The right ML model deployment platform depends on who you want running the infrastructure around the model, your own team or a managed service, and on whether you already use Kubernetes or a particular cloud. This page lists nine deployment platforms in no particular order, from managed cloud services to open-source serving tools.
Training a model in a notebook is the start. Deploying it means turning it into a service your product can call reliably, with scaling, monitoring and a way to roll back when something goes wrong.
The nine platforms below are listed alphabetically and in no particular order. Every description comes from the provider's own website, read this week. If you're on Google Cloud already, my guide to Google's AI platform covers the managed route there.
ML model deployment platforms at a glance
I checked the free plan column on each provider's own website, as of Oct 2026. Yes means the page I read names a free plan, free tier, free credits or open source software. Trial only means a free trial or starter credit. Not stated means the pages I read named neither, even where there's a button inviting you to start, so check the provider's page before you sign up.
| Tool | Type | Good for | Free plan |
|---|---|---|---|
| Amazon SageMaker AI | Managed ML deployment on AWS | Dedicated endpoints for models on AWS | Yes |
| Anyscale | Managed Ray platform | Scaling Python AI workloads with Ray | Not stated |
| BentoML | Open-source model serving framework | Packaging any model as an API | Yes |
| Databricks Model Serving | Model and agent serving in Databricks | Serving models beside the lakehouse | Not stated |
| Gemini platform | Google Cloud's AI and ML platform | Training and deploying on Google Cloud | Not stated |
| KServe | Open-source model serving on Kubernetes | Self-hosted serving on your clusters | Yes |
| NVIDIA Dynamo-Triton | Open-source inference server | High-throughput serving on GPUs and CPUs | Yes |
| Ray Serve | Model serving library for Ray | Composing several models in Python | Not stated |
| TrueFoundry | AI deployment platform and gateway | Deployments plus an AI gateway | Not stated |
Nine deployment platforms worth a look
Amazon SageMaker AI
Best for: Data teams on AWS that want to deploy open-source and custom models to production on dedicated SageMaker AI infrastructure.
Anyscale
Best for: Teams that want Ray, the AI compute engine, managed for data curation, distributed training and batch embedding jobs.
BentoML
Best for: Python developers who want an open-source way to serve AI and ML models as APIs, with a managed option for scale.
Databricks Model Serving
Best for: Companies already on Databricks that want to deploy AI models and agents next to their data and governance.
Gemini platform
Best for: Organisations on Google Cloud that want to train, test, tune and deploy ML models and agents on one platform.
KServe
Best for: Platform engineers who want an open-source standard for self-hosted generative and predictive AI serving on Kubernetes.
NVIDIA Dynamo-Triton
Best for: Infrastructure teams that want open-source inference serving on NVIDIA GPUs, other accelerators, and x86 or ARM CPUs.
Ray Serve
Best for: Engineers who want to scale model serving in Python, combining several models and business logic in one application.
TrueFoundry
Best for: Enterprises that want an AI deployment platform, training and fine-tuning, plus AI and MCP gateways in one product.
Which platform fits the job
- Managed Ray for large AI workloads: Anyscale.
- Serving models beside your lakehouse: Databricks Model Serving.
- Training and deploying on Google Cloud: Gemini platform.
- High-throughput serving on many kinds of hardware: NVIDIA Dynamo-Triton.
- Combining several models in Python: Ray Serve.
- Deployments plus an AI gateway: TrueFoundry.
The model is the small part
Google Cloud's MLOps guide, published in the Google Cloud Architecture Center, says only a small fraction of a real-world ML system is composed of the ML code, and the required surrounding elements are vast and complex.
That's the best lens for choosing a deployment platform. You're not really picking where the model runs. You're picking who handles data checks, monitoring, versioning, scaling and rollbacks around it. A managed platform takes much of that off your plate. An open-source serving tool gives you control and leaves the rest of that list with your team.
If your model only needs a GPU now and then, our best serverless GPU platforms list covers platforms that scale back to zero when idle.
Managed cloud service, open-source serving or Kubernetes?
A managed cloud service deploys your model on the provider's infrastructure, with monitoring and scaling built in. It's the quickest route if you already use that cloud.
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An open-source serving framework packages your model as an API you can run anywhere. You choose the hosting and keep the code portable.
A Kubernetes-native platform runs models on clusters your team already manages, which suits companies with a platform team and many models.
If you're not sure which camp you're in, look at who answers the pager today. A team that already runs its own servers will find open-source serving natural. A team that's never managed infrastructure will move faster on a managed service, and that's a perfectly sensible choice.
If you'd rather call a hosted model than deploy your own, our AI inference platforms list compares the platforms that serve one.
Seven things to plan before you deploy
- How will you version models and roll back a bad release?
- What will you monitor: errors, response times or prediction quality?
- How will you spot when live data drifts from training data?
- Does the model need GPUs, CPUs or both?
- How will you test a new model before full traffic?
- Who gets alerted when it breaks?
- Where are requests and predictions stored?
Do you make an ML deployment platform? Tool makers can ask to be considered for a featured place on this list.
My plain advice on deploying ML models
Choose based on the team you have, not the team you wish you had. If nobody wants to run Kubernetes, a managed service is the honest answer. If you already have a platform team and many models, open-source serving on your own clusters gives you more control.
Either way, plan monitoring and rollbacks before the first deployment, not after the first incident. My guide to deep learning explains why some models need GPUs to serve at all.
See also: AI Haiku Generators.
Frequently asked questions
What is ML model deployment?
It's turning a trained model into a reliable service that your product or users can call, with scaling and monitoring.
What is the difference between MLOps and model deployment?
Deployment is one stage. MLOps covers the whole lifecycle, including data checks, training pipelines, monitoring and retraining.
Can I deploy a model without Kubernetes?
Yes. Managed cloud services and serverless options deploy models without you running a cluster.
Is open-source model serving free?
The serving software is free to use, but you still need hardware to run it on.
How do I monitor a deployed model?
Track errors and response times, and compare live inputs and predictions with what the model saw in training.
Which deployment platform suits a small team?
Usually a managed service, because it handles scaling and monitoring so a small team can focus on the model and product.
