| LandingLens | LandingLens on Snowflake |
|---|---|
Deployment Options
There are a few ways to deploy your LandingLens model:- Cloud Deployment: Deploy your model to a virtual environment hosted within the LandingLens app. Use API calls or to send images to your model. If using , the LandingLens app exists within your Snowflake environment, so the model will be hosted in Snowflake.
- LandingAI Deploy Docker: Download our Docker image to create a Dockerized container. Deploy your model and run inference in this self-hosted container. You must first activate a project to deploy its models with Docker.
- LandingEdge: Use the LandingEdge application to communicate with edge devices, industrial cameras, and programmable logic controllers (PLCs). You must first activate a project to deploy its models with LandingEdge.
| Attribute | Cloud Deployment | Container Deployment | LandingEdge |
|---|---|---|---|
| Latency | High | Low | Low |
| Throughput | Configurable | 30 FPS | 30 FPS |
| Pricing | Per inference | Free | Free |
For Enterprise users, can hide the LandingEdge and Docker deployment options if you don’t want your organization to deploy using these methods. To have these disabled, contact support@landing.ai.
When to Use Cloud Deployment
Cloud Deployment is a scalable and cost-effective deployment solution. It can accommodate surges in inference traffic up to a configurable rate limit, with charges incurred per inference. Cloud Deployment is a preferred option for managing variable inference loads. Use Cloud Deployment if you:- Want to start running inference without purchasing GPU machines or managing deployments.
- Have a good network connectivity from your inferencing point to the cloud.
When to Use Docker Deployment
Docker Deployment is the most flexible deployment option for developers that build mission-critical solutions or process high-throughput continuous inference loads. It can be deployed in your private cloud, on-premises, or at the edge. Use Docker Deployment if you:- Have a deployment infrastructure and want to add inferencing capabilities to it.
- Are looking for deployment automation in a container-based infrastructure.
When to Use LandingEdge
LandingEdge is an application that lets you deploy to an edge computer, such as an industrial PC. Use LandingEdge if you:- Want to build machine vision solutions using specialized hardware, like industrial cameras and PLCs.
- Want to build mission-critical solutions at the edge.
Compare Deployment Options
Use the table below as a reference when choosing a deployment option.| Feature | Cloud Deployment | Docker | LandingEdge |
|---|---|---|---|
| General | |||
| Hosting | LandingLens-hosted | Self-hosted | Self-hosted |
| Operating system | Linux, Mac, Windows | Any | Linux, Windows |
| Can run inference when not connected to the internet | ✖ | ✓ | ✓ |
| Can run inference on Visual Prompting models | ✓ | ✖ | ✖ |
| Can see live results in the user interface | ✓ | ✖ (there is no user interface) | ✓ |
| Can upload image metadata to images | ✓ | ✓ | ✓ |
| Maximum inference calls per minute | 40 | Depends on system | Depends on system |
| Can communicate with PLCs | ✖ | ✖ | ✓ |
| Can deploy on NVIDIA Jetson devices | ✖ | ✓ | ✖ |
| Can deploy on ARM64 processors | ✖ | ✓ | ✖ |
| Running inference consumes credits | ✓ | ✖ | ✖ |
| Send Images for Inference | |||
| Drag and drop images | ✓ | ✖ | ✓ |
| GenICam | ✖ | ✖ | ✓ |
| Images from webcam | ✓ | ✖ | ✖ |
| Video (will convert to images) | ✓ (via Python library) | ✖ | ✓ |
| Select from a designated folder (folder watcher) | ✖ | ✖ | ✓ |
| Send images via POST APIs | ✓ | ✓ | ✓ |
| Post-Inference Features | |||
| Apply post-processing scripts | ✓ (via Python library) | ✓ (via Python library) | ✓ |
| Can view inferenced images & predictions in LandingLens | ✓ | ✓ (must pass --upload flag) | ✓ (must enable “Upload results to LandingLens”) |
| Can save inferenced images to a local folder | ✖ | ✖ | ✓ |
The only deployment option for is Cloud Deployment.