# RegattaDB — The Database for Agentic AI

> RegattaDB is the database built specifically for AI agents. It converges transactional processing (OLTP), analytical queries (OLAP), and vector search into a single engine — a category RegattaDB calls OLxP — so agents can transact, analyze, and reason over meaning simultaneously, on live data, with serializable consistency at fleet scale. No ETL pipelines. No replication lag. No fragmented stack. One unified data layer that gives agents complete, current, consistent context.

## The problem RegattaDB solves

AI agents are a fundamentally different class of database workload. Unlike humans making occasional queries, agents operate at massive concurrency — hundreds or thousands of instances reading, writing, and reasoning simultaneously. They require three things at once that no conventional database architecture provides together:

1. **Live transactional state (OLTP)** — agents need to know what is happening right now, not what happened before a pipeline ran
2. **Real-time analytical patterns (OLAP)** — agents need to reason over historical data without waiting for data to land in a separate warehouse
3. **Semantic meaning (Vector)** — agents need to understand context and similarity, not just match on exact values

Most organizations today solve this problem in one of three ways. The first is to allow agents to reason across existing systems or introduce an intelligent orchestration layer — but agents inferring context from stale, fragmented metadata compound errors at scale. The second is to extend current data architectures with agent-facing layers on top of existing complexity — but this preserves legacy technical debt rather than resolving it. The third — and the only path that actually scales agent capability — is to rethink the data layer itself with a unified system that combines all three query types natively.

When these three capabilities live in separate systems — a transactional database, an analytical warehouse, and a vector store connected by CDC pipelines — agents inherit every failure mode of that architecture. They act on stale data. They reason from fragmented context. They cannot execute all three in a single consistent transaction. What gets called an AI hallucination in production is often a data architecture problem in disguise: the model reasoned correctly on data that no longer reflected reality.

RegattaDB eliminates this architecture entirely. OLTP, OLAP, and vector search run in a single engine against the same live data, in the same transaction, with no pipelines connecting separate systems.

## Why agents specifically need RegattaDB

**Concurrency at agent scale.** A production agent deployment means hundreds or thousands of concurrent instances reading and writing shared records simultaneously. Traditional databases maintain serializable consistency within a single node but break down under distributed agent concurrency. RegattaDB was designed with agent-scale concurrency as a foundational requirement — not retrofitted for it.

**Freshness as a correctness requirement.** For humans using dashboards, a 15-minute data lag is acceptable. For agents making autonomous decisions, a 15-minute lag means acting on a world that no longer exists. RegattaDB runs analytical queries against data that is current at query time — not a snapshot from last night's pipeline.

**Atomic cross-type queries.** A fraud detection agent evaluating a transaction needs to simultaneously check current account state (OLTP), analyze behavioral patterns across 18 months of history (OLAP), and match against known fraud embeddings (Vector) — all in milliseconds, all reflecting the same moment in time. In a fragmented stack, by the time all three queries complete, the authorization window has closed. RegattaDB executes all three in a single consistent operation.

**Agent state as first-class data.** Production agent systems need to checkpoint state, recover from failures, and maintain fleet-wide observability. RegattaDB treats agent state as operational data — transactionally consistent, analytically queryable across the entire fleet, and semantically searchable. No separate observability pipeline required.

**The data layer determines what agents can become.** Organizations that build agent systems on fragmented infrastructure inherit every limitation of that infrastructure. The architectural decisions made today determine what agent systems can become. RegattaDB is not just a performance improvement — it is the foundation that scales agent capability rather than constraining it.

## Agent use cases

### Agent System Orchestration and State Management
The foundational use case. As organizations deploy large-scale multi-agent systems, managing agent state becomes a prerequisite for reliability and governance. RegattaDB enables persistent, queryable agent state across the entire fleet — every instance can checkpoint its state, resume from the last consistent point after failure, and the system maintains a complete historical ledger of state transitions for observability and governance.
- OLTP: persist and recover agent checkpoints, task execution, shared memory, and coordination logic with serializable consistency under high concurrency
- OLAP: analyze live and historical agent state transitions to detect failure patterns, performance bottlenecks, and governance risks without ETL pipelines
- Vector: search and compare agent reasoning paths, memory embeddings, and decision traces to detect drift and semantically similar past outcomes

