Your AI model works in the cloud. Does it work on device?
AI models that pass cloud tests silently break on real hardware — thermal throttling, firmware quirks, and quantization drift cause failures you'll only discover in production. EdgeGate catches them in CI.
From model upload to hardware-validated CI
Watch how EdgeGate catches on-device regressions before they reach production — in under 60 seconds.
Edge AI breaks in ways you can't test for — until now
Emulators and cloud GPUs can't replicate what happens on a real Snapdragon device in the field.
Cloud tests lie
Your model scores 95% accuracy on cloud GPUs. On a Snapdragon chipset running at 40°C? It drops to 71%. Cloud benchmarks don’t predict on-device behavior.
Hardware is unpredictable
Thermal throttling, firmware updates, and power states change how your model runs. These variables don’t exist in simulation — only on real silicon.
Regressions ship silently
A weight update that looks fine in your training pipeline quietly degrades latency by 3x on device. Without hardware-in-the-loop CI, you won’t know until users complain.
From git push to hardware-validated in minutes
Add hardware regression testing to your existing CI/CD pipeline. No infrastructure to manage.
Push your model
Upload your ONNX model and create a pipeline in the dashboard. Set pass/fail gates for inference time and peak memory on your target device.
# Pipeline config (via Dashboard or API)
model: resnet18_fp32.onnx
format: onnx # embedded weights
gates:
inference_time_ms: "<=1.0"
peak_memory_mb: "<=150"
device: sm8650 # Samsung Galaxy S24Test on real hardware
EdgeGate runs your model on physical Snapdragon devices through Qualcomm AI Hub. No emulators. Median-of-N measurements with warmup exclusion for deterministic results.
[EdgeGate] Device: Samsung Galaxy S24 (SM8650)
[EdgeGate] Compiling via Qualcomm AI Hub...
[EdgeGate] Profiling on-device (median-of-N)
[EdgeGate] Inference: 0.176ms ✓ (gate: ≤1.0ms)
[EdgeGate] Peak memory: 121.51 MB ✓ (gate: ≤150 MB)
[EdgeGate] Model size: 1.07 MB (270,146 params)Gate your PR
Results flow back to your CI pipeline as a pass/fail gate. Failed gates block the merge. Every run produces a signed evidence bundle with SHA-256 hashes for auditability.
✓ 2/2 GATES PASSED — PR #247 can merge
Evidence bundle: dc2e9f67
model_hash: sha256:4f8a2c...
signed: Ed25519 (workspace key)
device: SM8650 (Samsung Galaxy S24)
inference: 0.176ms | memory: 121.51 MBEverything you need to ship edge AI with confidence
Purpose-built for teams deploying AI models to Snapdragon-powered devices.
Emulators miss thermal, firmware, and power-state behavior
Real Snapdragon Devices
Test on a fleet of physical Snapdragon chipsets through Qualcomm AI Hub. Capture real-world latency, accuracy, and thermal behavior that emulators can’t reproduce.
Flaky tests erode trust in your CI pipeline
Deterministic Gating
Warmup exclusion, median-of-N repeats, and built-in flake detection ensure your pass/fail gates are reliable. No more re-running tests hoping for a green build.
Hardware testing is a manual, out-of-band process
CI/CD Native
Drop EdgeGate into your existing GitHub Actions or GitLab CI workflow. One YAML file. Results appear as PR checks. Failed gates block the merge automatically.
No audit trail for on-device test results
Signed Evidence Bundles
Every run produces a cryptographically signed evidence bundle — SHA-256 hashes and Ed25519 signatures. Prove to your team (and regulators) that the model was validated on real hardware.
You don’t know what metrics each device supports
ProbeSuite Discovery
ProbeSuite automatically discovers which capabilities, metrics, and profile keys are available on each Snapdragon device. No manual guesswork about what you can measure.
Teams step on each other’s device reservations
Multi-Tenant Workspaces
Isolated workspaces with role-based access (Owner, Admin, Viewer). Each workspace gets its own device queue, secrets vault with envelope encryption, and run history.
Unprecedented
Performance Insights
Visualize your model's performance delta across different firmware versions, temperatures, and battery states. Our dashboard provides a granular view that emulators simply cannot match.
- done_allInference Latency (Median-of-N Gating)
- done_allPeak Memory vs. Gate Threshold
- done_allFP32 vs INT8 Model Comparison
For teams shipping AI to real devices
Whether you're building robots, drones, smart cameras, or mobile AI features — if it runs on Snapdragon, EdgeGate is your regression safety net.
ML Engineers
You train and optimize models for edge deployment. EdgeGate lets you validate that your INT8 quantization actually works on target hardware before merging.
- checkModel quantization validation
- checkAccuracy regression checks
- checkCross-device compatibility testing
Embedded / IoT Engineers
You build firmware and applications for Snapdragon-powered devices. EdgeGate catches latency regressions and thermal issues your desktop benchmarks miss.
- checkLatency gate enforcement
- checkThermal throttling detection
- checkFirmware update impact testing
DevOps / ML Platform Teams
You own the CI/CD pipeline. EdgeGate plugs into GitHub Actions or GitLab CI with one YAML file and gives you deterministic hardware gates.
- checkCI/CD integration in minutes
- checkHMAC-signed webhook triggers
- checkSigned evidence for audit trails
Industries using EdgeGate
Stop shipping blind to hardware
Join the waitlist for early access. Be the first to add hardware regression gates to your CI pipeline.