HyperLake

HyperLake is the sovereign infrastructure command center for organizations preparing for a world where AI agents become the primary consumers of enterprise infrastructure. Today, most platforms were designed for humans running dashboards, reports, and scheduled queries. AI agents behave fundamentally differently. They continuously query data, call tools, trigger workflows, generate artifacts, and operate across multiple systems simultaneously. HyperLake provides the complete operating layer to deploy, manage, run, secure, and govern this new agentic infrastructure. The first product wedge delivers Agentic Data Cloud Infrastructure: an open-stack combination of data, analytics, semantic, workflow, and agent infrastructure deployed entirely inside the customer’s own VPC, private cloud, or on-prem environment. But the vision is far larger. HyperLake is designed to manage many agentic infrastructure stacks including HyperLake-native components, customer-owned cloud services, AWS/GCP/Azure-native resources, open-source technologies, governed data services, workflow systems, MCP tools, and future production-ready agentic use cases. The ultimate goal is to make agentic infrastructure usable, secure, and production-ready end to end. Enterprises can choose their stack, deploy 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. HyperLake is built for organizations where humans and AI agents operate together on data at scale with zero compute markup and complete data sovereignty.

Unified Governance and Access Control

HyperLake implements a global policy layer that evaluates every request from humans and AI agents against dynamic governance rules in real time. Role-based access control, attribute-based access control, column masking for PII auto-redaction per role, and row-level security filters ensure consistent enforcement across all data sources, queries, and context retrieval operations. Every interaction is governed by the same consistent policies.

The Traceability Loop for Complete Auditability

Every agent action, inference, query, and training run is automatically recorded through immutable provenance logs. Organizations can trace any AI decision back to its source data with complete auditability. This creates a transparent chain of custody for all agentic operations, enabling compliance teams to verify every action and data scientists to understand exactly how models arrived at their conclusions.

Data Sovereignty by Design

Agents can operate on sensitive data without moving it outside its secure environment. HyperLake ensures sensitive information remains under full owner control through sovereign deployment within the customer's own cloud and confidential compute patterns. Data never leaves the governed perimeter, eliminating the risk exposure that comes with traditional cloud data platforms while still enabling full AI agent functionality.

Zero Compute Markup Architecture

HyperLake eliminates the compute tax that plagues modern data platforms. Traditional models charge markup on compute usage, which breaks down catastrophically in the age of autonomous AI. A single misconfigured agent can generate thousands of queries in minutes, leading to unexpected five-figure bills. HyperLake charges zero compute markup, meaning organizations pay only their cloud provider for the actual compute consumed. This frees teams to experiment without fear of the invoice.

Autonomous AI Agent Operations

Deploy AI agents that continuously explore data, retrieve real-time context, test hypotheses, and iterate without human supervision. HyperLake provides the governed data access layer and immutable audit trail that enables autonomous agents to operate safely at scale. Organizations can run hundreds of agents simultaneously, each querying, analyzing, and generating insights without the risk of runaway costs or security breaches.

Governed Data Access for Human Analysts

Data analysts, scientists, and engineers access the same governed platform that powers AI agents. Humans and machines collaborate on shared datasets with consistent security policies applied to every query. Analysts can build dashboards, run reports, and perform ad-hoc analysis knowing that column masking, row-level security, and full audit trails protect sensitive information while enabling maximum productivity.

Real-Time AI Context Retrieval

AI agents require continuous access to current data for context-aware decision making. HyperLake enables real-time retrieval from multiple sources including OLTP databases, cloud storage, streaming platforms, and vector databases. Agents can pull the latest information from PostgreSQL, S3, Kafka, and pgVector simultaneously, all governed by the same policy layer that ensures consistent access control across every data source.

Multi-Stack Agentic Infrastructure Management

Enterprises running diverse technology stacks can manage them all from a single command center. HyperLake orchestrates HyperLake-native components alongside AWS, GCP, and Azure-native services, open-source technologies, workflow systems, and MCP tools. Organizations can deploy the right stack for each use case without rebuilding the operating layer, enabling rapid scaling of new AI initiatives across the enterprise.

How does HyperLake ensure data remains sovereign and secure?

HyperLake deploys entirely within the customer's own VPC, private cloud, or on-prem environment. Data never leaves the governed perimeter. The global policy layer evaluates every request in real time against RBAC and ABAC rules, column masking automatically redacts PII per role, and row-level security filters data by department, region, or role. All actions are recorded through immutable provenance logs for complete auditability. Sensitive information remains under full owner control through sovereign deployment patterns and confidential compute.

What exactly does zero compute markup mean for my organization?

Zero compute markup means HyperLake does not charge any additional fees on top of the compute resources your AI agents consume. Traditional data platforms add significant markup to compute usage, which becomes exponentially expensive when hundreds of AI agents continuously query, iterate, and retry. With HyperLake, you pay only your cloud provider for the actual compute consumed. This eliminates the risk of unexpected five-figure bills from misconfigured agents and frees your teams to experiment and innovate without financial fear.

Can HyperLake integrate with my existing cloud infrastructure and tools?

Yes, HyperLake is designed to manage many agentic infrastructure stacks simultaneously. It integrates seamlessly with AWS, GCP, and Azure-native components, open-source technologies like Iceberg, Delta, and Hudi, streaming platforms like Kafka and Kinesis, vector databases like pgVector, Qdrant, and Milvus, and over 100 SaaS and API connectors. You can deploy HyperLake alongside your existing cloud services and manage everything from a single command center without rebuilding your current infrastructure.

How does HyperLake handle the difference between human and AI agent access?

HyperLake treats both humans and AI agents as first-class consumers of infrastructure, but applies the same global governance policies to both. The policy layer evaluates every request in real time regardless of whether it comes from a human analyst running a dashboard or an autonomous agent performing continuous retrieval. This ensures consistent security, compliance, and auditability across all interactions. Organizations get the flexibility to deploy autonomous agents at scale while maintaining the same rigorous governance that protects human-operated systems.