---
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"
---


# Managed Postgres that keeps up with your machines

Ingest, storage, live and historical queries, backups, upgrades, and scaling, handled for you at petabyte scale on Tiger Cloud.

## 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 machine data without leaving Postgres

### Automatic Partitioning

Hypertables partition any Postgres table by time or id automatically, so ingest stays fast and queries stay predictable as tables grow.

**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

Row storage keeps writes fast. Columnar storage makes analytical scans fast. One table does both, converting automatically as data ages.

**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. A distributed block layer with 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. No separate search service, no sync pipelines, 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.

## One truth across your database and lakehouse

When telemetry lives in one system and analytics in another, the two can disagree. Tiger Lake and Connectors keep your database, lakehouse, and streams in sync without brittle pipelines.

### 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