Timescale (now Tiger Data)

Timescale, now rebranded as Tiger Data, is a New York-based database infrastructure company founded in 2015 by Ajay Kulkarni and Mike Freedman (CTO and Princeton professor).

Updated 6 Jun 2026 Reviewed by 7wData

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PostgreSQL-native database platform optimized for time-series, vector, and event data at petabyte scale, available as cloud-managed service or self-managed enterprise deployment.

Timescale, now rebranded as Tiger Data, is a New York-based database infrastructure company founded in 2015 by Ajay Kulkarni and Mike Freedman (CTO and Princeton professor). The company provides TimescaleDB, a PostgreSQL extension engineered to handle time-series data, vectors, events, and analytics workloads at scale. In February 2022, Timescale raised $110 million in Series C funding led by Tiger Global, achieving a $1 billion valuation and bringing total funding to $181 million.

The company rebranded to Tiger Data in June 2025 to reflect its evolution beyond time-series specialization into a comprehensive PostgreSQL platform serving 2,000+ customers with eight-figure annual recurring revenue and year-over-year growth exceeding 100%. In April 2026, Tiger Data launched TimescaleDB Enterprise, a commercially licensed, self-managed offering for on-premises and edge deployment targeting regulated industries like manufacturing, oil and gas, and utilities. The company also expanded into vector and AI capabilities through pgvectorscale and pgai extensions, supporting developers building retrieval-augmented generation and semantic search applications.

Tiger Data has demonstrated significant momentum in the industrial data space, announced a strategic partnership with Inductive Automation (maker of Ignition, used by 69% of Fortune 100 companies) to modernize legacy historian databases. However, the company has faced workforce instability, with Glassdoor reviews documenting multiple layoffs since 2023, followed by reassurances and renewed cuts, creating recurring morale challenges among remaining staff.

Who buys this

Manufacturing and industrial operations requiring on-premises time-series infrastructure for sensor and equipment telemetry
Oil, gas, and utilities companies with regulatory or data-sovereignty mandates preventing cloud adoption
SaaS and fintech companies running real-time analytics on high-volume transactional and event data
Enterprise software vendors building embedding search and RAG features into applications
Telecommunications and media companies processing network metrics and telemetry at scale

Publicly disclosed clients

Speedcast (satellite communications)
Flowco / Flogistix (oil and gas vapor recovery)
Axpo (energy trading and infrastructure)
Trading Strategy (algorithmic trading)
Inductive Automation (through partnership, serves 69% of Fortune 100)

Strengths and what to watch

Strengths

Broad SQL compatibility built on PostgreSQL, avoiding vendor lock-in and leveraging existing developer skills and tooling
Proven ability to scale to trillions of metrics daily with automatic partitioning, compression, and hybrid row-columnar storage
Strategic expansion into vector and AI primitives (pgvectorscale, pgai) positioning the platform for modern LLM-driven applications

Watch for

Recurring workforce instability: multiple layoffs since 2023 followed by rehiring cycles, documented employee concerns about strategic clarity and morale on Glassdoor
Execution risk on TimescaleDB Enterprise on-premises variant: general availability deferred to later 2026, targeting smaller addressable market than cloud SaaS
Competitive intensity from ClickHouse, QuestDB, and InfluxDB in time-series, plus PostgreSQL community-driven pgvector alternatives in vector search

Recent moves

Key Information

Industry: Databases
Founded: 2015
Headquarters: New York, Country

Frequently Asked Questions

What is Tiger Data?

Tiger Data, formerly Timescale, is a PostgreSQL-native database platform optimized for time-series, vector, and event data at petabyte scale. Founded in 2015 and rebranded June 2025, it serves 2,000+ customers with self-managed and cloud deployment options for handling trillions of metrics daily.

What is Tiger Data used for?

Tiger Data serves manufacturing, oil and gas, utilities, fintech, and SaaS companies needing real-time analytics on high-volume transactional data. The platform handles sensor telemetry, equipment monitoring, and event processing for on-premises and cloud deployment. It's also used for retrieval-augmented generation and semantic search through vector and AI extensions.

How does Tiger Data compare to competitors like ClickHouse?

Tiger Data runs on PostgreSQL, avoiding vendor lock-in. Developers can use existing SQL knowledge and tools. It competes with ClickHouse, QuestDB, and InfluxDB in time-series databases. Differentiation comes from pgvectorscale and pgai extensions enabling AI, RAG, and semantic search capabilities traditional time-series alternatives lack.

Is Timescale open source?

TimescaleDB is a PostgreSQL extension built on PostgreSQL ecosystem, providing broad SQL compatibility and avoiding vendor lock-in. TimescaleDB Enterprise, launched April 2026, is the commercially licensed edition for on-premises and edge deployment targeting regulated industries like manufacturing, oil, gas, and utilities.