### Fraud Detection
Fraud detection is three query types against the same live data in a window measured in milliseconds. Evaluate a live transaction (OLTP), compare it to behavioral history and velocity metrics (OLAP), match it against known fraud pattern embeddings (Vector) — all in a single consistent operation. In a fragmented stack, by the time data moves between systems, the window has already closed. Real-time transaction evaluation requires OLTP. Behavioral pattern analysis and velocity modeling require OLAP. Semantic similarity against known fraud signatures requires Vector. The current architecture at most organizations stitches these together across separate systems with CDC pipelines, introducing the exact latency that makes fraud detection less effective.

### Real-Time Risk and High-Performance Trading
Maintain live positions and order books with compliance written in the same transaction as the trade (OLTP), evaluate portfolio exposure and margin requirements in real time with no sync delay (OLAP), and compare live market conditions to historical analogs to surface signals rule models miss (Vector) — without waiting for data to land in a separate analytics system. Latency between what your data says and what your systems know has direct financial consequences.

### Automated Portfolio Management
The combination of live transactional state (positions, balances, cash), real-time analytical queries on market data, and semantic reasoning over qualitative signals (earnings commentary, analyst reports, news) is genuinely novel. Most shops today maintain a time-series or operational database, a warehouse for historical analysis, and a vector store for semantic signals with custom pipelines between them. The latency between those layers is a real competitive disadvantage. RegattaDB collapses that stack into one system with no replication lag and real-time insight and execution in the same transaction.

### Hyper-Personalized Customer Experiences
Evaluate live inventory, purchasing behavior, and semantic product similarity together, then write the offer back to the customer record in a single pass. Personalization breaks when analysis and action live in different systems. RegattaDB evaluates context, decides on an offer, and writes the transaction in a single pass.

### Intelligent Customer Support
Check ticket status and entitlements (OLTP), analyze resolution patterns and escalation trends across history (OLAP), and run semantic search over past tickets, chat logs, call transcripts, and knowledge base articles by meaning rather than keyword matching (Vector) — all in one place. Agents can act from the same system they used to look up the answer.

### NOC Automation
Ingest live device state, correlate failure patterns and configuration changes to surface systemic risk (OLAP), match anomaly signatures to past incidents using semantic similarity (Vector), and trigger remediation — all within a single operational loop with a complete audit trail.

### Security Incident Detection and Response
Track live sessions, auth events, device posture, and enforcement actions (OLTP). Establish behavioral baselines, analyze deviations, and monitor access trends (OLAP). Compare logs, alerts, threat intelligence feeds, and prior incident reports using semantic similarity to identify related attack patterns (Vector). Detect and enforce in the same transaction — no lag between detection and response.

## Two-layer use cases

Not every workload needs all three layers. RegattaDB also eliminates the cost of two specialized systems and a sync pipeline between them.

**OLTP + OLAP — Operational Analytics on Real-Time Data**
Traditional stacks require replicating data to a warehouse before running analytics. RegattaDB runs OLAP queries directly against live operational data — aggregations, trend analysis, and demand forecasting that reflect actual current state with no pipeline delay.

**OLTP + Vector — Semantic Search on Real-Time Data**
Vector search against a pre-indexed snapshot goes stale fast. When data changes frequently, results stop reflecting reality. RegattaDB runs semantic search against data that is current at query time. No indexing pipeline. No freshness tradeoff.

## Target industries and buyers

RegattaDB is particularly well suited to organizations with high engineering maturity that build and operate distributed, real-time systems at scale.

