Focus on ML, not infrastructure

Inference and training anywhere – on-prem, edge and public cloud.

Get personalized strategies from our cloud native AI/ML experts
Platform9 AI/ML

Trusted by leading companies

We delight IT operations, edge infrastructure, developers, and DevOps teams at these companies.

Six reasons
Why your AI/ML initiatives are slow to launch

Challenges faced by our customers before they transitioned to Platform9.

Icon: Time Spent

A lot of time is spent on infrastructure by data scientists and MLOps

Icon: Cost Increases

Slow roll out of AI apps increases costs and decreases adoption

Icon: Skilled Resources

Operational tasks are consuming the time of scarce skilled resources

Icon: Cloud

Initially, the cloud appeared perfect, but now its costs have skyrocketed

Icon: POC Rollouts

POC rollouts don’t meet executive’s expectations or timelines

Icon: Equipment Shortage

Despite equipment shortage, infrastructure flexibility is unrealistic

Explore alternatives to public cloud for your Machine Learning workloads

Buy your own GPU

GPUs and accelerators run in commodity x86 hardware and can be bought today to run anywhere for a fraction of the cost of public cloud.

Lease or purchase your own servers

You can now rent or buy hardware suiting your financial models (OpEx/ CapEx), with amortization over 3, 5, or 7 years.

Consume managed data centers, private clouds, and colos

Data centers have modernized alongside public cloud, eliminating traditional rack, stack, and provision cycles while enabling consuming them as a service.
Watch the webinar to learn how to maximize infrastructure ROI for AI/ML workloads

Platform9 offloads the burden of running infrastructure anywhere

Focus on your data science and machine learning modeling and execution

AI/ML: Data Pipeline

Data pipeline, training & inference

Platform9’s agnostic approach allows you to choose your tools and run anywhere.

Platform9 Managed Kubernetes

Offload operational complexity while gaining portability and efficient execution of AI/ML workloads on Kubernetes.

AI/ML: Platform9 Managed Kubernetes
AI/ML: Cloud on prem

Cloud, on-prem or edge

Run your workloads on any infrastructure, from bare metal in edge locations to the public cloud.

MLOps for streamlined app deployment

Icon: Deploy Anywhere

Deploy anywhere instantly

Create a cluster and instantly deploy your ML tools and models across public, private, and edge clouds.
Icon: Own Infrastructure

Choose your own infrastructure

Choose the right cloud for your team, regardless of need or location, and leverage any workflow tool.
Icon: Infrastructure Burden

Minimize infrastructure burden

Focus on building and running ML workloads instead of managing infrastructure.
Icon: Automated Operations

Get automated operations

Simplify operations with monitoring, upgrades, security patching, and more.

Resources for AI/ML infrastructure management

Resource: Why and how to run ML workloads on Kubernetes

Why and how to run ML workloads on Kubernetes

Resource: How to maximize infra ROI for AI/ML workloads

Enhance Data Workflows with MetaFlow on Platform9 Managed Kubernetes

Resource: Deploying, monitoring, and managing clusters with Platform9

Deploying, monitoring, and managing clusters with Platform9

Learn how to simplify your ML infrastructure operations

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