Performance

The ZEN Engine is written in Rust and achieves exceptional throughput across all language bindings. On a MacBook Pro M3 single-core, the engine processes an average of 91,000 evaluations per second across 74 real-world decision benchmarks.

Download benchmark report

Full benchmark results with 74 decision scenarios

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Benchmark highlights

Performance varies based on decision structure. Pure decision tables and expression nodes achieve 150K+ evaluations per second. Decisions using Function nodes run slower (3-50K req/s) due to JavaScript runtime overhead.

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Language binding performance

All bindings share the same Rust core. The binding layer adds minimal overhead for most languages.

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Performance optimization tips

Pre-compile decisions. Use ZenDecisionContent to parse and compile decisions once, then reuse them for multiple evaluations. This is available in Node.js, Python, and Go SDKs. Reuse engine instances. Create a single engine at startup rather than instantiating per-request. Engine creation has initialization costs. Batch evaluations when possible. If you’re evaluating the same decision with many inputs, batch them to amortize any per-call overhead. Profile your specific decisions. Performance varies significantly based on decision complexity. A simple lookup table runs 50x faster than a complex multi-stage graph with custom functions.

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Benchmark methodology

All benchmarks were run on a MacBook Pro with Apple M3 chip using single-core execution. Each scenario represents a real-world business rule pattern:

• Decision tables only (100K+ req/s): Pure table lookups and expressions
• Mixed graphs (50-100K req/s): Multiple nodes, branching logic
• Function-heavy (3-50K req/s): Decisions using Function nodes with JavaScript execution

The benchmark measures pure evaluation time excluding I/O, network latency, and decision loading. Production throughput depends on your infrastructure, decision structure, and data serialization overhead.