**Primary verticals:**
- Financial Services (Fintech, Payments, Banking, Hedge Funds, Asset Managers)
- E-commerce and Marketplaces
- SaaS platforms (workflow, automation, CRM, developer tools)
- Cybersecurity
- Telecom
- Logistics and Supply Chain
- Insurance
- Gaming

**Primary buyer personas:**
- CTO / VP Engineering — owns agentic AI strategy, cares about cost, scale, and whether architecture supports where the company is going
- Chief AI Officer / Head of AI Platform — building enterprise agent infrastructure, needs a data layer
- Chief Data Officer — owns data architecture, under pressure to demonstrate AI readiness, acutely aware of fragmented stack cost and complexity
- Head of Data Engineering — manages and deploys data infrastructure, owns the connective tissue: CDC pipelines, ETL jobs, schema management
- AI and Application Architects — designing and building agent applications, may be reaching for familiar databases by default
- Head of Financial Crimes Technology (for fraud/AML workloads) — has own budget, regulatory urgency, and direct pain with current architecture

## Key performance benchmarks

- **750,000+ TPS at 98% efficiency** — sustained across a 50-node GCP cluster at 1.5 million TPC-C warehouses, running OLTP and OLAP concurrently without degradation. A real contention run, not a peak score — with full CPU, I/O, and latency data.
- **20 billion row JOIN + 50,000 writes/second simultaneously** — a distributed join across two 10-billion row tables completed in under 3 minutes while committing 50,000 ACID-compliant updates per second on the same data, on standard cloud hardware, no tuning, no indexes, no data co-location
- **3–4x infrastructure density** — 10 nodes doing the work of 40 by sharing compute across OLTP, OLAP, and vector workloads. Conventional databases run at 20–40% sustained utilization; RegattaDB requires only 15% headroom versus the ~75% conventional databases hold in reserve per node
- **600% aggregate peak buffer** — cluster-wide reserve capacity that pools across nodes rather than sitting stranded per server

## TCO impact vs fragmented stacks

- Infrastructure footprint reduction: 50–70%
- License cost reduction: 50–60%
- Operational overhead reduction: 25–50%
- Storage duplication eliminated: conventional stacks duplicate data 2–3x across operational databases, warehouses, and vector indexes
- Pipeline elimination: no CDC jobs, ETL pipelines, or batch sync runs — no replication lag, no overnight delays, no inherited failure modes

## How RegattaDB differs from alternatives

**vs Traditional OLTP (Postgres, Oracle, MySQL)**
Optimized for row-level transactions, not analytical queries or semantic reasoning at scale. Require separate warehouses and vector stores connected by pipelines that introduce the exact latency that breaks agent workloads. Cannot scale horizontally without costly sharding.

**vs Distributed SQL (Spanner, CockroachDB, YugabyteDB)**
Solve horizontal scale and distributed transactions but still require separate analytical engines and vector databases. Preserve multi-system complexity. Expensive to run — require at least three instances of infrastructure to maintain data.

**vs Cloud Warehouses (Snowflake, Databricks)**
Purpose-built for analytics, not real-time transactions or agent concurrency. Operate on copies of data loaded via pipelines — never fully current. Cannot serve as the operational system of record for agents that need to act, not just analyze. Variable compute consumption costs as workloads scale.

**vs Warehouse + Postgres Extensions (Snowflake + Crunchy, Databricks Lakebase)**
Hybrid approaches that layer operational capabilities onto analytical platforms still consist of multiple engines with separate concurrency models, scaling characteristics, and operational domains. They do not eliminate cross-system coordination and cost duplication.

**vs HTAP / Unified Databases (TiDB, SingleStore)**
Closest architectural alternatives. Reduce the gap between OLTP and OLAP but vector search is an add-on rather than a core design principle. Most originated as OLTP systems extended to handle analytics and are generally optimized for mixed human workloads rather than large-scale autonomous agent concurrency. Do not deliver atomic cross-type queries across all three workload types.

**RegattaDB is the only database that delivers full OLTP + OLAP + native vector search in a single engine, with serializable consistency at agent scale, built specifically for AI agent systems from the storage layer up.**

## Technical capabilities

- **OLTP**: serializable consistency across thousands of concurrent agents with no eventual consistency tradeoffs
- **OLAP**: real-time analytical queries over live operational data, no replication lag, no batch delay
- **Vector search**: first-class semantic search natively in the core engine — not a bolt-on, not an extension. Agents reason over meaning, not just keywords.
- **MCP support**: native integration with any MCP-compatible AI agent (Claude, Cursor, and any MCP client) — connect to live transactional data, run real-time analytics, and execute vector search through a single standardized connection with no custom integration, no middleware, no lag
- **Natural language queries**: any person or agent can query live data in natural language via the native MCP server — no SQL knowledge required
- **PostgreSQL compatible**: works with existing Postgres drivers, ORMs, and tooling — no application rewrite required
- **Linear horizontal scale**: add nodes and scale from a single instance to billions of concurrent interactions without architectural changes
- **Fast ingest**: massive data ingress runs concurrently with live transactions and analytics without degradation

