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TIMESCALEDB
Time-series database for high-performance real-time analytics
Apache-2.0
ABOUT
AI applications that generate time-series data — model inference logs, token usage metrics, sensor readings, or monitoring telemetry — need a database that can efficiently ingest and query high-velocity temporal data alongside vector embeddings. TimescaleDB adds automatic time-based partitioning, continuous rollups, and compression to PostgreSQL, letting teams store and analyze millions of data points per second while keeping the option of pgvector-powered semantic search in the same database.
INSTALL
docker pull timescale/timescaledb
INTEGRATION GUIDE
1. Store and analyze high-frequency model inference metrics, token usage, and latency data alongside vector embeddings
2. Build real-time monitoring dashboards for AI agents and LLM pipelines using PostgreSQL-compatible SQL queries
3. Ingest and query IoT sensor data at millions of data points per second with built-in partitioning and compression
TAGS
postgrestime-seriesdatabaseanalyticssqliotreal-timepgvector