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Your agents need one database. Not five.

Unify OLTP, OLAP, and vector search in a single system built for AI agents. Give your apps and agents real-time data, contextual reasoning, and transactional consistency at any scale.

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RegattaDB

One System. Complete Business Context. Zero pipelines.

OLTP runs the business. OLAP understands the business. Vectors provide the semantics. Together, agents and applications read live state, execute transactions, and reason over context in a single step. The result is a stable, scalable data foundation that grows with your workloads rather than constraining them.

OLAP
Analyze in real-time, at scale
Run complex analytical queries across live operational data without replication delays or batch lag. Agents understand what is happening right now, not what happened last night.
OLTP
Transact on live business state
Every write, update, and transaction executes with serializable consistency, even at high concurrency across thousands of simultaneous agents. Not eventual consistent.
VECTOR
Reason and gain meaning
Semantic search and vectors are first-class capabilities, not bolt-ons. Agents surface contextually relevant information, compare reasoning paths, and act on meaning, not just keywords.
Massive Linear Scale
Add nodes and scale linearly to meet any workload, from a single instance to billions of concurrent interactions across a distributed cluster.
Best-in-Class Performance
Top-of-class results across OLTP, OLAP, and vector benchmarks. RegattaDB doesn't trade performance in one mode to enable another.
Efficient by Design
A unified engine means no redundant infrastructure, no overprovisioning across three systems. Do more with less compute and storage.

One engine. One dataset. No ETL.

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Millions
Concurrent agents
100%
Correct context
Zero
Insight lag
1
Unified data layer

AI agents don't just assist.
 They decide, coordinate, and act at scale and in real-time.

Autonomous AI agents surface a fundamental gap: the infrastructure we built for humans making decisions was never designed for millions of agents acting in real time. They demand a new class of capabilities and system requirements that existing systems were never designed to handle.

The legacy data stack won't keep up.

Critical Requirements for Agentic AI

Capabilities

Think, Reason & Act Autonomously

Agents must form goals, evaluate options, and execute actions without human intervention, operating as independent decision-making entities.

✓ RegattaDB delivers the ability to transact with integrity, analyze live operational data and reason over semantics all through a single connection.

Cognition
RegattaDB Delivers on your Agentic AI Requirements
10⁶
Concurrent agents
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<500ms
Decision latency
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10⁹
DB interactions/sec
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AI winners won't be decided by models. They'll be decided by data.

The Agentic AI Challenge: Data

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Built different. From the storage up.

Many have tried to collapse OLTP and OLAP into a single database. None have succeeded, until now. The RegattaDB team looked at the problem differently: not as a compute challenge, but as a storage challenge. That inversion changes everything.

RegattaDB is a database architected from day one for agents.

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A New Concurrency Model

RegattaDB implements a patented transaction and concurrency control model that guarantees serializable isolation within a distributed environment, with extreme compute efficiency. Transactional and analytical workloads share a single engine without conflicting.

The result: transactions, analytics, and vector search at massive scale, in a single engine, at a fraction of the cost of running each separately.

Explore the Documentation

Connect any agent.
 Instantly.

RegattaDB ships with native MCP support, meaning any MCP-compatible AI agent can connect to your live transactional data, run real-time analytics, and execute vector search through a single, standardized connection. No custom integration. No middleware. No lag.

WORKS WITH CLAUDE | WORKS WITH CURSOR | ANY MCP CLIENT

Read the MCP docs

mcp_config.json

Agent connected · OLTP · OLAP · Vector · Live

Built for agents that act in the real world.

  • NOC Automation
    Shift network operations from reactive firefighting to supervised automation: detect anomalies, match to prior incidents, apply corrective actions, and maintain a complete operational record.
  • Agent Fleet Orchestration
    Checkpoint every agent instance in real time. Resume from any state after failure. Query the full history of every agent decision across your fleet with zero ETL lag.
  • Hyper-Personalized Commerce
    Combine live inventory state, purchasing behavior analysis, and semantic product similarity to execute personalized pricing and bundling decisions at the moment of intent.

  • Real-Time Fraud Detection
    Evaluate live transactions against behavioral baselines and semantic similarity to known fraud patterns, and act at the moment of authorization, not hours later.

  • Autonomous Portfolio Management
    Ingest market data, evaluate risk, reason over qualitative signals, and execute trades, all within a single transactionally consistent system at the speed markets require.

  • Security Incident Response
    Detect behavioral anomalies against live baselines, compare with semantically similar historical incidents, and enforce responses automatically with full auditability.

  • NOC Automation
    Shift network operations from reactive firefighting to supervised automation: detect anomalies, match to prior incidents, apply corrective actions, and maintain a complete operational record.
  • Agent Fleet Orchestration
    Checkpoint every agent instance in real time. Resume from any state after failure. Query the full history of every agent decision across your fleet with zero ETL lag.

No existing database fully solves the agent data problem.

Every database category solves part of the problem. All require multi-system architectures and integration pipelines that introduce duplication, latency, and compounding cost. Except one.

Fragmented context degrades reasoning
Agents inferring context from stale data and disconnected metadata make errors that multiply across every downstream interaction.
Pipelines introduce latency and risk
CDC, ETL, and overnight batch runs aren’t meant to be real-time intelligence infrastructure. Building agents on them inherits every failure mode.
Complexity compounds as you scale
Layering agents on top of legacy architecture preserves technical debt rather than resolving it. Intelligence degrades rather than compounds.

Stop stitching.
 Start building.

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