---
title: "Tiger Cloud — Production Postgres at Scale"
description: "Fully managed PostgreSQL from the creators of TimescaleDB. 99.9% uptime SLA, 10,000+ IOPS, columnar compression up to 95%, and built-in search. Try it free."
url: "https://www.tigerdata.com/cloud"
---


# Production Postgres That Doesn't Break

From the Creator of TimescaleDB, fully-managed, highly available, at petabyte scale

## Durability by the numbers

### 99.9% uptime SLA

Monthly guarantee for HA Replicated services (Enterprise). Public SLA. Real-time status page.

### 110,000+ IOPS

Single-volume read throughput on Fluid Storage. 1.4 GB/s bandwidth. Synchronously replicated on every write.

### Up to 14-day PITR

Point-in-time recovery via pgBackRest. Weekly full backups, daily incrementals, continuous WAL retention. Optional cross-region.

## Scale TimescaleDB

### Automatic Partitioning

TimescaleDB hypertables turn any Postgres table into a table automatically partitioned by time or id for fast ingest and predictable queries at massive scale.

**How it works:**
- Time/id partitioning; partition skipping at query planning time
- Efficient index-only scans; supports skip-scan patterns on composite indexes

**Cloud highlight:** One-click hypertable creation; streaming from Kafka, S3 and other Postgres databases into hypertables; hypertable monitoring and exploration; partition size recommendations.

### Row/columnar Hybrid Storage

Keep years of history online at a fraction of the cost—while making your analytical queries even faster.

**How it works:**
- Automatic conversion between rowstore and columnstore; fast columnstore updates.
- Vectorized operators with SIMD acceleration; chunk skipping with sparse indexes.

**Cloud highlight:** Deep insights into query performance with access to plans for slow queries; detailed access to compression ratio and storage savings.

### Compression (up to 95%)

Keep years of history online at a fraction of the cost—while making your analytical queries even faster.

**How it works:**
- Columnar encodings (delta/dictionary/RLE), time-order aware.
- Applies filters and aggregates directly on compressed data, only decompressing what's needed for faster queries.

**Cloud highlight:** Automated tiering to low-cost object storage; scheduled columnstore/compression jobs with alerts.

### Incremental Materialized Views

Our continuous aggregates (caggs) enable incrementally refreshed rollups for instant dashboards.

**How it works:**
- Handle late data and updates; parallelized batched refreshes.
- Hierarchical caggs for more efficient computation; real-time mode to include latest changes.

**Cloud highlight:** Cagg creation wizard, cagg refresh observability with refresh failure notifications.

### Automated Data Management

First-class automation for columnstore, retention, and aggregate refresh with full auditability.

**How it works:**
- Built-in job scheduler with retries and visibility.
- Configurable policies for columnstore, retention and continuous aggregates.

**Cloud highlight:** Policies for tiering to low-cost object storage, job monitoring.

### Specialized Time-series Functions

Hyperfunctions simplify fast, advanced time-series analysis using ~200 native SQL functions.

**How it works:**
- Statistical rollups, time-weighted averages, approximations, interpolation and more.
- Partial aggregations to eliminate costly reprocessing.

**Cloud highlight:** Query-level performance insights, and plan visualization tools.

## Fluid Storage

A storage layer Postgres never had. Your workloads will notice. Fluid Storage is a distributed block layer that gives Postgres capabilities it was never designed to have. More throughput, true elasticity, and no wasted storage at any scale.

### No duplicated data

One copy of your data, replicated synchronously across block servers before every write is acknowledged. A single source of truth that is always consistent and always protected, without paying for mirrored volumes.

### True elasticity

Volumes expand and contract automatically with your workload. No manual resizing, no cooldown windows, no paying for capacity you don't use. Storage scales to 128TB and beyond.

### Local SSD cache

Frequently accessed data is served from local SSD, so your most demanding queries never wait on network round-trip. 400K+ IOPS without the high cost.

## Search, built into Postgres

Run keyword, vector, and hybrid search where your data already lives in Tiger Cloud. No separate search service, no sync pipelines, and no extra infrastructure to manage.

### Keyword + semantic

Use BM25, vectors, or both together in one database.

### Less to operate

No duplicated data, no ETL pipeline, no extra search cluster.

### Built for production

Lower latency, lower complexity, and fewer moving parts from dev to scale.

### Keyword search (BM25)

Full-text search on transactional data.

### Vector search

Store and query embeddings in Postgres.

### Hybrid search

Combine keyword and vector results in one system.

## Always in sync with your data sources

TimescaleDB is built in the open. Join thousands of developers, engineers, and data scientists pushing the boundaries of what's possible with PostgreSQL.

### Tiger Lake

Tiger and your lakehouse always in sync so analytics and agents share one truth.

### Connectors

Stream data with SQL and keep Kafka, S3, and Tiger in sync without brittle pipelines.

## Plug into your tech stack

Use Tiger Data with your preferred cloud provider, and the wider Postgres ecosystem.

## Enterprise-ready by default

Meet security and operational requirements of production systems.

### 24/7 Support

Round-the-clock coverage with global Postgres experts and guaranteed enterprise response times.

### Enterprise Trust

Contractual uptime SLAs, regional data isolation, and enterprise-ready compliance certifications.

## Start building or migrate today