Timescale, the company behind the popular time-series database built on PostgreSQL, has officially rebranded as TigerData. This move signals its evolution from a niche player into a comprehensive platform tailored for modern, high-performance workloads.
While the company’s roots are in time-series data, its ambitions now go far beyond. TigerData is positioning itself as the foundation for real-time transactional systems, large-scale analytics, and AI agent infrastructure, all built on the familiar and trusted PostgreSQL ecosystem.
PostgreSQL, Supercharged
PostgreSQL has long been the default choice for operational databases, but modern demands have outpaced its native capabilities. TigerData has responded by augmenting PostgreSQL without forking it, delivering a platform that is both scalable and production-ready, yet remains fully compatible with the PostgreSQL standard.
Its cloud-native platform, Tiger Cloud, introduces features typically found in specialized systems, including petabyte-scale compression, hot/cold data tiering, horizontally scalable reads, and rich observability tools. This architecture enables users to power both real-time analytics and traditional transactional workloads within a single database.
TigerData’s features are not just enhancements; they are innovations. Hypertables for time-based partitioning, Continuous Aggregates for always-updated materialized views, and Hypercore, a hybrid row-columnar engine optimized for analytics, are just a few examples. The native support for low-latency vector search, embedding pipelines, and memory-based retrieval systems is crucial for building production-ready AI applications, making TigerData a platform that sparks curiosity and invites exploration.
From Time-Series to Agentic Workloads
The company’s evolution mirrors a broader shift in how developers build applications. Traditional boundaries between transactional, analytical, and intelligent workloads are dissolving. Instead of relying on separate systems for ingestion, analytics, and AI reasoning, developers are increasingly seeking unified platforms that can perform all these tasks in real-time.
This is where TigerData steps in. What began as an open-source extension to enable high ingest time-series data has now grown into an enterprise-grade system capable of powering the next wave of AI-native applications.
With support for vector search algorithms like DiskANN and HNSW, as well as SQL-native embedding pipelines, TigerData is now being utilized for intelligent agent architectures, recommendation systems, and semantic search, extending its applications far beyond its original time-series use case.
Used by Industry Leaders
TigerData is already deployed at scale across industries that rely on real-time responsiveness and massive data throughput. Automotive companies, such as Lucid Motors, utilize it for ingesting vehicle telemetry and performing real-time AI analysis on video streams.
With high-profile users like Hugging Face, Mistral, Barclays, and the European Space Agency, TigerData’s reliability is not just a claim, it’s a proven fact. Its cross-industry relevance and reliability are a testament to its robustness and performance, providing reassurance to potential users about its capabilities.
Bridging Operational and Analytical Worlds
One of TigerData’s most ambitious projects is its unified architecture that connects operational and historical data layers. As data lakehouse models gain popularity, TigerData is building continuous, high-throughput synchronization between real-time systems and longer-term analytical stores, all of which are accessible through a standard PostgreSQL interface.
Looking Forward
TigerData is also developing a next-generation storage engine designed for the most demanding workloads, featuring compute-local caching and disaggregated replicas. And perhaps most notably, it’s laying the groundwork for Agentic PostgreSQL, where memory, retrieval, and reasoning are integrated into the core, positioning the database as a first-class citizen in AI system architecture.
The name may have changed, but the mission is bigger than ever. TigerData is betting that the future of data infrastructure won’t be built around silos but around a fast, unified, AI-ready PostgreSQL.