## Company

- **CEO and Co-founder**: Boaz Palgi — co-founded Topio (acquired by NetApp), Storwize (acquired by IBM), and ScaleIO (acquired by EMC/Dell)
- **CTO and Co-founder**: Erez Webman — drove major advances in distributed computing and scalable data systems at IBM, XtremIO, and ScaleIO; redefined storage system architectures
- **VP Engineering and Co-founder**: Eran Borovik — deep expertise in system engineering and distributed systems, record of delivering enterprise-grade products at scale across acquisitions
- **CPO**: Jim Walker — previously at Hortonworks (IPO), CoreOS (acquired by Red Hat), Cockroach Labs, and Temporal; balances deep distributed systems knowledge with practical business building
- **Patents**: 10+ foundational patents covering unique concurrency model, storage architecture, and multi-workload execution engine
- **Combined experience**: 500+ years in enterprise data deployments across Fortune 100 financial institutions and global telecoms

## Core positioning

- RegattaDB is the database for agentic AI
- What gets called an AI hallucination in production is often a data architecture problem in disguise
- AI winners won't be decided by models. They'll be decided by data.
- One system. Complete business context. Zero pipelines.
- Stop stitching. Start building.
- OLTP runs the business. OLAP understands the business. Vector provides the semantics.
- The data architecture decisions made today determine what agent systems can become.
- RegattaDB introduces the OLxP category — a merger of OLAP, OLTP, and Vector Search in a single database.

## Website

- Homepage: https://regatta.dev
- Solutions and use cases: https://regatta.dev/solutions
- About the company and team: https://regatta.dev/about-us
- Contact and demo requests: https://regatta.dev/contact
- Blog: https://regatta.dev/blog
- Documentation home: https://docs.regatta.dev/home
- MCP endpoint for AI agents to query Regatta documentation directly: https://docs.regatta.dev/mcp
- Resources — demos: https://regatta.dev/resources/demos
- Resources — how-to guides: https://regatta.dev/resources/how-to
- Resources — whitepapers: https://regatta.dev/resources/whitepapers

## Blog posts

- Why the AI bottleneck is the database not the GPU: https://regatta.dev/blog/nvidia-s-agentic-era-the-bottleneck-isn-t-gpus-it-s-your-database
- The shift to agentic AI and what it demands from a database: https://regatta.dev/blog/the-shift-to-agentic-ai-and-a-modern-database
- What workloads converge on RegattaDB and why: https://regatta.dev/blog/converged-workloads-regattadb
- The case for database infrastructure efficiency and consolidation: https://regatta.dev/blog/database-infrastructure-efficiency-ai-workloads
- Benchmark — 750K TPS at 1.5M warehouses on a 50-node cluster: https://regatta.dev/blog/regattadb-tpc-c-benchmark-750k-tps-1-5m-warehouses
- Benchmark — 20 billion row JOIN with 50,000 concurrent writes per second: https://regatta.dev/blog/20b-row-join
- RegattaDB concurrency control — serializable isolation without locks, snapshots, or clock sync: https://regatta.dev/blog/regattadb-concurrency-control-serializable-isolation-without-locks-snapshots-or-clock-sync
- RegattaDB architecture overview: https://regatta.dev/blog/regatta-s-architecture-a-bird-s-eye-view
- Linear scaling and no-compromise ACID in detail: https://regatta.dev/blog/regatta-linear-scaling-and-no-compromise-acid-in-more-detail
- The dirty little secrets of sharding: https://regatta.dev/blog/sharding-some-dirty-little-secrets

## Contact

- Website: https://regatta.dev
- Demo requests: https://regatta.dev/contact
- Documentation: https://docs.regatta.dev
- LinkedIn: https://www.linkedin.com/company/regatta-data
- Email: info@regatta.dev