TigerData (formerly Timescale)

TigerData is a PostgreSQL-native data platform built on TimescaleDB, a widely-deployed open-source extension for time-series and analytical workloads.

Reviewed by 7wData
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TigerData is a PostgreSQL-native data platform built on TimescaleDB, a widely-deployed open-source extension for time-series and analytical workloads. Founded in 2015 and headquartered in New York, the company rebranded in June 2025 to reflect its evolution beyond time-series databases toward a general-purpose PostgreSQL platform serving transactional, analytical, and AI applications. The rebrand was driven by customer reality: the majority of workloads on Tiger Cloud are not time-series—companies use it for entire applications, AI/vector workloads, and real-time analytics at petabyte scale.

TigerData's core strength is automatic time-based partitioning and compression (up to 95%), which reduces storage costs and accelerates analytical queries by 10-100x compared to vanilla PostgreSQL. The platform integrates with Kafka and S3 for streaming data ingestion, supports vector search via pgvectorscale for AI/RAG applications, and offers tiered storage that places hot data on fast SSDs and cold data on cheaper object storage. As of 2026, TigerData operates 3 million databases in production for 2,000+ customers including Mistral, HuggingFace, Nvidia, and JP Morgan Chase.

The open-source TimescaleDB extension remains under Apache-2.0 license (with enterprise features under Timescale License), preserving zero vendor lock-in for the core product. However, the managed Tiger Cloud offering carries usage-based pricing that increases with compute and storage, and users report trade-offs: UI slowness at scale, higher licensing costs than some competitors, and occasional breaking changes between PostgreSQL versions. The community has grown skeptical of marketing claims (particularly positioning against NoSQL), though technical reliability and PostgreSQL compatibility remain highly valued.

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How it works

  1. Automatic time-based partitioning

    Data chunks are automatically split by time intervals and space keys, enabling faster queries and easier data retention policies without manual table management.

  2. Columnar compression and storage

    Achieves up to 95% compression on time-series data using columnar format, significantly reducing storage footprint and query latency for analytical workloads.

  3. Continuous aggregates

    Incrementally maintains materialized views of aggregated time-series data (sums, averages, percentiles) with automatic refresh policies, avoiding expensive full recalculations.

  4. Vector search via pgvectorscale

    Combines pgvector with StreamingDiskANN indexing for semantic similarity search, enabling RAG and AI applications without external vector databases.

  5. Tiered storage

    Automatically moves older, less-frequently-accessed data from fast SSD storage to cost-effective object storage (S3, etc.), balancing performance and expense.

  6. Kafka and S3 connectors

    Built-in connectors stream data from Apache Kafka topics and S3 buckets in real time, with automatic schema handling for CSV and Parquet files.

  7. Live migration with logical decoding

    Migrates 100GB–10TB+ databases from PostgreSQL to Tiger Cloud with minutes of downtime by replicating schema, data, and ongoing transactions.

Strengths and trade-offs

Strengths

  • PostgreSQL compatibility means familiar SQL, existing drivers/tools, and no vendor lock-in for the open-source TimescaleDB extension.
  • Automatic partitioning and compression eliminate manual tuning; 10–100x faster analytical queries than vanilla PostgreSQL with lower storage costs.
  • Dual-license model (Apache-2.0 core + commercial cloud) gives teams the choice to self-host cost-free or pay for managed Tiger Cloud with 24/7 support and compliance.

Trade-offs

  • Managed Tiger Cloud pricing scales quickly with compute and storage; usage-based billing surprises users comparing to fixed-tier competitors like BigQuery.
  • UI becomes sluggish when managing many tables/schemas; live migration does not support mutable columnstore (INSERT/UPDATE/DELETE on compressed data) without recreating data.
  • Breaking changes between PostgreSQL minor versions (e.g., 17.1, 16.5, 15.9) force manual version skips; community skepticism about marketing claims (e.g., 'NoSQL is dead') damages trust.

