Timescale, long known for pioneering time-series databases built on PostgreSQL, is now TigerData, a rebrand that reflects the company’s expanded vision and platform. With over 2,000 customers and 3 million active databases, the new name marks a shift from a niche time-series focus to a full-fledged platform powering real-time, analytical, and agentic applications.
“Modern applications don’t fit neatly into traditional database categories,” said Ajay Kulkarni, Co-founder and CEO. “They capture vast streams of data, power real-time analytics, and increasingly rely on intelligent agents that reason and act. These workloads—transactional, analytic, and agentic—require a new kind of operational database. That’s exactly what we’ve built at TigerData: a system that delivers speed without sacrifice.”
Building on PostgreSQL Without Forking It
PostgreSQL has become the go-to operational database for developers. But as real-time requirements intensify, its out-of-the-box capabilities often fall short. TigerData’s innovation lies in enhancing PostgreSQL without forking it, preserving full SQL compatibility while layering in advanced performance features.
“We began by extending PostgreSQL through open source, with TimescaleDB enabling high-ingest, time-series and real-time analytical applications,” the company noted. “That work laid the foundation for a fully cloud-native PostgreSQL platform—Tiger Cloud—featuring horizontally scalable reads, compression at 100+ petabyte scale, hot/cold data tiering, and deep observability.”
TigerData’s unique features include:
- Hypertables for automatic time-based partitioning
- Continuous Aggregates for always-fresh materialized views
- Hypercore, a hybrid row-columnar engine for high-speed analytics
- Streaming vector search (DiskANN and HNSW)
- SQL-native embedding pipelines with freshness guarantees
“These aren’t experimental features,” the company emphasized. “They’re running in production at global scale—today.”
Deployed at the Heart of Real-Time Systems
TigerData has become the engine behind critical systems in automotive, finance, AI, and industrial IoT.
Lucid Motors uses the platform for ingesting high-volume telemetry and autonomous driving data. TigerData has become the foundation of their next-generation data infrastructure, providing a unified platform to ingest high-volume vehicle telemetry, generate AI-ready embeddings from video, and run real-time, context-rich search, all in one system.
Other high-profile adopters include Hugging Face, Mistral, Barclays, Schneider Electric, and even the European Space Agency.
A Unified Architecture for a Fragmented Landscape
In addition to rethinking PostgreSQL for real-time use, TigerData is working to unify two historically separate data domains: operational and analytical. Its approach centers on continuous, high-throughput sync between fast-moving application data and lakehouse-style storage, making both accessible via a single PostgreSQL interface.
Looking ahead, TigerData is also building:
- A new storage engine with compute-local caching and zero-copy branching
- A concept it calls Agentic PostgreSQL, where memory, reasoning, and retrieval are built in from the core, not as external tools
Not Just a New Name—A New Direction
The name TigerData reflects the company’s evolution from a time-series specialist to a platform redefining what’s possible with PostgreSQL in the age of AI, automation, and real-time responsiveness.
“Speed, flexibility, and simplicity—delivered together, on a foundation they already trust: PostgreSQL,” said Kulkarni.
For organizations building the next generation of data infrastructure, TigerData represents more than a renamed product. It’s a redefinition of what a modern database can do.
