A managed AI and data infrastructure platform that lets teams deploy, orchestrate, and manage AI agent frameworks and data pipelines on their own cloud (AWS, GCP, Azure). It provides a unified control plane for running tools like LangChain, CrewAI, AutoGen, Haystack, and other AI frameworks without managing underlying Kubernetes infrastructure. Unlike generic compute platforms such as Anyscale or Modal, Shakudo focuses on providing a fully pre-integrated stack of 170+ data and AI components that can be composed into production pipelines, all deployed inside the customer's VPC for full data residency and compliance.
A managed AI and data infrastructure platform that lets teams deploy, orchestrate, and manage AI agent frameworks and data pipelines on their own cloud across AWS, GCP, and Azure. It provides a unified control plane for running tools like LangChain, CrewAI, AutoGen, Haystack, and other AI frameworks without managing the underlying Kubernetes infrastructure.
Shakudo is a sales-priced enterprise AI infrastructure platform for deploying governed AI agents, AI applications, and data pipelines inside a customer's own cloud, private cloud, on-premises, or air-gapped environment; exact prices are not publicly listed, so buyers must contact sales for a demo, workshop, proof of concept, and quote.
Shakudo positions itself as "The Operating System for AI," giving enterprise teams a managed control plane for building AI and data systems on top of their existing AWS, GCP, Azure, private cloud, or on-premises infrastructure. Instead of asking platform teams to assemble Kubernetes, workflow orchestration, vector databases, agent frameworks, security controls, monitoring, and model governance from scratch, Shakudo provides a pre-integrated stack that its public site describes as 170+ AI tools. The tool supports frameworks and technologies such as LangChain, CrewAI, AutoGen, Haystack, vector databases, knowledge graphs, reverse ETL, workflow automation, document extraction, and text-to-SQL. Its strongest technical differentiator is that deployments run in the customer's own VPC or controlled environment, including air-gapped networks, so sensitive enterprise data can remain within the organization's security boundary.
The platform is aimed at organizations where AI experimentation has moved beyond prototypes and into operational workflows that require governance, auditability, and repeatable deployment. Shakudo's public product pages describe enterprise security capabilities including SOC 2 Type II certification, automatic mitigation of OWASP Top 10 LLM risks, deep role-based access control linked into stack components, air-gapped network support, container image vulnerability scanning, and PyPI and CRAN package vulnerability scanning. The platform page also highlights isolated environments in a customer's VPC, consistent jobs from development to production, on-demand distributed computing with Spark, Dask, and Ray clusters, hardened model and data services, logging, monitoring, alerting, audit trails, and lineage.
Shakudo also provides higher-level AI applications and platform services such as Patina, Kaji, AI Gateway, MCP Proxy, Extract Flow, knowledge graph tooling, text-to-SQL, and vector database deployment for teams that want more than raw infrastructure. The best fit is a regulated or data-intensive enterprise that needs private deployment, multi-cloud or hybrid flexibility, dedicated support, and a governed way to compose many open-source and commercial AI tools. The weakest fit is a small team looking for transparent monthly pricing, a public free tier, or a lightweight self-serve multi-agent builder, because Shakudo's buying path is demo-led and quote-based rather than public fixed-tier SaaS pricing.
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Shakudo deploys within the customer's own infrastructure, whether that is a public cloud VPC on AWS, GCP, or Azure, a private cloud, or an air-gapped on-premises environment. This makes it suitable for defense, government, and heavily regulated enterprise environments where data sovereignty is a hard requirement.
The platform provides a catalog of 170+ pre-integrated open-source AI/ML and data tools that are configured to work together. This includes agent frameworks, vector databases, ETL tools, knowledge graph engines, and more, reducing the integration engineering typically required to build a composable AI data stack from individual open-source projects.
Shakudo is described on its public site as SOC 2 Type II certified and includes automatic mitigation of OWASP Top 10 LLM risks, deep RBAC linked into stack components, container image vulnerability scanning, and PyPI/CRAN package vulnerability scanning. These security features are built into the platform foundation to provide governance across deployed AI services.
Beyond infrastructure, Shakudo offers ready-to-deploy AI applications: Patina provides autonomous cross-department workflows with auditability, Kaji serves as an enterprise AI expert assistant, and the AI Gateway acts as a unified control plane for governing AI model usage. These applications accelerate time-to-value by providing business-ready functionality on top of the managed infrastructure.
Shakudo includes a dedicated autonomous multi-agent platform that runs within the customer's cloud, along with an MCP Proxy that connects existing enterprise APIs to AI systems. This combination allows organizations to deploy complex multi-agent workflows that can interact with internal systems and data sources while maintaining security controls and audit trails.
Not publicly disclosed
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