HyperLake

HyperLake is the command center for the agentic era, purpose-built for organizations preparing for a world where AI agents become the primary users of infrastructure. Traditional enterprise infrastructure was designed for humans: dashboards, reports, and scheduled pipelines. AI agents behave differently. They query data continuously, call tools, trigger workflows, generate artifacts, and operate across multiple systems simultaneously. They need constant access to governed compute, data, policies, and services. HyperLake provides the sovereign infrastructure to deploy, manage, run, secure, and govern that entire agentic ecosystem. The product starts with Agentic Data Cloud Infrastructure: an open-stack combination of data, analytics, semantic, workflow, and agent infrastructure deployed directly inside your own VPC, private cloud, or on-prem environment. But the vision is much larger. HyperLake is designed 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, MCP tools, and future production-ready agentic use cases. The core value proposition is making agentic infrastructure usable, secure, and production-ready end to end. Enterprises can choose their 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. With $0 compute markup, 100% deployment in your cloud, and governance built into the design, HyperLake eliminates the fear of runaway costs and gives AI agents the freedom to experiment and iterate at scale.

Unified Governance and Access

A global policy layer that evaluates every request, whether from a human or an AI agent, against dynamic governance rules in real time. Access is enforced consistently across all data sources, queries, and context retrieval operations. This means organizations can define role-based and attribute-based access controls, column masking for PII auto-redaction, row-level security filters, and a complete audit trail for every action. No matter how many agents are operating simultaneously, every interaction is governed by the same consistent policies.

The Traceability Loop

Every single agent action, inference, query, and training run is recorded through immutable provenance logs. This creates a complete, auditable chain from any AI decision back to its source data. Organizations can trace exactly what data was used, when it was accessed, and by which agent or human. This feature is critical for compliance, debugging, and building trust in autonomous systems. It turns agentic operations from a black box into a fully transparent, verifiable system.

Data Sovereignty by Design

Agents can operate on data without ever moving it outside its secure environment. Sensitive information remains under full owner control through sovereign deployment and confidential compute patterns. HyperLake deploys 100% inside your own cloud, VPC, or on-prem infrastructure. Data never leaves your controlled environment. This is essential for regulated industries, enterprises with strict data residency requirements, and any organization that cannot afford to risk data exposure to third-party infrastructure.

Human-Agent Symbiosis

Humans and AI agents operate on the same governed data platform with shared context and standardized memory layers. This allows human insight and machine intelligence to collaborate seamlessly on the same datasets. Analysts, scientists, and engineers work alongside autonomous and supervised AI agents, all accessing the same governed data sources. The result is a unified operational environment where human expertise and AI speed combine to drive faster, more accurate outcomes.

Autonomous AI Agent Operations

Deploy AI agents that continuously explore, retrieve context, test hypotheses, and iterate on data without human intervention. HyperLake provides the governed data and context runtime these agents need to operate at scale. Agents can query multiple data sources, trigger workflows, generate artifacts, and call tools across systems, all while being tracked by immutable provenance logs and governed by consistent policies. This use case is ideal for organizations building autonomous research, analysis, or operational agents.

Governed Self-Service Analytics for Humans and Agents

Replace traditional, human-centric analytics platforms with a unified governed layer that serves both human analysts and AI agents. Analysts can run SQL queries, build dashboards, and generate reports while AI agents simultaneously perform autonomous exploration and retrieval on the same datasets. The governance engine ensures that both humans and agents only access data they are authorized to see, with column masking and row-level security applied automatically.

Real-Time Context Retrieval for AI Applications

Provide continuous, governed access to real-time data for AI applications that need up-to-the-minute context. HyperLake connects to streaming sources like Kafka and Kinesis, OLTP databases, cloud storage, and SaaS APIs, making that data available to AI agents and applications in real time. This enables use cases like real-time fraud detection, dynamic pricing, personalized recommendations, and live operational dashboards powered by AI.

Multi-Cloud and Hybrid Agentic Infrastructure Management

Manage agentic infrastructure stacks across multiple cloud providers and on-prem environments from a single command center. HyperLake can orchestrate HyperLake-native stacks alongside customer-owned AWS, GCP, and Azure services, open-source technologies, and governed data services. This allows enterprises to deploy agents where their data lives, whether that is in a private cloud, a specific cloud region, or across multiple providers, without rebuilding the operating layer each time.

What does it mean that HyperLake has $0 compute markup?

HyperLake does not charge any markup on the compute resources used by your AI agents and data workloads. You pay only your cloud provider directly for the underlying compute. This eliminates the risk of unexpected five-figure bills from a single misconfigured agent generating thousands of queries, and it allows organizations to experiment and iterate freely without fear of runaway costs.

How does HyperLake ensure data sovereignty?

HyperLake deploys 100% inside your own cloud environment, VPC, or on-prem infrastructure. Data never leaves your controlled environment. Sensitive information remains under full owner control through sovereign deployment patterns and confidential compute. This is critical for regulated industries and organizations with strict data residency requirements.

Can HyperLake work with my existing cloud services and tools?

Yes. HyperLake is designed to manage many agentic infrastructure stacks, including your existing AWS, GCP, and Azure-native components, open-source technologies, governed data services, workflow systems, and MCP tools. You can choose your preferred stack and deploy it where your data lives, without being locked into a single vendor.

What types of data sources does HyperLake support?

HyperLake supports a wide range of data sources including OLTP and RDBMS systems like PostgreSQL and MySQL, cloud storage like S3, GCS, Azure, and R2, open formats like Iceberg, Delta, and Hudi, streaming platforms like Kafka and Kinesis, over 100 SaaS and API connectors, and vector databases like pgVector, Qdrant, and Milvus.