A reliable foundation for microservices that need to change over time.

Record business events once ad reuse them everywhere - making systems easier to evolve, debug and trust.

Eliminate coordination glue

Record events once and stream them natively. No dual writes, no outbox patterns, no CDC pipelines to keep services in sync.

Ship faster, evolve safely

Focus on business logic instead of maintaining sagas, retries, and reconciliation jobs. Evolve data models through replay, not migrations.

Simplify your stack

One platform for durable storage, real-time streaming, and projections. Fewer systems to operate, fewer failure modes.

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The Problem

From business processes to distributed complexity

Microservices and modular architectures are widely adopted to improve team autonomy and delivery velocity. In practice, however, teams quickly discover that the hardest part is not service boundaries or APIs—it's data. Each service owns its own database, updates propagate asynchronously, and no single place represents "what actually happened."

Data Inconsistency Across Services

Each service owns its own data, and consistency is achieved asynchronously. Over time, different services disagree about the current state of the business. Teams lose confidence because there is no single, authoritative record of what actually happened.

Tight Coupling vs. Fragmented Data

Shared databases undermine service autonomy and make schema changes risky, while isolated databases make even simple data access patterns complex. As the system grows, data ownership becomes unclear.

Distributed Transactions & Fragile Workflows

Business processes that span multiple services require coordination across databases without shared transactional boundaries. Teams implement sagas, retries, and compensating actions—mixing business logic with failure handling.

Dual Writes & Reliability Workarounds

To keep services in sync, teams write to a database and publish an event as two separate operations. When one succeeds and the other fails, data integrity is compromised. Outbox and CDC patterns add infrastructure and failure modes.

Reconstructing Past State & Debugging

When something goes wrong, teams can't answer "what did the system believe at that point in time?" Logs and snapshots provide partial clues but rarely allow deterministic reconstruction of behavior.

Projection & Read Model Management

Events need transformation into read models. General-purpose databases require external frameworks, custom microservices, or batch jobs—plus checkpoint management and synchronization logic.

The Microservices Complexity Tax

Data Fragmentation

Coordination Overhead

Operational Sprawl

Audit & Compliance Risk

Delivery Slowdown

Rising Infrastructure Cost

THE SOLUTION

Microservices without the mess

Microservices promise agility, but coordinating data across distributed services creates complexity. KurrentDB eliminates this challenge by making events the foundation of your architecture. Building on the principles of event sourcing and CQRS, services stay loosely coupled yet perfectly synchronized—no brittle point-to-point integrations, no distributed transaction headaches, just clean event-driven communication that scales.

Event-Native Data Model

Guaranteed Ordering & Concurrency

Durable Storage & Real-Time Streaming

Decoupled Read & Write Models

Deterministic Replay

Schema Evolution Without Downtime

High Performance Stream Indexing

Built-In Audit & Traceability

Native SDKs for Major Languages

HOW IT WORKS

KurrentDB in a microservices architecture

In a microservices architecture, business events flow through services and are persisted to KurrentDB as the single source of truth. Projections transform events into read-optimized models for queries, while reactions listen to events and trigger downstream services. Teams record what happened once and reuse it everywhere—with immediate consistency for commands and eventual consistency for projections and reactions.

More about KurrentDB View Docs

The Kurrent Difference

Plug-and-play vs. infrastructure project

Most teams don't struggle because they chose the wrong architecture pattern. They struggle because traditional databases and messaging systems were never designed to jointly guarantee ordering, history, and auditability across distributed services. KurrentDB addresses these problems natively—preserving the ordered history of business actions in a single, reusable backbone.

Traditional

Kurrent

Split between database state and message log

Source of Truth

Single, event-native system of record

Separate systems with coordination required

Data Storage & Streaming

Built-in - same technology

Dual writes - outbox or CDC needed

Write & Publish Consistency

Guaranteed - single write path

Partition-based or application-managed

Event Ordering

Guaranteed - per stream and global

Custom logic across database and broker

Concurrency Control

Native - stream level

Limited by broker retention and offsets

Replay & Time-Travel

Native - unlimited history

External stream processors required

Read Models & Transformations

Built-in projection engine

Migrations, backfills and reprocessing jobs

Schema & Model Evolution

Rebuild via replay

Multiple systems, configs and failure modes

Operational Footprint

One system to operate

High - state, messages and CDC can diverge

Risk of Data Drift

Minimal - single canonical log