React Native Camera: Expo vs VisionCamera — What You Need to Know

We're now in the world of AI development (aka vibe coding), and everyone is now wanting to deploy new apps that before they couldn't do to either time constraints or education limitations. I've built out a couple of mobile apps in the past, one I'm actively working on to help train hockey players, and I learned very quickly about performance bottlenecks simply because I chose the wrong stack to build with.

When building mobile applications with React Native, accessing the camera is often a key requirement. Whether you're capturing photos, scanning barcodes, or running real-time AI inference on video frames, the choice of camera library can make or break your app’s performance.

There are two primary approaches:

• Use the expo-camera module (for managed workflow projects)

• Use react-native-vision-camera (for bare workflow/native modules)

In this article, we’ll break down the pros and cons of each option and help you decide which one is right for your project.

🧪 Quick Comparison

🚀 Option 1: expo-camera

✅ Why Use It?

• You're building a quick prototype or MVP

• You want to stay inside the Expo Managed Workflow

• Your needs are simple: take photos, record video, maybe scan a barcode

❌ Why Avoid It?

• You don’t get direct access to raw video frames

• Advanced camera controls are absent

• Real-time ML tasks (e.g., object detection, image classification) are slow or unsupported

📦 Installation (Expo)

npx expo install expo-camera

🔧 Basic Usage

import { Camera } from 'expo-camera';

const MyCamera = () => {
  const [permission, requestPermission] = Camera.useCameraPermissions();

  if (!permission?.granted) {
    return <Button onPress={requestPermission} title="Grant Camera Access" />;
  }

  return <Camera style={{ flex: 1 }} />;
};

🔥 Option 2: react-native-vision-camera

If you want full control of the camera and need performance, this is the tool to use.

✅ Why Use It?

• You need fast access to camera frames for AI or AR.

• You want to integrate advanced features (HDR, frame rate control, manual focus)

• You’re okay configuring native modules

❌ Why Avoid It?

• You need a managed Expo workflow

• You’re avoiding native builds entirely

📦 Installation

npm install react-native-vision-camera
npx pod-install

Note: You must also configure native permissions for Android and iOS, and request runtime permissions.

⚙️ Sample Code

import { Camera, useCameraDevices } from 'react-native-vision-camera';

const MyCamera = () => {
  const devices = useCameraDevices();
  const device = devices.back;

  if (device == null) return <LoadingSpinner />;

  return (
    <Camera
      style={{ flex: 1 }}
      device={device}
      isActive={true}
    />
  );
};

🧠 VisionCamera Frame Processors

This is where vision-camera really shines. You can tap into every video frame and run real-time tasks like face detection, QR scanning, or AI inference using TFLite or custom native modules.

const frameProcessor = useFrameProcessor((frame) => {
  'worklet';
  const faces = scanFaces(frame); // Native plugin
  console.log(faces);
}, []);

🧠 Technical Breakdown: Why VisionCamera Is Superior

When performance, flexibility, or advanced use cases are involved, react-native-vision-camera clearly outperforms expo-camera. Here's why:

1. Frame Access & Native Performance

• expo-camera limitation: Does not expose raw camera frames to JavaScript or native modules.

  • Result: No real-time ML, AR, or video filters.

• react-native-vision-camera advantage: Offers Frame Processors, written in C++/Java/Objective-C and executed on a dedicated thread, not the JS thread.

  • Result: Run real-time computer vision tasks (e.g. object detection, barcode scanning) with low latency and zero dropped frames.

2. Direct Access to Native Camera APIs

• Expo wraps native APIs and only exposes what the team provides.

• VisionCamera interfaces directly with CameraX (Android) and AVCaptureSession (iOS).

  • You get:

    • Full manual control over ISO, shutter speed, zoom, focus, white balance.

    • Support for ultra-wide lenses, telephoto, HDR, depth sensors.

    • Fine-grain access to stream formats like raw or YUV.

3. No JavaScript Thread Bottlenecks

• expo-camera sends preview frames and camera actions over the JS bridge:

  • This introduces lag and frame drops.

• VisionCamera:

  • Uses GPU-backed native rendering for the preview.

  • Fully decouples camera logic from the JS thread.

  • Preview stays fluid even while running compute-heavy tasks in parallel.

4. Plugin Ecosystem & Custom Native Extensions

• VisionCamera supports a modular plugin system, enabling:

  • Custom Frame Processors (e.g., scanFaces, detectBarcodes, trackMotion).

  • Direct integration with TensorFlow Lite, MediaPipe, or even OpenCV.

  • Native code execution inside the camera pipeline for peak performance.

5. Real-Time ML/AI Processing

• AI camera apps demand low latency.

• With Expo:

  • Capture → JS bridge → process in JS → render = 🚫 too slow.

• With VisionCamera:

  • Frames processed natively within 2–5ms.

  • Consistent 30–60 FPS inference supported.

  • Enables use cases like gesture detection, object recognition, pose estimation, etc.

🔧 TL;DR – If You're Doing Anything Advanced:

🚀 Conclusion

If you need a basic camera for photos or videos and want fast setup, expo-camera is fine.

But for serious apps — especially those involving AI, AR, advanced camera controls, or high-performance UX — react-native-vision-camera is the right tool.

⚠️ Just be ready to go beyond Expo Go and configure native builds — it's worth it.

💡 Final Recommendation

Use Case Library Simple photo/video features, fast setup expo-camera Real-time ML, AR, advanced controls needed react-native-vision-camera Need to stay inside Expo Go expo-camera Willing to eject for better performance react-native-vision-camera

🧱 Wrap-Up

This is obviously written specifically for those that are working on an active project in Gauntlet AI Cohort 2. If any of you have any questions on this, feel free to hmu.

🧰 Helpful Links

• VisionCamera GitHub

• Expo Camera Docs

• VisionCamera Frame Processor Docs

Let me know if you want to dive deeper into performance benchmarks or set up a real-time ML example using VisionCamera + TensorFlow Lite!