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Deploy any model, anywhere.

Deliver your insights to any customer, with any data, in any country, on any infrastructure, securely.

Global Model Deployment Graphic
Federated Inference

Deliver analytics inside customers infrastructure.

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Private LLMs

Keep custody of your data.

Hybrid/Multi-Cloud

Don't fight data gravity.

Edge Inference

Process data in real-time at the edge.

FEDERATED INFRASTRUCTURE

Fix data governance with architecture

Legacy SaaS architectures expose customer data to harvesting and leakage. Keep customer data safe while delivering a SaaS experience.
  • Confidential data stays where it is
  • Model IP is protected
  • Fine-tuned models are deployed privately
  • Centralized, automated provisioning
  • DATA CUSTODY

    Your customers are thinking differently about data.

    Organizations are increasingly hesitant to transfer data to third-parties for insights. From practical problems like data gravity to increased regulatory restrictions, data is becoming increasingly immobile.

    The rise of Large Language Models and other Generative AI are making the problem even worse.

    Regional inference and datastores screenshot

    PROXIML SOLUTION

    If you can't bring the data to the model, bring the model to the data

    proxiML's federated infrastructure platform lets you deploy models inside your customer's infrastructure easily and securely.
    It only takes 4 steps.
  • 1
    Load your model onto the platform
  • 2
    Your customer creates a proxiML deployment
  • 3
    The customer creates a trust relationship with you
  • 4
    Start deploying models directly to the customer
  • FEDERATION IN PRACTICE

    Manage 1,000s of customer deployments seamlessly

    Fully Automated

    Our Python SDK makes it easy to programmatically execute jobs on customer infrastructure. Just provide the job specification and proxiML handles the rest.

    Centralized Management

    Manage and support 1000s of separate deployments through a single pane of glass. Reuse base models across customers seamlessly.

    Data Partnerships

    Forge more equitable data sharing relationships with customers. Easily separate data for inference and data shared for base model improvements.

    DATA PITFALLS

    Don't risk your business over data

    Many analytics companies and initiatives fail because of antiquated thinking about data. Don't make these mistakes.

  • Leaking customer data through shared inference models or poor controls can end your business.
  • The chicken and egg problem of access to initial datasets kills businesses before they can start.
  • Excluding customers that won't send you their data for inference dramatically limits your market
  • Sending your models to customers directly risks losing your IP and limits usable licensing models.
  • Bankruptcy court
    Need help getting started?

    Speak to one of our architects to get personalized on-boarding recommendations

    Contact us
    Explore pricing options

    Our simple pricing model only charges for what you use.

    Are you ready to be everywhere?

    Sign-up Now

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