Autonomous database optimization 

DBO continuously analyzes your production database traffic, finds risks before they become incidents, and shows your team what to do next.

Trusted by 2100+ companies globally

Key features

Database optimization, deployed in minutes

Performance recommendations

Get the exact index or rewrite to ship, backed by before and after query plans.

  • Built from your actual production traffic, not synthetic benchmarks.
  • Before/after execution plans show projected impact first.
  • Apply with confidence , no trial and error on live data

In-memory cache

Cut tail latency and CPU headroom risk without touching application code or planning a migration.

  • Repetitive selects served from cache, up to 400x faster
  • Smart SQL-level invalidation on every write, no stale data
  • Auto or manual modes — let DBO decide or set rules per query.

Automated caching

Identify caching opportunities using advanced machine learning to detect patterns in query traffic automatically.

  • Continuously adapt to changing query patterns in real-time.
  • Autonomously optimize cache configurations at scale, achieving efficiency levels beyond manual tuning
  • No configuration or application-side coding necessary

Connection pooling

Consolidates hundreds of microservice connections into a managed pool, freeing the CPU consumed by connection overhead and churn.

  • Stops connection storms from taking production down.
  • One fewer infrastructure component — drop the proxy line item.
  • Transparent to your app, just update the connection string

Setup database agent

Get started in two steps

Configure DBO for your database.

Install DBO Agent in your cluster.

Learn more

Additional resources

Qwen2.5:14B vs GPT-4o-mini – Cost & Performance at Scale

Blog

Qwen2.5:14B vs. GPT-4o-mini: Which One Is Cheaper at Scale?

Discover how you can optimize LLM cost without sacrificing performance.

Docs

Database Optimizer – Introduction and Key Benefits

Learn more about DBO in our product pages – explore how our AI-driven database cache works.

Cloud Cost Optimization_ 5 Impactful Tactics For 2025

Blog

Cloud Cost Optimization: 5 Impactful Tactics For 2025

Cloud cost optimization is the best way to manage long-term cloud costs – and automation helps.

FAQ

Your questions, answered

What is Cast AI’s Database Optimizer (DBO)?

Cast AI DBO is an autonomous caching solution that improves database query performance, reduces database load, and lowers infrastructure costs without requiring code changes, configuration, or manual tuning.

How does DBO improve application performance?

DBO acts as a transparent proxy between your application and database, automatically caching frequently accessed query results and serving them at sub-millisecond speeds, while intelligently invalidating cached data when changes occur.

What types of workloads benefit most from DBO?

DBO is designed to accelerate read-heavy workloads, including SaaS applications, APIs, microservices, and transactional systems, especially those with stateful workloads or long-running jobs that are sensitive to database performance.

How is DBO deployed in my environment?

DBO is deployed as a lightweight proxy within your architecture. To deploy it, simply update your application’s database connection string to point to DBO. No code changes or application rewrites are needed.

What databases does DBO support today?

DBO initially supports PostgreSQL and MySQL, with additional database engines planned on the roadmap. Please contact the Cast AI team for the most up-to-date compatibility details.

How does DBO ensure cache consistency and freshness?

DBO uses real-time query analysis and a smart invalidation engine to automatically detect when cached data becomes stale and remove or refresh it, ensuring applications always receive accurate results.

Can I control what DBO caches or customize caching rules?

Out of the box, DBO works fully automatically, but advanced users can customize caching policies if needed.

What kind of results can I expect from DBO?

Customers typically see significantly faster query response times, reduced database CPU and I/O load, improved resource efficiency, and lower cloud database costs — all with minimal operational effort.

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