What are pgvectorscale and pgai?

pgvectorscale and pgai are PostgreSQL extensions developed by Tiger Data enabling vector and AI capabilities. pgvectorscale powers semantic search and vector similarity queries; pgai provides AI primitives for building retrieval-augmented generation and LLM-driven applications. Both integrate seamlessly with TimescaleDB for enterprises modernizing analytics and embedding search features.

When did Timescale rebrand to Tiger Data?

Timescale rebranded to Tiger Data in June 2025 to reflect evolution beyond time-series into a comprehensive PostgreSQL platform. The rebranding coincided with expansion into vector and AI capabilities. The company now serves 2,000+ customers with eight-figure annual recurring revenue and year-over-year growth exceeding 100 percent globally.

How Timescale (now Tiger Data) compares

Direct head-to-head against 3 competitors. Picked by 7wData.

Positioning: PostgreSQL-native database platform optimized for time-series, vector, and event data at petabyte scale, available as cloud-managed service or self-managed enterprise deployment.
Customer segments: Manufacturing and industrial operations requiring on-premises time-series infrastructure for sensor and equipment telemetry
Strengths: Broad SQL compatibility built on PostgreSQL, avoiding vendor lock-in and leveraging existing developer skills and tooling
Watch for: Recurring workforce instability: multiple layoffs since 2023 followed by rehiring cycles, documented employee concerns about strategic clarity and morale on Glassdoor
Recent moves: Tiger Data launches TimescaleDB Enterprise for on-premises and edge deployment, announced at Hannover Messe with partnerships including Inductive Automation

Positioning: Purpose-built time-series platform for industrial IoT, DevOps, and observability workloads across cloud and on-premises.
Customer segments: Industrial IoT operators, telecom teams, DevOps engineers, and fintech firms running high-throughput time-series pipelines.
Strengths: InfluxDB 3.0 columnar storage on Apache Arrow and Parquet delivers SQL performance on time-series without schema overhead.
Watch for: Three incompatible major versions (v1, v2, v3) force costly migrations; community forums document upgrade complexity as primary buyer friction.
Recent moves: InfluxDB 3 Core reached general availability April 2025; added as managed service on Amazon Timestream, October 2025.

Positioning: Real-time column-oriented analytics database targeting sub-second OLAP at petabyte scale for AI and data teams.
Customer segments: Ad-tech, fintech, and AI infrastructure teams needing sub-second real-time aggregation on billions of event rows.
Strengths: Fastest real-time column scanning across billions of rows; consistently top-ranked on public OLAP benchmarks for sub-second aggregation.
Watch for: Rapid expansion into Postgres and LLM observability after January 2026 Series D risks roadmap dilution; enterprise scope breadth concerns buyers.
Recent moves: Raised $400M Series D led by Dragoneer at $15B valuation, acquired Langfuse, launched native Postgres service, January 2026.

Positioning: Open-source SQL time-series database targeting capital markets and high-frequency industrial workloads, known for sub-millisecond write throughput.
Customer segments: Capital markets firms, financial exchanges, and fintech companies running high-frequency data pipelines or market data infrastructure.
Strengths: Sub-millisecond ingestion for tick data; named Best Trading Analytics Platform at TradingTech Insight Awards Europe 2026.
Watch for: Total funding under $20M limits enterprise support depth; small team raises vendor-risk concerns for regulated-industry and enterprise buyers.
Recent moves: Launched partnership with B3 Exchange to power next-generation central securities depository platform, April 2025.

Sources

www.tigerdata.com — Company overview, product capabilities, customer impact metrics, current positioning as Tiger Data
www.businesswire.com — Series C funding amount ($110M), date (February 22, 2022), valuation ($1B+), investor names, revenue growth metrics
www.tigerdata.com — Series C announcement details, growth metrics (7x community, 20x revenue), founding year (2015), founder names
www.tigerdata.com — Rebranding to Tiger Data in June 2025, current metrics (2,000 customers, mid 8-digit ARR, >100% YoY growth), company evolution narrative
news.ycombinator.com — Community sentiment on rebranding and company evolution
www.globenewswire.com — TimescaleDB Enterprise launch (April 20, 2026), features, target industries, availability timeline, Hannover Messe announcement venue
inductiveautomation.com — Inductive Automation partnership (April 2026), Gold Technology Provider status, Fortune 100 adoption of Ignition
www.prnewswire.com — PopSQL acquisition (April 2024), developer tools expansion
www.tigerdata.com — Named customer case studies and customer segments
www.crunchbase.com — Funding rounds, investor names, company profile validation