On-Demand ML Infrastructure Powered by Git

Deploy, scale, and manage your ML models with ease - in your cloud or ours.

Machine learning teams run on Modelbit

ML workflows built for developers

Spend time building better ML models, not fighting with your infrastructure.
Whether you're building something custom or deploying open-source models, you can use Modelbit to rapidly deploy your models to production.
With one git command, your model is instantly deployed to an isolated container.
Modelbit is backed by your Git repo. A staging environment is as easy as a git branch.
Modelbit will automatically generate APIs for each version of your model.
Choose your hardware and it will automatically scale up and down.
Logging, alerting, performance tracing - Modelbit has all the MLOps tools you need to serve and scale your ML models in production.

Ship better models, faster

Modelbit lets you deploy and iterate faster with git-based deployment workflows.

Deploy any model with git push

One command and your code is instantly synced with your Git repo, and deployed to a fully isolated container behind a REST endpoint.

Fast, scalable infrastruture

Run batch or real-time inference with on-demand compute that automatically scales up and down. Fast cold starts, and fully configurable latency requirements.

Enterprise Readiness

Deploy to our secure cloud or to your own. Modelbit is backed by your Git repo, and built from the ground up to be fast, safe, and secure.

Trusted by ML leaders

Machine learning teams that move fast choose Modelbit.
We retrain and redeploy thousands of models daily, so choosing the right partner for model deployment is critical. We initially investigated SageMaker, but Modelbit’s performance paired with its ease of use was a game-changer for us.
Cody Greco
Co-Founder &CTO
With Modelbit, we’ve been able to experiment with new models faster than ever before. Once a model is up, we’re able to make iterations in a manner of minutes. It feels a bit like magic every time I make a change to our model code, push it to Github, and see it live on Modelbit just seconds later.
Daniel Mewes
Staff Software Engineer
Modelbit enabled us to easily deploy several large vision transformer-based models to environments with GPUs. Rapidly deploying and iterating on these models in Modelbit allowed us to build the next generation of our product in days instead of months.
Nick Pilkington
Co-Founder &CTO
Explore Case Studies

Go from prototype to production

Everything you need to deploy, serve, and scale your ML models in your product.
1. Build models with any technology

Deploy any open-source or custom ML model

Computer vision models built with PyTorch. Open-source LLMs like Mistral and Llama 3.  Fine-tuned multimodal models.

No matter what you're building, Modelbit can help you deploy it in minutes.

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Sample data rows and results metadata from a Modelbit dataset
2. Developer workflow

git push to deploy your models to fully isolated containers

You have full control over your environment. Deploy your code with git push, Modelbit will deploy it to an isolated container in minutes.

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Sample data rows and results metadata from a Modelbit dataset
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One line of code

Deploying your model is as simple as calling mb.deploy right from your notebook. No need for special frameworks or code rewrites.

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Deploy into Warehouse

Models are deployed directly into your data warehouse, where making a model inference is as easy as calling a SQL function!

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From Python to REST

Modelbit models become REST APIs in the cloud. Your team can call them from websites, mobile apps, and IT applications.

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Backed by git

Modelbit is backed by your git repo, where data is secure and version controlled, and CI/CD runs when changes are deployed.

3. Run on next-gen infrastructure

On-demand compute that automatically scales

We built a new compute framework that scales up and down as needed. Run on compute in our cloud or deploy Modelbit into your VPC.

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Snowflake code calling a Modelbit model
4. Integrate your ML with Git

Everything backed by your git repo

Modelbit is backed by your git repo. GitHub, GitLab, or home grown.

Code review. CI/CD pipelines. PRs and Merge Requests. Bring your whole git workflow to your Python ML models.

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5. Manage your models like a pro

Built-in MLOps tools and integrations

Once your models are deployed you'll get logging, monitoring, alerting, and all the tools you need to manage ML in production. Modelbit also integrates with your favorite ML tools like Weights & Biases, and many more.

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Deploy anywhere.

Run your models on autoscaling compute, in your cloud or ours.

Run Modelbit in your private cloud

Use Modelbit to deploy ML models into your cloud for maximum convenience paired with maximum security. Reach out to us to request access.

Request Access
Sample data rows and results metadata from a Modelbit dataset

Deploy your models to Modelbit's cloud

Fast, safe, and secure. Modelbit's managed cloud lets you run your models on the latest hardware that automatically scale up and down. Pay only for what you use.

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Modelbit integrates with your stack.

Connect to your favorite warehouse, feature store, experiment tracker, and more.

Integrate with your favorite ML tools

From model experiment trackers, hosted data science notebooks, to feature stores and Snowpark ML.

Modelbit integrates with your ML stack.

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Built-in MLOps tools that help you scale

When you deploy models with Modelbit, you get all the tools and integrations you need to run ML in production.
Backed by Git
Code review. CI/CD pipelines. Bring your whole git workflow to your Python ML models.
Model Registry
Manage hundreds, or even thousands of model deployments and training jobs
Industry-standard SOC2 compliance, bug bounties, and penetration testing.
Auto Retraining
Schedule your models to automatically retrain and redeploy production.
Logging. Alerting. Monitoring. Everything you need to know about your ML models in production.
Model Testing
Run A/B tests, shadow deployments, and more to always have the best models in production.

Ready to deploy your ML model?

Get a demo and learn how ML teams are deploying and managing ML models with Modelbit.
Book a Demo