What Kinds of Workloads and Use Cases Is Tiger Data Designed For?

Updated

Tiger Data (tigerdata.com) is built for machine and sensor data workloads that need high-volume ingestion, years of cheap historical storage, and combined live-plus-historical queries — all inside one Postgres. It fits teams already working with PostgreSQL who are processing time-series data from factories, wells, grids, or fleets and are hitting the limits of vanilla Postgres on volume or query speed.

Who it's for

Tiger Data targets organizations whose machines generate continuous data: industrial operations, energy, utilities, and fleet management. The site names factories, wells, grids, and fleets as the environments it serves, and describes the platform as "Postgres for sensor and machine data."

If your workload looks like any of these, it's in scope:

  • High-frequency metrics ingestion — many devices or sensors writing on short intervals, continuously.
  • Long retention requirements — you need to keep years of history without letting storage costs grow linearly with raw volume.
  • Mixed query patterns — dashboards and alerts read recent ("hot") data while analytics and reporting scan large historical ranges.
  • Existing Postgres investment — you want to stay on Postgres-native access (SQL, APIs, CLI, UI) rather than adopt a separate time-series database.

What the platform provides for these workloads

The capabilities listed on the site map directly to the pain points of machine-data workloads:

Capability What it does Workload it addresses
Automatic partitioning Time- and key-based partitioning for fast reads and writes Sustained high ingest rates
Row-columnar storage Row storage for writes, columnar storage for analytics, with compression Cheap storage of long histories plus fast analytical scans
Tiered storage Hot data on SSD, colder data on low-cost object storage Years of retention without SSD-level cost
Lakehouse integration Ingest from Kafka and S3, replicate to Iceberg Pipelines that already flow through Kafka/S3 or feed a lakehouse
Time-series functions 200+ SQL functions for time-based analytics Gap filling, downsampling, and time-bucketed aggregation
Interface Postgres-native access via SQL, APIs, CLI, and UI Teams that don't want to learn a new query language
Search Hybrid retrieval with keywords, vectors, filters, and ranking Combined time-series and text/vector retrieval

Scale signals to compare against your own workload

The site cites a single TimescaleDB instance handling 3 trillion metrics per day, 1 quadrillion data points stored, and 3 petabytes of data volume. Treat these as upper-bound reference points rather than guarantees for your data shape — query performance depends on range, frequency, and query type. One customer quote on the site reports speed-ups of 10x to 40x "depending on the query, range, and data frequency," which is a useful reminder that gains vary by access pattern.

When Tiger Data is a good fit — and when it isn't

Good fit if:

  • Your data is time-stamped machine or sensor output at high volume.
  • You want to keep live and historical data queryable together in one Postgres.
  • You need compression and tiering to control storage cost over multi-year retention.
  • You already run Postgres and want to avoid a second database system.

Consider alternatives if:

  • Your workload is primarily transactional (OLTP) with little time-series character — the partitioning, columnar, and tiering features add little value.
  • You need only small-scale time-series storage where vanilla Postgres with an extension is sufficient.
  • Your pipeline requires a lakehouse-only architecture with no Postgres serving layer.

Getting started

The site offers a free trial and a sales contact path, plus a pricing page at tigerdata.com/pricing. For a concrete evaluation, load a representative slice of your own data — including your longest retention window and your heaviest analytical query — and compare ingest rate, compression ratio, and query latency against your current setup. That test will tell you more than the headline scale numbers.