Simulation Tools

Model your backend under four workload archetypes: read-heavy cache pressure, write amplification under indexing, burst traffic connection saturation, and analytics query contention with OLTP.

Project infrastructure costs over 3–36 months using compound monthly growth rates for compute, storage, network, and managed services. Includes optional engineering overhead.

Simulate DAU growth → RPS → connection pool saturation → latency inflection over months. Supports single DB, read replicas, sharding, and serverless architecture tiers.

Model producer/consumer throughput imbalance over a 10-minute window. Includes consumer health degradation under backlog pressure and autoscaling with configurable delay.

Chain two simulation engines together with deterministic transfer functions. Output signals from the first engine (connection utilization, WAL throughput, queue data delay) feed as parameter overrides into the second. Models causal cross-domain interactions no single engine can capture.

Derive node stress states, risk path activations, and deterministic mutation suggestions from simulation output. Maps metric peaks to architectural stress types (pool saturation, cache eviction, queue backlog, replication lag) and generates structured remediation paths. Optionally overlay onto architecture graphs.

Run the same base parameters against 2–6 architecture variants simultaneously. Compare P95 latency, connection utilization, cost trajectories, or queue backlog across single_db vs read_replicas vs sharded, or any other parameterized dimension. Deterministic metric rankings and time-series overlays.

Model cascading failure propagation across interconnected components over a 20-step (600s) window. Captures retry storm amplification, thundering herd recovery spikes, and per-depth cascade attenuation. Quantifies how quickly an initial failure fraction saturates the entire system and how long recovery takes.

Project how RPS, technical debt, team capacity, infrastructure cost, and migration pressure compound over 1–5 years. Captures debt accumulation under load, remediation diminishing returns at high debt, and the composite migration urgency index that signals when an architectural transition becomes unavoidable.

Track how team maturity, on-call burden, feature velocity, and migration readiness evolve over 6–24 months. Models burnout threshold at 40h/engineer/month (maturity growth stalls), diminishing-returns training curves, and the composite readiness score required to safely execute a major architectural migration.

Simulate how compliance violations accumulate, propagate, and respond to remediation over 6–36 months. Tracks compliance score decay driven by violation discovery rate vs. remediation capacity, audit risk spikes at scheduled audit windows, and composite governance pressure combining compliance deficit, audit risk, and backlog.

Model the 4-phase migration lifecycle (Preparation → Execution → Validation → Cutover) over 4–52 weeks. Phase durations vary by migration type (monolith→microservices, postgres→sharding, sync→eventual). Captures dual-write overhead peaks, risk escalation at cutover, parallelism benefits, and rollback capability penalties.