Degradation

Instant alerting when model performance starts to deteriorate

No production monitoring

Category

Model Monitoring

Monitor and manage models, from small teams to massive scale

Easy to integrate with any training environment

Add two lines of code to your notebook or script and automatically start tracking code, hyperparameters, metrics, and more, so you can compare and reproduce training runs.

Experiment Management

PythonJavaR

1 from comet_ml import Experiment
2 
3 # Initialize the Comet logger
4 experiment = Experiment()

Build your own tailored, interactive visualizations

In addition to the 30+ built-in visualizations Comet provides, you can code your own visualizations using Plotly and Matplotlib.

Track and version datasets and artifacts

Knowing what data was used to train a model is a key part of the MLOps lifecycle. Comet Artifacts allows you to track data by uploading directly to Comet’s machine learning platform or by storing a reference to it.

Comet Artifacts

Manage your models and trigger deployments

Comet Model Registry allows you to keep track of your models ready for deployment. Thanks to the tight integration with Comet Experiment Management, you will have full lineage from training to production.

Comet Model Registry

Monitor your models in production

The performance of models deployed to production degrade over time, either due to drift or data quality. Use Comet’s machine learning platform to identify drift and track accuracy metrics using baselines automatically pulled from training runs.

Comet Model Production Monitoring

Log and evaluate your LLM prompts

Track your LLM prompts and responses in Comet to keep a single system of record for all your Prompt Engineering work. Add token metadata, benchmark the performance of different LLMs, and score prompt responses to find the best prompt templates for your specific use-cases!

Comet LLMOps

Frequently Asked Questions

All my data is currently stored in Weights & Biases, is it easy to migrate that data to Comet?

Yes, we have helped many current Comet customers migrate all their data in Weights and Biases to the Comet platform and have scripts to help streamline the process.

Does Comet have a community for support if I have questions?

Yes, we are well known for our world class support as we work with the ML teams at companies like Uber, Netflix, and Etsy. We also have a community Slack channel you can join to ask questions directly.

Is Comet compliant with enterprise security requirements?

Yes, we are SOC 2 Type 2 Compliant and ISO 27001 Certified. We have a wealth of experience working with financial institutions, such as Affirm and NatWest, that have rigorous security requirements.

Still Using Weights & Biases? Make The Switch To Comet

Discover Comet’s advanced capabilities and state-of-the-art security in a free demo.

Talk To Us Try Comet Free

Run open source LLM evaluations with Opik!

MLOps for teams who've outgrown Weights & Biases

Trusted by the most innovative ML teams

Olcay Cirit

Anuradha U.

Andy McMahon

In-Depth Feature Breakdown: Comet vs Weights & Biases

In-Depth Feature Breakdown: Comet vs Weights & Biases

Monitor and manage models, from small teams to massive scale

Easy to integrate with any training environment

Build your own tailored, interactive visualizations

Track and version datasets and artifacts

Manage your models and trigger deployments

Monitor your models in production

Log and evaluate your LLM prompts

Frequently Asked Questions

Still Using Weights & Biases? Make The Switch To Comet

Products

Learn

Company

Pricing

Run open source LLM evaluations with Opik!

MLOps for teams who've outgrown Weights & Biases

Trusted by the most innovative ML teams

Olcay Cirit

Anuradha U.

Andy McMahon

In-Depth Feature Breakdown: Comet vs Weights & Biases

In-Depth Feature Breakdown: Comet vs Weights & Biases

Monitor and manage models, from small teams to massive scale

Easy to integrate with any training environment

Track and share training run results in real time

Build your own tailored, interactive visualizations

Track and version datasets and artifacts

Manage your models and trigger deployments

Monitor your models in production

Log and evaluate your LLM prompts

Frequently Asked Questions

Still Using Weights & Biases? Make The Switch To Comet