This content originally appeared on DEV Community and was authored by Md Mahbubur Rahman
Quick Verdict (TL;DR)
| Use Case | Best Choice | Why |
|---|---|---|
| Browser extension / Web-based AI |  ONNX Runtime Web
|
Faster, WebAssembly backend, works in all browsers, supports more models, no special conversion steps |
| Mobile app / Electron app / native desktop | TensorFlow Lite
|
Designed for native edge devices (Android, iOS, Raspberry Pi, etc.) |
| General-purpose local AI for multiple environments (browser + backend) | ONNX Runtime (Web + Node + Python) |
Same model across environments — “write once, run anywhere” |
| Tiny in-browser inference (<100 MB, no backend) | ONNX Runtime Web
|
Smaller footprint, simple setup, no GPU drivers |
| Hardware-optimized inference (GPU, NNAPI, CoreML) | TensorFlow Lite
|
Deep optimization for edge hardware accelerators |
Detailed Comparison
| Feature | TensorFlow Lite (TFLite) | ONNX Runtime Web (ORT-Web) |
|---|---|---|
| Target Platform | Primarily mobile / embedded | Browser, Node.js, Python, C++ |
| Browser Support | Indirect (requires TF.js bridge) | Direct WebAssembly & WebGPU |
| Model Conversion | Convert .pb / .keras → .tflite
|
Convert from any major framework → .onnx
|
| Supported Models | TensorFlow-trained models only | PyTorch, TF, Scikit, HuggingFace, etc. |
| Performance | Great on Android/iOS (NNAPI/CoreML) | Excellent on desktop browsers (WASM SIMD / WebGPU) |
| GPU Acceleration (Browser) |  Limited / experimental |
WebGPU + WebGL |
| Model Size / Load Time | Usually smaller, quantized | Slightly larger, but flexible |
| Ease of Setup (Firefox) | Harder — needs TF.js shim | Simple <script> or npm import |
| Community Trend (2025) | Declining for web use |  Rapidly growing, backed by Microsoft + HuggingFace |
| APIs |
Interpreter (low-level) |
InferenceSession.run(inputs) (modern) |
Real-World Developer Experience
For browser-based plugins like MindFlash:
import * as ort from 'onnxruntime-web';
const session = await ort.InferenceSession.create('model.onnx');
const results = await session.run(inputs);
Works offline and cross-platform.
Minimal setup, perfect for WebExtensions.
TensorFlow Lite is better for native mobile or IoT apps, not browser extensions.
Future-Proofing for All Projects
| Project Type | Recommended Runtime |
|---|---|
| Firefox / Chrome / Edge Extension | ONNX Runtime Web |
| Electron Desktop App | ONNX Runtime Node |
| Native Mobile (Android/iOS) | TensorFlow Lite |
| Local Server or API Backend | ONNX Runtime Python / C++ |
| IoT Edge Device (Raspberry Pi, Jetson) | TensorFlow Lite or ONNX Runtime C++ |
Model Conversion Workflow
# PyTorch → ONNX
torch.onnx.export(model, dummy_input, "model.onnx")
# TensorFlow → TFLite
tflite_convert --saved_model_dir=saved_model --output_file=model.tflite
# Quantize ONNX
python -m onnxruntime.quantization.quantize_dynamic model.onnx model_int8.onnx
Privacy + Offline Advantage
ONNX Runtime Web runs entirely in the browser sandbox, never sends webpage data to any server — ideal for privacy-focused extensions like MindFlash.
Final Recommendation
For Firefox / Chrome / Edge AI plugins → ONNX Runtime Web
For native apps → TensorFlow Lite
This content originally appeared on DEV Community and was authored by Md Mahbubur Rahman