Pricing context

TigerData offers three tiers: Performance (starting at $30/month, pay-as-you-go for development and moderate workloads), Scale (starting at $36/month, for production applications with HA replicas and advanced features), and Enterprise (custom pricing for mission-critical systems with 24/7 support, disaster recovery, and compliance certifications). Billing combines hourly compute metering with per-GB-per-month storage charges on a usage-based model; tiered storage incurs separate fees. New users receive a $1,000 trial credit and 30-day free trial with no credit card required.

The company launched a free 'Agentic Postgres' tier in October 2025 as part of product-led growth, though feature limits are unspecified. No hidden fees; customers can cancel anytime and adjust compute up or down without service interruption.

Getting started with TigerData (formerly Timescale)

  1. Sign up for Tiger Cloud

    Go to tigerdata.com and click "Start free trial". Enter your email, set a password, and verify your account. You receive a $1,000 trial credit and 30-day free trial with no credit card required.

  2. Create a database service

    In the Tiger Cloud console, click "Create service". Choose a Performance or Scale tier, select a cloud provider and region, then set compute and storage sizes. Click "Create" to provision your PostgreSQL database with TimescaleDB enabled.

  3. Connect your application

    Copy the connection string from the service details page. Use your PostgreSQL client or driver (psql, pgAdmin, or your app's ORM) to connect. Replace the password placeholder with the one provided during service creation.

  4. Load sample time-series data

    Run the provided SQL script from the TigerData docs to create a hypertable and insert sample data. Use the `create_hypertable` function to partition by time. Verify data with `SELECT * FROM <table> LIMIT 10;`.

  5. Enable compression and query

    Run `ALTER TABLE <table> SET (timescaledb.compress);` then `SELECT add_compression_policy('<table>', INTERVAL '7 days');`. Query the compressed data with a time filter to see 10-100x faster analytical performance.

Frequently Asked Questions

What is TigerData and how is it different from Timescale?

TigerData is a PostgreSQL-native data platform built on TimescaleDB, rebranded in June 2025 to reflect its evolution beyond time-series databases. It now serves transactional, analytical, and AI workloads, with most cloud workloads being non-time-series.

How does TigerData's automatic partitioning and compression work?

TigerData automatically splits data into chunks by time intervals and space keys, enabling faster queries. Its columnar compression achieves up to 95% storage reduction, lowering costs and accelerating analytical queries by 10-100x compared to vanilla PostgreSQL.

What are TigerData's pricing tiers and costs?

TigerData offers three tiers: Performance from $30/month, Scale from $36/month, and Enterprise with custom pricing. Billing combines hourly compute metering with per-GB storage charges. New users get a $1,000 trial credit and 30-day free trial.

Does TigerData support vector search for AI applications?

Yes, TigerData supports vector search via pgvectorscale, combining pgvector with StreamingDiskANN indexing. This enables semantic similarity search for RAG and AI applications without needing an external vector database, with 28x lower latency than Pinecone.

Is TigerData open source and free to use?

The TimescaleDB extension remains open source under Apache-2.0 license, with enterprise features under the Timescale License. This means no vendor lock-in for the core product. However, the managed Tiger Cloud service has usage-based pricing.

What are the main weaknesses of TigerData according to user reviews?

Users report that managed cloud pricing scales quickly with usage, the UI becomes sluggish with many tables, and there are occasional breaking changes between PostgreSQL versions. Some also criticize marketing claims, though technical reliability and PostgreSQL compatibility remain valued.

Alternatives

How TigerData (formerly Timescale) compares

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

This tool

TigerData (formerly Timescale)

Pricing
TigerData offers three tiers: Performance (starting at $30/month, pay-as-you-go for development and moderate workloads), Scale (starting at $36/month, for production applications with HA replicas and advanced features), and Enterprise (custom pricing for mission-critical systems with 24/7 support, disaster recovery, and compliance certifications). Billing combines hourly compute metering with per-GB-per-month storage charges on a usage-based model; tiered storage incurs separate fees. New users receive a $1,000 trial credit and 30-day free trial with no credit card required. The company launched a free 'Agentic Postgres' tier in October 2025 as part of product-led growth, though feature limits are unspecified. No hidden fees; customers can cancel anytime and adjust compute up or down without service interruption.
Target
TigerData is a PostgreSQL-native data platform built on TimescaleDB, a widely-deployed open-source extension for time-series and analytical workloads.
Strength
PostgreSQL compatibility means familiar SQL, existing drivers/tools, and no vendor lock-in for the open-source TimescaleDB extension.
Watch for
Managed Tiger Cloud pricing scales quickly with compute and storage; usage-based billing surprises users comparing to fixed-tier competitors like BigQuery.

