Tiger Data and AWS Sign Strategic Collaboration Agreement to Advance Unified Postgres Infrastructure

As enterprises contend with rising data volumes from connected devices, software applications, and emerging AI-driven systems, the limitations of fragmented data architectures have become increasingly apparent. Organizations are looking for ways to consolidate operational data, streamline analytics workflows, and support the next generation of AI agents without abandoning the tools and ecosystems developers already rely on.

Tiger Data, the company behind TimescaleDB and Agentic Postgres, announced a strategic collaboration agreement (SCA) with Amazon Web Services (AWS). The agreement aims to accelerate the delivery of modern data infrastructure on Postgres, integrating key AWS analytics and AI services while providing a unified platform for developers, devices, and autonomous agents.

The partnership follows growing demand for infrastructure that can ingest high-volume time-series and event data, support real-time analytics, and enable safe experimentation for AI systems. According to Tiger Data, the collaboration will deepen technical integrations, expand go-to-market efforts, and bring new joint solutions to the AWS Marketplace.

"The future of data infrastructure isn't about specialized systems for every workload," said Ajay Kulkarni, CEO and co-founder of Tiger Data. "It's about a unified infrastructure that handles what developers build, what devices generate, and what agents need to operate---all on Postgres. With AWS, we're making that architecture real, with integrations that connect Postgres to the full AWS stack, and the performance that makes it production-ready at scale."

Integrations Strengthened Across AWS Analytics and AI Services

Under the agreement, Tiger Data and AWS will expand existing service integrations that already connect Postgres-based workloads to Amazon Athena, Amazon Redshift, Amazon QuickSight, and Amazon SageMaker. These integrations allow developers to access operational data and lakehouse analytics through a unified SQL interface, eliminating the need to move between multiple tools or environments.

The SCA also includes plans to broaden AWS Marketplace availability and invest jointly in customer success programs and technical enablement, helping enterprises adopt unified data architectures more quickly. Additional integrations are already underway, building on Tiger Data's recent support for streaming Postgres data to Apache Iceberg on Amazon S3, achieving near real-time synchronization using SQL and open standards.

Infrastructure Designed for AI Agents

As companies deploy more AI agents capable of autonomous testing, workflow execution, and decision-making, new infrastructure demands have emerged. Tiger Data's Agentic Postgres introduces database primitives built specifically for this use case. The platform allows instant, zero-copy environments that agents can fork and run in parallel, enabling safe experimentation without affecting production systems.

This feature set is expected to be central to future AI development strategies, particularly for enterprises seeking to scale agent-driven operations while maintaining tight governance over data and workflows.

Operational Data at Scale

Tiger Data's core platform is engineered for workloads requiring high throughput and low latency. Postgres has been enhanced to support billions of writes, time-series ingest, automatic compression, and rapid queries, capabilities that have made it a popular choice for IoT, Web3, and AI workloads.

Through AWS, Tiger Data customers can extend these capabilities with cloud-native analytics and machine learning services. Tiger Cloud, now available on AWS Marketplace, powers more than 2,000 production deployments across industries ranging from manufacturing to AI research. Users can learn more or create an account directly through Tiger Cloud.

A Consolidation Moment for Enterprise Data

The Tiger Data-AWS collaboration marks a significant moment for enterprises seeking to simplify data infrastructure while preparing for the rise of AI agents. By unifying developers, devices, and autonomous systems on a Postgres-based architecture tightly integrated with AWS analytics and AI, the companies aim to offer an alternative to the increasingly complex mix of specialized systems many organizations currently maintain.

As data demands continue to rise, the partnership provides a clearer path toward scalable, cohesive, and cloud-native infrastructure built for both the operational realities of today and the AI-driven workloads of the near future.