Postgres.AI— a proactive DBA platform for Postgres
Detect, predict and prevent bottlenecks with Artificial Intelligence
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What we do
Detect performance bottlenecks
3 artificial DBAs (parts of the platform) control all database performance aspects, find bottlenecks before they affect your system and propose fixes using machine learning models. They predict effect of each proposed optimization.
Query analysis
Smart query normalization, deep performance analysis, SQL-tuning assistant based on Machine Learning.
Index set optimization
Index adviser for all types of Postgres indexes (multi-column, partial, btree, GIN, GiST, SP-GiST, brin).
Postgres parameters tuning
No more need of reading tons of documentation to tune Postgres. Just define parameters of your system and get detailed recommendations.
Detailed monitoring (Postgres-specific metrics)
WAL, checkpoints, autovacuum, replication, table and index bloat, SQL performance and more.
ML-backed Database Experiments
Our artificial DBA can run experiments to verify proposed optimization and avoid mistakes (continuous database administration).
Become an early adopter of the Postgres.AI support platform.
Automated Routine Health Check for Your PostgreSQL Databases
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Automated Health Check
Highly automated health-check procedures help heavy-loaded projects to quickly check wide variety of performance, scalability and HA/reliability aspects.
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Guidance and Training
Human experts with decades of DBA experience will guide you through the procedure and provide trainings to your engineers, if necessary.
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Optimization and Economy
The use of postgres.ai's framework for conducting database experiments helps to find optimal configuration, optimize SQL queries and do capacity planning to scale your project better at a reduced cost and resources.
Contact us
"Nancy" – an artificial DBA, expert in conducting database experiments
Database experiments are needed when you:
Add or remove indexes.
For a new DB schema change, want to validate it and estimate migration time.
Want to verify some query optimization ideas.
Tune database configuration parameters.
Do capacity planning and to stress-test your DB in some environment.
Plan to upgrade your DBMS to a new major version.
Want to train ML model related to DB optimization.
