HyperLake

HyperLake is a sovereign infrastructure platform built for organizations that are preparing for a world where AI agents become the primary consumers of enterprise infrastructure. Unlike traditional data platforms designed for humans, dashboards, applications, and scheduled pipelines, HyperLake is architected from the ground up to handle the fundamentally different behavior of AI agents. These agents query data continuously, call tools, trigger workflows, generate artifacts, and operate across multiple systems simultaneously. They need uninterrupted access to governed compute, data, policies, and services. HyperLake provides the command center to deploy, manage, run, secure, and govern this agentic infrastructure. The first product wedge is Agentic Data Cloud Infrastructure: an open-stack data, analytics, semantic, workflow, and agent infrastructure that deploys inside the customer's own VPC, private cloud, or on-prem environment. The broader vision is to manage many agentic infrastructure stacks including HyperLake-native stacks, customer-owned cloud services, AWS/GCP/Azure-native components, open-source technologies, governed data services, workflow systems, and MCP tools. The goal is to make agentic infrastructure usable, secure, and production-ready end to end. Enterprises can choose the stack, deploy it where their data lives, govern every human and agent interaction, audit every action, and scale new AI use cases without rebuilding the operating layer each time. It is built for organizations where AI agents are first-class infrastructure consumers, not afterthoughts.

Unified Governance and Access Control

HyperLake deploys a global policy layer that evaluates every request, whether from a human or an AI agent, against dynamic governance rules in real time. This is not a simple access control list. It is a comprehensive system that enforces role-based access control (RBAC), attribute-based access control (ABAC), column masking for automatic PII redaction per role, row-level security that filters by department, region, or role, and an immutable audit trail that version-tracks every action. Access is enforced consistently across all data sources, queries, and context retrieval operations. This means AI agents cannot accidentally or maliciously access data they should not see, and human operators have full visibility into every interaction.

The Traceability Loop

Every single agent action, inference, query, and training run is recorded through immutable provenance logs. HyperLake creates a complete audit trail that allows organizations to trace any AI decision back to its source data with full auditability. This is critical for compliance, debugging, and trust. If an AI agent makes a questionable decision, you can instantly see what data it accessed, what queries it ran, and what context it retrieved. This traceability loop ensures that AI systems are not black boxes but transparent, accountable components of your infrastructure.

Data Sovereignty by Design

HyperLake ensures that AI agents can operate on data without ever moving it outside its secure environment. Sensitive information remains under the full control of the data owner through sovereign deployment and confidential compute patterns. The platform deploys 100% inside your cloud, meaning your data never touches third-party infrastructure. This is a fundamental architectural difference from competitors who require data to be copied or moved to their own servers for processing. With HyperLake, your data stays where it belongs, and agents come to the data, not the other way around.

Zero Compute Markup Pricing Model

Most modern data platforms charge a significant markup on compute usage. This model breaks down catastrophically in the age of autonomous AI agents. A single misconfigured agent can generate thousands of queries in minutes, leading to unexpected five-figure bills overnight. At scale, when hundreds of agents iterate, retry, and explore simultaneously, costs can grow exponentially. HyperLake eliminates this entirely with a $0 compute markup model. You pay only your cloud provider for the underlying compute resources. This gives organizations the freedom to experiment and innovate without fear of the invoice.

Autonomous AI Agent Operations

Enterprises deploying autonomous AI agents that continuously explore data, retrieve context, test hypotheses, and iterate need a platform that can handle high-frequency, unpredictable query patterns. HyperLake provides the governed data and context runtime required for these agents to operate securely at scale. The platform handles the continuous retrieval, real-time context building, and autonomous exploration that traditional data platforms were never designed to support. Organizations can deploy hundreds of agents simultaneously without worrying about governance breaches or runaway compute costs.

Human-Agent Collaborative Analytics

HyperLake enables a new paradigm where human analysts and AI agents operate on the same governed data platform simultaneously. Shared context and standardized memory layers allow human insight and machine intelligence to collaborate on the same datasets. A data scientist can run exploratory analysis while an AI agent simultaneously retrieves context for a different use case, all governed by the same policy layer. This symbiosis accelerates discovery and decision-making while maintaining full security and auditability.

Production-Grade AI Workflow Automation

Organizations building production AI systems that require continuous data access across multiple systems can use HyperLake as the operational backbone. The platform supports workflow systems, MCP tools, and future production-ready agentic use cases. AI agents can call tools, trigger workflows, generate artifacts, and operate across different cloud services and open-source technologies, all within a unified governance and audit framework. This eliminates the need to rebuild the operating layer for each new AI use case.

Compliance and Audit-Intensive AI Deployments

For industries like finance, healthcare, and government where every AI decision must be explainable and auditable, HyperLake provides the necessary infrastructure. Every agent action, inference, and query is recorded through immutable provenance logs. Organizations can trace any AI decision back to its source data with complete auditability. Column masking and row-level security ensure that sensitive data like PII, financial records, or classified information is never exposed to unauthorized agents or humans.

How does HyperLake differ from traditional data platforms like Snowflake or Databricks?

Traditional data platforms were designed for humans running dashboards and scheduled queries. They charge significant compute markups that become exponentially expensive when AI agents generate thousands of queries per day. HyperLake is architected specifically for AI agent workloads, with a $0 compute markup model, sovereign deployment inside your own cloud, and a global governance layer that evaluates every request from both humans and agents in real time. It is built for the agentic era, not retrofitted for it.

Does HyperLake require moving my data to a new location?

No. HyperLake deploys 100% inside your own VPC, private cloud, or on-prem environment. Your data never leaves your secure infrastructure. Agents operate on data where it lives, and the platform provides the governance, audit, and orchestration layer on top. This is a fundamental architectural difference from competitors that require data to be copied or moved to their own servers for processing.

How does HyperLake prevent runaway AI agent costs?

HyperLake eliminates the compute markup entirely. You pay only your cloud provider for the underlying compute resources. This means that even if a misconfigured agent generates thousands of queries in minutes, you are not hit with unexpected five-figure bills from the platform itself. Combined with the governance layer that can enforce query limits, access controls, and real-time monitoring, HyperLake gives organizations full control over both security and cost.

Can HyperLake manage infrastructure from multiple cloud providers?

Yes. HyperLake is designed to manage many agentic infrastructure stacks simultaneously. This includes HyperLake-native stacks, customer-owned cloud services, AWS/GCP/Azure-native components, open-source technologies, governed data services, workflow systems, and MCP tools. Enterprises can choose the stack that best fits each use case and deploy it where their data lives, all managed from a single command center.