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Concerned about AWS Timestream’s future? We can help.

Tiger Data is the #1 time-series database, from the creators of TimescaleDB.

De-risk your future by keeping time-series workloads on AWS while scaling performance to handle billions of data points at scale.

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Why AWS Timestream Creates Future Technical Debt

Roadmap risk

Unclear long-term support for Timestream for LiveAnalytics and Timestream for InfluxDB.

Non-standard syntax

’Postgres-like’ SQL and custom Flux languages create challenges for long-term maintenance and extension.

Performance drag

Slow ingest and queries for high-throughput time-series workloads and complex JOINS.

Tiger Cloud

Postgres Purpose-Built for Time-Series Workloads

Tiger Crypto

Edge-to-cloud ingest

Unified data platform consolidating operational and historical data. On Tiger Cloud.

Built for time-series

Access downsampling, retention, rollups without losing raw fidelity and ACID guarantees.

Fully managed Postgres

95% compression coupled with sub-second SQL-native queries.

Fresh dashboards

Incremental materialized views keeps your data fresh.

Performance at scale

Compared to Timestream for LiveAnalytics, Tiger offers:

  • Up to 6,000× higher insert rates
  • 5–175× faster queries
  • 150–220× lower storage costs

Ready to Scale Your Timeseries Workloads?

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Use Cases

CERN

At CERN, our control systems span a wide range of equipment domains producing millions of time-stamped signals every second. We needed a solution that easily combined with our existing tech stack while also improving performance. Tiger delivered with more than 7x storage space savings thanks to compression, and 10x-40x faster queries.

Rafal Kulaga, Staff Software Engineer, CERN

Cloudflare

For many teams at Cloudflare, Tiger strikes a phenomenal balance between the simplicity of storing your analytical data under the same roof as your configuration data, while also gaining much of the impressive performance of a specialized OLAP system.

Robert Cepa, Senior Software Engineer, Cloudflare

Polymarket

It was a very easy process to set up so we went with the Tiger Cloud solution, which is helpful because we have a lot of time-series data that we are processing.

Jonathan Amenechi, Software Engineer, Polymarket

Read the Case Studies

Migration Path forward

Choose from 3 Migration Options Depending on Your Level of Expertise

pg_dump/pg_restore

Only recommended if your database is small (<100 GB) and you can afford downtime

Live migrations

End-to-end migration solution (including backfill, change data capture)

Dual-write and backfill

Best if your application is mostly append-only (no frequent updates or deletes)

Ready to de-risk your Timestream dependencies? Talk to an engineer today.

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