Amazon Aurora

Pricing
Pay per hour for compute + I/O. I/O-Optimized from $1.20/hr (db.r6g.large).
Target
Developers needing a managed, high-availability relational database on AWS.
Deployment
AWS cloud only
Strength
Decoupled compute/storage architecture with up to 15 low-latency read replicas.
Watch for
I/O costs can escalate unpredictably under spiky workloads without I/O-Optimized.

Google BigQuery

Pricing
$5/TB per month for storage; $6.25/TB for queries (on-demand). Flat-rate slots from $2,000/month.
Target
Analysts and data teams needing serverless SQL analytics on large datasets.
Deployment
Google Cloud only
Strength
Serverless architecture with automatic scaling and built-in ML capabilities.
Watch for
Query costs can be unpredictable; performance degrades with many concurrent small queries.

Couchbase Capella

Pricing
Starting at $0.40/hr per node (3-node minimum). Custom enterprise plans available.
Target
Developers building real-time, low-latency applications with flexible data models.
Deployment
AWS, GCP, or Azure
Strength
Multi-model (JSON, SQL, full-text search) with built-in caching for sub-millisecond reads.
Watch for
Complex pricing model; node-based scaling can lead to over-provisioning for small workloads.

User reviews

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Sources

Reporting on this tool draws on these publicly available sources.

  1. www.tigerdata.com — Company overview, product positioning as 'Time-Series PostgreSQL at Petabyte Scale', core capabilities, and Tiger Cloud branding.
  2. www.tigerdata.com — Rebrand rationale (majority of workloads not time-series), evolution toward transactional-analytical hybrid and AI/agentic workloads, customer examples (Mistral, HuggingFace, Nvidia, Tesla, JP Morgan Chase).
  3. dev.to — Rebrand narrative, company history (founded 2015, $180M raised, 2,000 customers, 3M databases in production), product vision for AI/agentic era.
  4. news.ycombinator.com — Community feedback on rebrand (criticism of naming, marketing tone, DBA requirement for tuning, tiered reads issues), user strengths (PostgreSQL compatibility, reliability).
  5. www.tigerdata.com — Pricing tiers (Performance $30+/month, Scale $36+/month, Enterprise custom), billing structure (hourly compute, per-GB storage), free trial ($1,000 credit, 30 days), flexibility (cancel anytime, no hidden fees).
  6. www.g2.com — User ratings (4.6 stars, 114 reviews), strengths (real-time analytics, PostgreSQL compatibility, continuous aggregation, compression, community support), weaknesses (UI slowness at scale, higher query costs, missing vector visualizers).
  7. github.com — Open-source license (Apache-2.0 primary, TSL for enterprise features), architecture (C 65.3%, PLpgSQL 27.1%), core capabilities (hypertables, compression, continuous aggregates).
  8. www.tigerdata.com — Live migration limitations (no mutable columnstore, no schema/table exclusion, NaN/Inf value conversion), breaking changes (PostgreSQL 17.1, 16.5, 15.9 binary interface issue), disk resize constraints (once per 6 hours).
  9. www.tigerdata.com — Kafka integration (Kafka source connector, SASL/SCRAM authentication, Schema Registry support), S3 integration (CSV/Parquet sync, real-time connector), lakehouse export (Apache Iceberg).
  10. www.tigerdata.com — Vector search via pgvector and pgvectorscale, StreamingDiskANN indexing (28x lower p95 latency, 16x higher throughput than Pinecone), hybrid BM25+embedding search.