> ## Documentation Index
> Fetch the complete documentation index at: https://landinglens.docs.landing.ai/llms.txt
> Use this file to discover all available pages before exploring further.
# Deployment Options
export const vp = 'Visual Prompting';
export const smartLabel = 'Smart Labeling';
export const productLL = 'LandingLens';
export const mi = 'Mobile Inference';
export const llsf = 'LandingLens on Snowflake';
export const companyName = 'LandingAI';
*This article applies to these versions of LandingLens:*
| LandingLens |
LandingLens on Snowflake |
| ✓ |
✓ |
After you are happy with the results of your trained model, you are ready to use it! To use a model, you **deploy** it, which means you put the model in a virtual location so that you can then upload images to it. When you upload images, the model runs **inferences**, which means that it detects what it was trained to look for.
## Deployment Options
There are a few ways to deploy your LandingLens model:
* [Cloud Deployment](./cloud-deployment): Deploy your model to a virtual environment hosted within the LandingLens app. Use API calls or [{mi}](./mobile-inference) to send images to your model. If using {llsf}, the LandingLens app exists within your Snowflake environment, so the model will be hosted in Snowflake.
* [LandingAI Deploy Docker](./landingedge/docker-deploy): Download our Docker image to create a Dockerized container. Deploy your model and run inference in this self-hosted container. You must first [activate](./activate-projects) a project to deploy its models with Docker.
* [LandingEdge](https://support-landingai.document360.io/docs/landingedge-overview): Use the LandingEdge application to communicate with edge devices, industrial cameras, and programmable logic controllers (PLCs). You must first [activate](./activate-projects) 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, {companyName} 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](mailto: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](./plans#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 {vp} is [Cloud Deployment](./cloud-deployment).