Simulator
Multi-fidelity prediction engine
The Simulator is the prediction engine at the heart of Inflight. It lets you test configuration changes before deploying them, using models built from your actual production data. No more guessing—know exactly what will happen before you make a change.
Why Simulation Matters
Traditional performance tuning is trial and error. The Simulator transforms it into a predictable, low-risk process.
Zero Risk Testing
Test any configuration change without touching production systems
Multi-Fidelity
Four simulation modes from fast statistical to full discrete event simulation
Continuous Learning
Models continuously calibrate from your production data
The Prediction Gap
Before Inflight, predicting the impact of configuration changes meant either risky production deployments or synthetic tests that don't reflect reality:
Without Simulation
- Trial and error in production
- Load tests that don't match reality
- Unexpected production incidents
- Conservative changes due to fear
With the Simulator
- Predict outcomes before deployment
- Models built from real production data
- Safe exploration of optimization space
- Confident, data-driven decisions
Multi-Fidelity Simulation
Not every prediction needs the same level of detail. The Simulator automatically selects the appropriate fidelity based on the change complexity and required confidence.
STATISTICAL
Quick predictions using statistical models for simple, well-understood changes.
Best for: Minor parameter adjustments with ample historical data
HYBRID
Combines statistical models with targeted simulation for balanced accuracy and speed.
Best for: Moderate changes where some simulation adds confidence
FULL
Complete discrete event simulation for maximum accuracy on complex scenarios.
Best for: Major changes, new configurations, or high-stakes decisions
DEGRADED
Provides predictions with reduced confidence when data or time is limited.
Best for: New services or when calibration data is incomplete
Automatic Fidelity Escalation
When a lower fidelity mode produces low-confidence results, the system automatically escalates to a higher fidelity mode for better accuracy.
Core Capabilities
The Simulator provides sophisticated prediction capabilities while remaining easy to use and understand.
Production-Based Models
Unlike synthetic load testing, the Simulator builds models from your actual production traffic patterns, workload characteristics, and resource usage.
Automatic Model Calibration
Models continuously learn from production data, automatically recalibrating when your application behavior changes.
Safety Validation
Every simulation runs through platform-aware safety checks that understand Kubernetes limits, cloud provider quotas, and runtime constraints.
Backtesting Validation
Predictions are validated against historical data to ensure accuracy. You can see how well the model would have predicted past scenarios.
What Gets Predicted
The Simulator predicts comprehensive impact across multiple dimensions:
Performance Impact
- Response time changes (p50, p95, p99)
- Throughput predictions
- Latency distribution shifts
- Error rate projections
Resource Utilization
- Memory consumption changes
- CPU utilization impact
- GC pause time predictions
- Container resource usage
Stability Assessment
- OOM risk evaluation
- Throttling probability
- Restart likelihood
- Contention predictions
Confidence Metrics
- Model confidence scores
- Data quality indicators
- Prediction uncertainty
- Calibration quality
Safety Verdicts
Every simulation produces a clear verdict to guide your decision:
APPROVED
All safety thresholds met. Win probability exceeds requirements. Safe to deploy.
WARNING
Some risk factors detected. May work but validate in staging first.
REJECT
Critical issues detected. Configuration change will not achieve intended outcome.
Model Governance
Trust requires transparency. The Simulator provides complete visibility into how predictions are made:
Calibration Transparency
See which data was used to calibrate models and when
Accuracy Tracking
Historical accuracy metrics for model predictions
Version History
Complete audit trail of model changes over time
Parameter Priors
Hierarchical defaults ensure sensible starting points
Ready to Predict Before You Deploy?
Stop guessing and start knowing. See how the Simulator validates configuration changes for your services.