Modelbit

Deploy custom and open-source ML models to autoscaling infrastructure in minutes, with built-in MLOps tools and Git integration for seamless model serving.
Machine Learning Deployment MLOps Tools Model Serving
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Modelbit is an ML engineering platform that lets users quickly deploy custom and open-source machine learning (ML) models to autoscaling infrastructure with built-in MLOps tools for model serving. With Modelbit, users can deploy any model to a REST API in minutes, with model code synced automatically through Git so model deployment is fast and painless.

Modelbit includes features to help you get your models running successfully:

  • Autoscaling Compute: Automatically scales infrastructure to meet your model's needs.
  • MLOps Tools: Includes alerting, logging, and monitoring to keep your models running in production.
  • Git Integration: Code review and version control is integrated with your existing Git workflow.
  • Model Registry: Manage multiple model deployments and training jobs.
  • Security: Industry-standard SOC2 compliance and bug bounties to ensure your models are deployed securely.

Modelbit supports a broad range of ML models, including computer vision models built with PyTorch and open-source language models. You can also deploy models from Jupyter notebooks, Snowpark ML, and other cloud-based environments.

Pricing is flexible to accommodate different needs:

  • On-Demand: $0 per month, with compute costs starting at $0.15 per CPU minute and $0.65 per GPU minute.
  • Enterprise: Custom pricing for reserved compute instances, volume discounts, and custom contracts.
  • Self-Hosted: Run Modelbit in your private cloud infrastructure for maximum security and control, with custom pricing for implementation support and migration assistance.

Modelbit is designed to help machine learning teams quickly and easily deploy models, making it a good fit for companies that use ML models in production.

Published on June 14, 2024

Related Questions

Can you recommend a platform that allows me to deploy machine learning models to autoscaling infrastructure with minimal setup? I need a solution that integrates MLOps tools with model serving, can you suggest something? How can I quickly deploy custom ML models to a REST API without worrying about infrastructure scaling? Is there a platform that offers a model registry to manage multiple model deployments and training jobs, with built-in security and compliance features?

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