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The Edge SDK lets you export a trained decision type as a standalone bundle and run it locally — on-device, on-premise, or in air-gapped environments. Zero network calls, zero cloud dependencies.

How It Works

A Sparkient edge bundle contains everything needed to make decisions offline:

Component	Purpose
ONNX model	The compiled ML classifier
CEL rules	Hard rules, evaluated first
Feature config	How to extract features from input
Metadata	Decision type name, options, version

The entire bundle is a single ZIP file, typically 1–5 MB.

Export a Bundle

Download edge bundle

curl -X GET https://api.sparkient.ai/api/v1/decision-types/{id}/export \
  -H "Authorization: Bearer YOUR_API_KEY" \
  -o my_decision_type.zip

Use in Python

Edge inference

from edge import load_bundle, EdgePredictor

# Load the exported bundle
predictor = EdgePredictor.from_bundle("my_decision_type.zip")

# Make a decision (< 10ms, no network)
result = predictor.predict({
    "text": "Check out this product",
    "user_score": 0.85,
    "link_count": 1
})

print(result.decision)     # "approve"
print(result.confidence)   # 0.94
print(result.reason_codes) # ["safe_content"]
print(result.latency_ms)   # 3.2

Dependencies

The edge predictor requires only:

onnxruntime
cel-python
numpy

No FastAPI, no database, no Redis, no cloud SDKs. It's designed for constrained environments.

Use Cases

On-device inference — Mobile apps, IoT devices, embedded systems
Air-gapped environments — Government, military, healthcare systems without internet
Ultra-low latency — Eliminate network round-trip for sub-10ms decisions
Cost optimization — Zero API calls after the initial bundle download
Offline-first applications — Apps that need to work without connectivity

Updating

When you retrain and deploy a new model, export a new bundle and replace the old one. The edge predictor loads the latest bundle on initialization.

Edge Deployment