Heroku for Machine Learning

Train, evaluate and deploy ML models in fully custom compute environments. As simple as git push.
Fully custom, containerized environments for training and inference
On-Demand GPUs for lightning fast inference
Backed by your Git repo for a fully infrastructure-as-code experience
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Machine learning teams run on Modelbit

Trusted by Engineering Leaders

Teams building vision powered products 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.
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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
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Custom Models
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.

Serverless Infrastructure You Can Trust

Every model you deploy gets it's own container running on autoscaling GPUs.

Every model gets its own isolated container and API.

When you develop with Modelbit, you aren't just getting an API. Your models are instantly deployed to their own containers that you'll have full control over.

Fast, scalable compute with lightning fast cold starts.

Run your models with on-demand GPUs that scale up and down as needed. You will have full control over your runtime environment and hardware.
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Enterprise Ready - in your cloud or ours.

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.

Serverless GPUs that scale to zero.

Only pay for what you use. No networking or storage fees. You're only charged for the time that you're model is running - calculated to the second.
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Compute Pricing

CPU
Cost Per Second
2vCPU Processor
$0.000052 / Sec
GPU
Cost Per Second
Nvidia T4
$0.000184    / Sec
Nvidia L4
$0.000231    / Sec
Nvidia A10G
$0.000326  / Sec
Nvidia A100 40GB
$0.001176    / Sec
Nvidia A100 80GB
$0.001593    / Sec
Nvidia H100
$0.002325    / Sec

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.

Data warehouse icon

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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All the MLOps Tools You Need

Git sync, model logs, monitoring, and much more. Modelbit has built-in tools to help you manage your vision models running in production.

Model logging, monitoring, and performance tracing.

We built a web app with all the tools you need to integrate computer vision models into your product with confidence.

Everything is backed by your Git repo

A staging environment is as easy as making a git branch. Modelbit makes it easy to integrate your computer vision models into your CI/CD workflows.

Run experiments with A/B tests and shadow deployments.

Modelbit makes it easy to rapidly deploy new vision models and run experiments to see which models perform best.

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
Security
Industry-standard SOC2 compliance, bug bounties, and penetration testing.
Auto Retraining
Schedule your models to automatically retrain and redeploy production.
Monitoring
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.

Want to see a demo before trying?

Get a demo and learn how teams are building computer vision products with Modelbit.
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