QuantumDMN vs Competitors

See how QuantumDMN compares to other DMN engines on the market. We've built QuantumDMN to address the limitations of traditional decision engines, particularly around stateful decisioning, quantitative metrics, and real-time simulation.

Quick Overview​

Detailed Comparison​

Core Capabilities​

What Makes QuantumDMN Different​

*Requires external data stores and pre-processing

Developer Experience​

Runtime & Performance​

Deployment & Operations​

SDK Support​

TCK Compliance Details​

The Quant Advantage Explained​

Stateful Decisioning: The Ledger​

Traditional DMN engines are stateless - they evaluate inputs and return outputs without any memory of past decisions. This means you need to:

1. Pre-compute all historical aggregates externally
2. Pass all context to every decision call
3. Build and maintain separate data pipelines for metrics

QuantumDMN introduces "The Ledger" - a built-in state management layer that enables:

// Traditional approach
precomputedData = computeRolling24hFraudScore(userId) // separate job
decision = evaluate(dmn, {...input, fraudScore: precomputedData})

// QuantumDMN approach
decision = evaluate(dmn, input) // metric computed in-flight

Real-time Simulation: The Sandbox​

Before QuantumDMN, changing decision logic was a "deploy and pray" exercise. You'd push changes to production and hope the outcomes matched expectations.

The Sandbox allows you to:

• Run proposed changes against historical data
• See KPI impact before deployment
• Compare original vs. modified decision outcomes
• Validate business assumptions with real numbers

KPIs as First-Class Citizens​

In traditional engines, KPIs are an afterthought - calculated in BI tools days or weeks after decisions are made.

QuantumDMN treats metrics as decision elements:

• Transactional Metrics: Calculated live (e.g., Debt-to-Income Ratio)
• Windowed Metrics: Aggregated from history (e.g., Rolling 24h Fraud Score)
• Blocking Metrics: Decisions can be halted based on calculated scores

Native Performance vs. JVM Overhead​

Traditional engines like Drools run on the JVM, leading to slow "cold starts" and massive memory consumption (often hundreds of megabytes just for a basic knowledge base). This makes them poorly suited for modern, serverless, or highly-containerized architectures.

QuantumDMN is written in 100% native Go:

• Sub-millisecond decision evaluation
• Lightweight memory footprint (~10MB-50MB)
• Instant startup times, making it perfect for serverless infrastructure.

Complete Auditability​

When managing business rules, knowing what was decided is only half the battle; you need to know why it was decided. Tools like Drools require significant custom development to trace the declarative rule graph and build audit trails.

QuantumDMN provides out-of-the-box audit trails for every transaction, instantly showing which rules fired, what the variables evaluated to, and precisely how the final decision was reached.

When to Choose QuantumDMN​

QuantumDMN is ideal if you:

• Want to utilize full DMN 1.5 conformance
• Want to have the best User Experience designing and testing your decisions
• Want to extend capabilities of Camunda 8 decisions
• Need stateful decisions based on historical patterns
• Want to measure decision impact with real KPIs
• Want to simulate impact of decision changes against historical data
• Require high-volume processing with minimal resource usage and exceptional speed
• Want simple deployments on Kubernetes or VM platforms
• Want a single platform for modeling, testing, and deployment
• Want to utilize our FEEL language support during your work on decision models

Sources​

1. Camunda DMN-Scala TCK Results - 2850/3390 tests passed (July 2024)
2. Drools DMN Conformance Level 3 - Full runtime support for DMN 1.1-1.6 at CL3
3. jDMN TCK Results - Goldman Sachs jDMN 9.0.0: 3390/3390 tests passed
4. Camunda Modeler - Desktop and Web modeler for BPMN/DMN
5. KIE DMN Editor - Web-based DMN editor for Drools
6. Camunda JVM Memory - Default Optimize heap: 1GB
7. Camunda 8 Architecture - Camunda 8 requires distributed microservices (Zeebe)
8. Drools Serverless Challenges - Kogito/GraalVM needed for cloud-native Drools
9. Camunda Cloud - Camunda's SaaS offering (Camunda 8)
10. Camunda Go Client - Go client for Zeebe (deprecated)
11. Camunda JS SDK - Node.js SDK for Camunda 8
12. jDMN JavaScript Runtime - Goldman Sachs jDMN JavaScript runtime
13. Camunda Python Client - PyZeebe community client
14. Camunda .NET Client - C# client for Zeebe
15. Camunda Platform 7 TCK Results - 2741/3390 tests passed

Ready to Try QuantumDMN?​

Start with Developer tier and experience quantitative decisioning today. If you have any more questions, feel free to reach out to us.