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TimescaleDB

Postgres for time-series, real-time analytics, events, and agentic applications.

Quick Info

Starting at $0
0 reviews
Build stage

Overview

TimescaleDB, now under the TigerData umbrella, is a specialized open-source relational database built as an extension for PostgreSQL. It is engineered to handle the unique challenges of time-series data, offering high-performance ingestion and complex query capabilities that standard relational databases often struggle with. By leveraging the familiarity and robustness of PostgreSQL, TimescaleDB provides a powerful yet accessible solution for developers and organizations.

The platform extends PostgreSQL's capabilities with features like automatic partitioning, advanced compression, and continuous aggregates, making it highly efficient for storing and analyzing time-stamped data from various sources such as IoT devices, financial markets, and application logs. With the introduction of 'Agentic Postgres,' it's also evolving to support the specific data needs of AI agents and intelligent applications, positioning itself as a versatile data platform for modern, data-intensive workloads.

Best For

IoT device data collection and analysis
Real-time monitoring and observability platforms
Financial market data analysis
Application performance monitoring (APM)
Sensor data processing
Event logging and analytics
AI agent data storage and processing

Key Features

PostgreSQL compatibility and ecosystem
Optimized for time-series data
Real-time analytics capabilities
Event processing
Scalable architecture
Cloud platform for robust deployment
Agentic Postgres for AI applications

Pricing

Beta Free plan

$0
  • Up to 2 services
  • Up to 750 MB per service
  • Shared compute
  • Forks
  • Logs
  • Insights
  • Connections
  • MFA
  • Pause/unpause
  • End-to-end encryption
  • Community support
POPULAR

Performance

$30 /month (compute) + $0.177
  • 4 database services
  • Up to 8 CPU and 32 GB memory per service
  • Up to 16 TB disk storage per service
  • Up to 5K IOPS, 250 Mbps BW
  • 1 VPC
  • 1 IP Allow-list
  • Single-node high-availability replicas
  • Point-in-time-recovery to 3 days
  • Performance Insights
  • Basic support (9-5 ET)

Pros & Cons

Pros

  • Leverages the familiarity and ecosystem of PostgreSQL
  • Highly optimized for time-series workloads, offering superior performance
  • Supports complex real-time analytical queries efficiently
  • Provides a robust cloud platform for easy deployment and management
  • Designed for scalability to handle massive data volumes
  • New 'Agentic Postgres' features cater to AI/agent-based applications

Cons

  • Specific optimizations might add complexity for users unfamiliar with time-series databases
  • While built on Postgres, advanced features require understanding TimescaleDB's extensions
  • Pricing model might be a consideration for very small projects or hobbyists
  • Performance benefits are most pronounced with time-series data; less critical for general-purpose relational data

Reviews & Ratings

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