In a world where data moves faster than ever, the name “Timescale” has become synonymous with time-series performance and real-time analytics. But today, that name evolves. Timescale is now TigerData, and with it comes a bold mission: to redefine PostgreSQL for the future of intelligent, data-intensive applications.
More than a rebrand, this transformation signals the arrival of what TigerData calls the fastest PostgreSQL platform, built for an era where transactional, analytical, and agentic workloads converge. Whether it’s real-time analytics, streaming vector search, or powering AI agents, TigerData is positioning itself as the next standard for developers building production-grade systems on PostgreSQL.
From Time-Series to AI Infrastructure
At its core, TigerData builds on the world’s most trusted open-source database, PostgreSQL, but reengineered for scale, speed, and modern flexibility. What started with TimescaleDB, the open-source extension that made time-series in PostgreSQL seamless, has evolved into a full cloud-native stack: Tiger Cloud.
Tiger Cloud supports workloads at a petabyte scale and production velocity, featuring horizontally scalable reads, time-based partitioning (via Hypertables), always-fresh materialized views (via Continuous Aggregates), and compression strategies optimized for environments exceeding 100 petabytes. This scalability ensures that TigerData can handle even the most demanding workloads.
But the most significant change? TigerData is now agent-ready. The platform includes native vector search via Streaming DiskANN and HNSW, SQL pipelines for embedding generation with freshness guarantees, and a hybrid engine (Hypercore) that merges row and columnar performance. These features enable the rapid development of production-ready AI systems directly within PostgreSQL.
“These workloads require a new kind of operational database,” said CEO and co-founder Ajay Kulkarni. “That’s exactly what we’ve built at TigerData: a system that delivers speed without sacrifice.”
Used by the Builders of Tomorrow
With over 3 million active databases and 2,000+ customers across more than 25 countries, TigerData isn’t just promising performance; it’s delivering it at massive scale, powering mission-critical infrastructure in industries ranging from autonomous vehicles to financial services.
Lucid Motors, for example, utilizes TigerData to ingest high-volume vehicle telemetry, generate embeddings from in-car video, and run real-time, context-rich search, all within a single unified system. Hugging Face and Mistral leverage TigerData’s vector infrastructure to build AI agents, while The Financial Times and Barclays rely on the platform for low-latency semantic search.
What’s Next: Agentic PostgreSQL
Looking ahead, TigerData is preparing to push PostgreSQL even further into uncharted territory.
On the near horizon: a new high-performance storage engine with compute-local caching, disaggregated replicas, and zero-copy branching. This is designed to support the most demanding data workloads, particularly in AI, where fast ingest and replay are key.
However, the real moonshot is what TigerData is calling Agentic PostgreSQL: a next-generation data infrastructure designed for both humans and intelligent agents. Memory, retrieval, and reasoning won’t just be bolted on. They’ll be core components of the database itself.
Why It Matters
In a landscape crowded with one-trick databases and purpose-built data stacks, TigerData is betting big on something familiar: PostgreSQL. But with performance enhancements, agent-native features, and real-world reliability, the company is making a compelling case that the world’s most popular open-source database can also be the most powerful.
TigerData isn’t just building another database. It’s redefining what operational data infrastructure should be: fast, flexible, and future-ready.
Explore more at www.tigerdata.com.
