Jina AI Cloud Hosting#


After building a Jina project, the next step is to deploy and host it on the cloud. Jina AI Cloud is Jina’s reliable, scalable and production-ready cloud-hosting solution that manages your project lifecycle without surprises or hidden development costs.


Are you ready to unlock the power of AI with Jina AI Cloud? Take a look at our pricing options now!


Jina AI Cloud provides a CLI that you can use via jina cloud from the terminal (or jcloud or simply jc for minimalists.)


You can also install just the JCloud CLI without installing the Jina package.

pip install jcloud
jc -h

If you installed the JCloud CLI individually, all of its commands fall under the jc or jcloud executable.

In case the command jc is already occupied by another tool, use jcloud instead. If your pip install doesn’t register bash commands for you, you can run python -m jcloud -h.

For the rest of this section, we use jc or jcloud. But again they are interchangable with jina cloud.


In Jina’s idiom, a project is a Flow, which represents an end-to-end task such as indexing, searching or recommending. In this document, we use “project” and “Flow” interchangeably.


Flows have a maximum lifetime after which they are automatically deleted.

A Flow can have two types of file structure: a single YAML file or a project folder.

Single YAML file#

A self-contained YAML file, consisting of all configuration at the Flow-level and Executor-level.

All Executors’ uses must follow the format jinaai+docker://<username>/MyExecutor (from Executor Hub) to avoid any local file dependencies:

# flow.yml
jtype: Flow
  - name: sentencizer
    uses: jinaai+docker://jina-ai/Sentencizer

To deploy:

jc deploy flow.yml


We recommend testing locally before deployment:

jina flow --uses flow.yml

Project folder#


The best practice for creating a Jina AI Cloud project is to use:

jc new

This ensures the correct project structure that is accepted by Jina AI Cloud.

Just like a regular Python project, you can have sub-folders of Executor implementations and a flow.yml on the top-level to connect all Executors together.

You can create an example local project using jc new hello. The default structure looks like:

├── .env
├── executor1
│   ├── config.yml
│   ├── executor.py
│   └── requirements.txt
└── flow.yml


  • hello/ is your top-level project folder.

  • executor1 directory has all Executor related code/configuration. You can read the best practices for file structures. Multiple Executor directories can be created.

  • flow.yml Your Flow YAML.

  • .env All environment variables used during deployment.

To deploy:

jc deploy hello

The Flow is successfully deployed when you see:


You will get a Flow ID, say merry-magpie-82b9c0897f. This ID is required to manage, view logs and remove the Flow.

As this Flow is deployed with the default gRPC gateway (feel free to change it to http or websocket), you can use jina.Client to access it:

from jina import Client, Document

        on='/', inputs=Document(text='hello')

Get status#

To get the status of a Flow:

jc status merry-magpie-82b9c0897f


Basic monitoring is provided to Flows deployed on Jina AI Cloud.

To access the Grafana-powered dashboard, first get the status of the Flow. The Grafana Dashboard link is displayed at the bottom of the pane. Visit the URL to find basic metrics like ‘Number of Request Gateway Received’ and ‘Time elapsed between receiving a request and sending back the response’:


List Flows#

To list all of your “Serving” Flows:

jc list

You can also filter your Flows by passing a phase:

jc list --phase Deleted

Or see all Flows:

jc list --phase all

Remove Flows#

You can remove a single Flow, multiple Flows or even all Flows by passing different identifiers.

To remove a single Flow:

jc remove merry-magpie-82b9c0897f

To remove multiple Flows:

jc remove merry-magpie-82b9c0897f wondrous-kiwi-b02db6a066

To remove all Flows:

jc remove all

By default, removing multiple or all Flows is an interactive process where you must give confirmation before each Flow is deleted. To make it non-interactive, set the below environment variable before running the command:


Update Flow#

You can update a Flow by providing an updated YAML.

To update a Flow:

jc update super-mustang-c6cf06bc5b flow.yml

Pause / Resume Flow#

You have the option to pause a Flow that is not currently in use but may be needed later. This will allow the Flow to be resumed later when it is needed again by using resume.

To pause a Flow:

jc pause super-mustang-c6cf06bc5b

To resume a Flow:

jc resume super-mustang-c6cf06bc5b

Restart Flow, Executor or Gateway#

If you need to restart a Flow, there are two options: restart all Executors and the Gateway associated with the Flow, or selectively restart only a specific Executor or the Gateway.

To restart a Flow:

jc restart super-mustang-c6cf06bc5b

To restart the Gateway:

jc restart super-mustang-c6cf06bc5b --gateway

To restart an Executor:

jc restart super-mustang-c6cf06bc5b --executor executor0

Recreate a Deleted Flow#

To recreate a deleted Flow:

jc recreate profound-rooster-eec4b17c73

Scale an Executor#

You can also manually scale any Executor.

jc scale good-martin-ca6bfdef84 --executor executor0 --replicas 2

Get Executor or Gateway logs#

To get the Gateway logs:

jc logs --gateway central-escargot-354a796df5

To get the Executor logs:

jc logs --executor executor0 central-escargot-354a796df5


Please refer to Configuration for configuring the Flow on Jina AI Cloud.


Jina AI Cloud scales according to your needs. You can demand different instance types with GPU/memory/CPU predefined based on the needs of your Flows and Executors. If you have specific resource requirements, please contact us on Discord or raise a GitHub issue.


  • Deployments are only supported in the us-east region.