HyperLake

HyperLake is a sovereign infrastructure platform built for the era where AI agents are primary consumers of enterprise data and compute resources. Unlike traditional data platforms designed for human-centric workflows like dashboards, reports, and scheduled queries, HyperLake provides a command center to deploy, manage, run, secure, and govern agentic infrastructure end to end. It is built for organizations that need to support autonomous AI agents that continuously query data, call tools, trigger workflows, generate artifacts, and operate across multiple systems requiring governed access to compute, data, policies, and services. The product's first wedge is Agentic Data Cloud Infrastructure: an open-stack data, analytics, semantic, workflow, and agent infrastructure deployed inside the customer's own VPC, private cloud, or on-prem environment. The broader vision extends beyond a single stack to manage many agentic infrastructure stacks including HyperLake-native components, customer-owned cloud services, AWS/GCP/Azure-native tools, open-source technologies, governed data services, workflow systems, MCP tools, and future production-ready agentic use cases. HyperLake eliminates the compute markup problem common in modern data platforms where a single misconfigured agent can generate thousands of queries leading to unexpected five-figure bills. With $0 compute markup, organizations pay only their cloud provider. The platform is IaaC and GitOps-managed, sovereign by default, and designed for mobile-first, app-centric user experiences that make agentic infrastructure usable, secure, and production-ready. It enables enterprises to choose their stack, deploy where data lives, govern every human and agent interaction, audit every action, and scale new AI use cases without rebuilding the operating layer each time.

Unified Governance and Access

HyperLake provides a global policy layer that evaluates every request whether from a human or an AI agent against dynamic governance rules in real time. This feature ensures consistent access enforcement across data sources, queries, and context retrieval. Role-based access control (RBAC), attribute-based access control (ABAC), column masking for PII auto-redaction per role, row-level security filters by department, region, or role, and an immutable audit trail that version-tracks every action are all built into the platform. This governance engine operates as a single pane of glass for all data interactions.

The Traceability Loop

Every agent action, inference, query, and training run is recorded through immutable provenance logs. This feature creates a complete audit trail that allows organizations to trace any AI decision back to its source data with full auditability. The traceability loop is critical for compliance, debugging, and understanding how AI agents interact with enterprise data. It provides mobile-first visibility into agent behavior, enabling users to monitor and review actions from anywhere via app-centric dashboards.

Data Sovereignty by Design

HyperLake enables agents to operate on data without moving it outside its secure environment. Sensitive information remains under full owner control through sovereign deployment and confidential compute patterns. The platform deploys 100% in the customer's cloud including AWS, GCP, or Azure, or on-premises, ensuring data never leaves the governed perimeter. This design is essential for organizations with strict data residency, regulatory, or security requirements, allowing them to leverage AI agents without compromising data control.

Human-Agent Symbiosis

HyperLake allows humans and AI agents to operate on the same governed data platform with shared context and standardized memory layers. This feature enables human insight and machine intelligence to collaborate on the same datasets seamlessly. Analysts, data scientists, and engineers can work alongside autonomous agents using the same governed access policies, audit trails, and data sources. The mobile-first user experience ensures that both human and agent interactions are visible, manageable, and auditable from any device.

Autonomous AI Agent Operations

Organizations deploying AI agents that continuously retrieve context, test hypotheses, and iterate can use HyperLake as the governed system of access. Agents query data, call tools, trigger workflows, and generate artifacts while the platform enforces policies, records provenance, and prevents runaway compute costs. This use case is critical for enterprises building agentic systems that need reliable, secure, and auditable infrastructure without fear of unexpected bills or data exposure.

Governed Data Access for AI and ML Teams

Data scientists and ML engineers can access governed data sources through HyperLake's unified data layer without needing separate approvals for each query. The platform handles RBAC, column masking, and row-level security automatically, enabling teams to focus on model development and insights. This use case accelerates AI and ML workflows while maintaining compliance and security standards across the organization.

Real-Time Analytics and Autonomous Pipelines

HyperLake supports real-time analytics and autonomous data pipelines where AI agents trigger workflows based on incoming data from streaming sources like Kafka, Kinesis, or SaaS APIs. The platform's governance engine ensures that every pipeline step is audited and policy-compliant. This use case is ideal for organizations needing to react to data in real time with AI-driven decision-making while maintaining full control and traceability.

Multi-Cloud and Hybrid Data Federation

Enterprises with data spread across multiple cloud providers, on-premises environments, and SaaS platforms can use HyperLake to federate access without moving data. The platform connects to OLTP databases, cloud storage, open formats like Iceberg and Delta, vector databases, and 100+ connectors. This use case enables a single governed interface for humans and AI agents to interact with all enterprise data, regardless of where it resides.

What is HyperLake and how does it differ from traditional data platforms?

HyperLake is a sovereign infrastructure platform designed specifically for AI agents as primary consumers. Unlike traditional data platforms built for humans running dashboards and scheduled queries, HyperLake provides a command center for deploying, managing, and governing agentic infrastructure. It eliminates compute markup, enforces unified governance across all interactions, and deploys 100% in your cloud or on-premises. Traditional platforms charge markup on compute usage, which can lead to unexpected costs when AI agents generate thousands of queries. HyperLake charges $0 compute markup, so you only pay your cloud provider.

How does HyperLake ensure data sovereignty and security?

HyperLake is designed with data sovereignty as a core principle. The platform deploys inside the customer's own VPC, private cloud, or on-prem environment, ensuring data never leaves the governed perimeter. It uses confidential compute patterns, column masking for PII auto-redaction, row-level security filters, and immutable audit trails. Every action by humans or AI agents is recorded through provenance logs, enabling full traceability. This design ensures sensitive information remains under full owner control while allowing agents to operate on data securely.

Can I use HyperLake with my existing cloud services and tools?

Yes, HyperLake is designed to manage many agentic infrastructure stacks including your existing cloud services. It integrates with AWS, GCP, and Azure-native components, open-source technologies, governed data services, workflow systems, MCP tools, and more. The platform supports connection to OLTP databases like PostgreSQL and MySQL, cloud storage like S3 and GCS, open formats like Iceberg and Delta, streaming sources like Kafka and Kinesis, and 100+ SaaS and API connectors. You can choose the stack that fits your needs and deploy it where your data lives.

What happens if an AI agent generates excessive queries or costs?

HyperLake eliminates the compute markup problem entirely. Traditional platforms charge markup on compute usage, which can lead to unexpected five-figure bills when a single misconfigured agent generates thousands of queries. HyperLake charges $0 compute markup, meaning you only pay your cloud provider for the underlying compute resources. Additionally, the platform's governance engine can enforce rate limiting, cost controls, and policy-based restrictions on agent behavior. The traceability loop provides full visibility into agent actions, enabling organizations to monitor and control usage proactively.