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AZURE AI FOUNDRY · AZURE AI STUDIO ALTERNATIVE

The Azure AI Foundry Alternative That Runs in Your Own Infrastructure

Shakudo runs the AI platform inside your own infrastructure, with a swappable model catalog and portability across Azure, AWS, GCP, and on-premises. Calls to a hosted model still go to that provider, under your own contract, with zero retention and no training on your data.

300+
Pre-Built Components
2-4
Weeks to Deploy
5,000+
Connectors

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Quick overview of pricing, features, and deployment options.

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THE CHALLENGE

Azure AI Foundry Is a Credible Platform. It Is Also an Azure Platform.

Azure AI Foundry is a capable managed environment: a curated model catalog, evaluation tooling, agent services, and integration with the rest of Azure. What it does not do is let the platform layer exist anywhere except where Azure runs it, or let the catalog contain anything Microsoft has not listed for your region. Those are separate decisions from the model choice, and they bind harder with every application built on top.

A Model Catalog You Control

Shakudo integrates 300+ components and treats the model catalog as a swappable layer. Route a request to an Azure OpenAI model, a model on another cloud, or an open-weight model you host yourself, without rewriting the applications around it.

Portability Across Clouds, Not a Rewrite

The platform runs on Kubernetes you already operate, so the target is a deployment choice rather than an architecture decision. Azure, AWS, GCP, your own data center, or an air-gapped network, with one identity, policy, and audit layer across them.

The Data Plane Sits Inside Your Perimeter

Prompts, embeddings, outputs, credentials, and logs are stored in your own databases and object storage, inside infrastructure you run. There is no Shakudo-hosted data plane holding a copy. A call to a hosted model goes to that provider under your own contract, with zero retention and no training on your inputs.

CAPABILITIES

The Platform Around the Model Catalog

Integration, deployment, and isolation are platform defaults, so the model service becomes one component instead of the foundation.

ONE UNIFIED ECOSYSTEM

One Ecosystem for the Entire Data Stack

Ingestion, transformation, orchestration, ML training, model serving, and BI managed through a single platform. The pipeline that feeds your models lives next to the models, rather than in services your data team has to reach across vendors to assemble.

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One Ecosystem for the Entire Data Stack
ANY CLOUD OR ON-PREM

Deploy Wherever Kubernetes Exists

Azure, AWS, GCP, or your own data center. Models reached through Azure AI Foundry sit side by side with models from other providers and open-weight models you host, under one consistent identity, policy, and audit layer. Moving clouds is a deployment change, not a rebuild.

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Deploy Wherever Kubernetes Exists
IN YOUR PERIMETER

Isolation Without Giving Up Hosted Models

Run fully air-gapped with self-hosted models, or run in your own cloud account and enable hosted models deliberately. Platform data, credentials, and logs stay inside your boundary either way, and each hosted call is made on your own provider contract.

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Isolation Without Giving Up Hosted Models

Shakudo vs. Azure AI Foundry

Shakudo

A platform in your infrastructure, on any cloud

  • 300+ components plus Azure OpenAI models, other providers, or open-weight models you host
  • Runs in your infrastructure: Azure, AWS, GCP, on-premises, or air-gapped
  • Your own cloud rates and model contracts, with no per-seat platform license
  • Swap the model or the cloud without rewriting the applications around it
  • Operational in 2-4 weeks on Kubernetes you already run
Other platforms

A managed AI platform on Azure

  • Catalog limited to the models Microsoft lists, and to the Azure regions that serve them
  • Prompts and outputs stay in your Azure subscription and region, but the platform layer above them is Azure-managed
  • The platform layer is an Azure service, so the deployment target is Azure: another cloud, your own data center, and air-gapped networks are outside what the service offers
  • Leaving Foundry means re-integrating the models and the pipelines built around them
  • Per-token pricing and throughput quotas set by Azure, with commitments negotiated through Azure

We’re an on-prem company because we didn’t want our data exposed to laws that we had no control over. Put it in the cloud and it may be subject to a different country’s legal framework and exposure to being opened at any time. The company made a decision to keep as much on-prem as possible, because then we had control over where that information ended up.

James Wakelin
Director of Business Intelligence
WHITECAP RESOURCES
1 TB/mo
Relational Data Governed
375K
Barrels of Oil Equivalent a Day

Frequently Asked Questions

How is Shakudo different from Azure AI Foundry?
Azure AI Foundry is a managed AI platform: it brings a curated model catalog, evaluation tooling, and agent services together inside Azure and operates that layer for you. Shakudo is the platform that sits around and underneath the model choice. It runs in your own infrastructure on any cloud or on-premises, integrates 300+ components for data and ML, and treats the model catalog as one swappable part. An Azure OpenAI model, or a model reached through Azure AI Foundry, remains callable through the platform on the agreement you already hold, and it sits next to models from other providers and open-weight models you host, so the model service is a component rather than the foundation.
What stays in our environment, and what leaves it?
Platform data stays in your environment: prompts, embeddings, outputs, credentials, and logs are stored in your own databases and object storage, inside infrastructure you run. There is no Shakudo-hosted data plane holding a copy. The one deliberate exception is a hosted model call: when a workflow calls Azure OpenAI, a model behind Azure AI Foundry, or another hosted provider, that request goes to that provider under your own contract, with zero retention and no training on your inputs. Self-hosted open-weight models stay entirely inside your boundary, including in air-gapped deployments.
Can we keep using Azure OpenAI and the other models we already run?
Yes. Shakudo is not a replacement for the models; it is the platform that reaches them. An Azure OpenAI deployment can be called through the platform on the Azure agreement you already hold, alongside models from AWS, GCP, or open-weight models you host, with one identity, policy, and audit layer across all of them. That also means migration is workload by workload: adopt the platform for a new workload first, then move existing pipelines when each one is ready, with the current stack still running beside it.
How long does implementation take?
Most enterprise customers are operational within 2-4 weeks. Shakudo deploys on Kubernetes you already run and connects to your identity provider, so the work is integration rather than migration: existing pipelines keep running while workloads move over.
What does pricing look like?
Pricing is based on your deployment scale and requirements. We work with enterprises of all sizes. The best way to understand pricing for your use case is to request a pricing overview through the form above.
What security attestations does Shakudo hold?
Shakudo holds SOC 2 Type II. A copy of the report is available under NDA on request. Because the platform runs in your infrastructure, its controls sit inside your own audit boundary rather than behind a vendor tenancy, and platform data residency is a deployment property you configure rather than a vendor commitment you rely on.

Keep the Azure Models. Move the Platform Inside Your Own Infrastructure.

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