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# Run AI applications & services - Ankra Use Cases

Canonical: https://ankra.ai/use-cases/ai-workloads

Deploy AI applications, private model gateways and supporting services on the infrastructure your team chooses.

[All use cases](https://ankra.ai/use-cases)

Run AI applications & services

# Build with AI. Run the services behind it.

Deploy AI applications, private model gateways and supporting services on the infrastructure your team chooses.

[Start with Ankra](https://platform.ankra.app/auth/signup)  [Read the guide](https://docs.ankra.ai/concepts/applications)

For teams building ai products and internal ai services

The work to take off your team

## An AI application still needs deployment, credentials, data services and operations.

Use Ankra to deploy the application and its supporting stack, including gateways such as LiteLLM or vLLM where your infrastructure supports them. Keep endpoints and credentials managed alongside the environment.

With Ankra

### AI workloads connected to an operational platform.

Prepare the application  Choose the infrastructure  Connect the dependencies  Operate the service

A practical path

## How this works with Ankra.

1. 01

  ### Prepare the application

  Register the repository or compose a service from its Helm chart and manifests.
2. 02

  ### Choose the infrastructure

  Select a cluster with the capacity, hardware and location the workload needs.
3. 03

  ### Connect the dependencies

  Reuse gateway credentials, database endpoints and storage access through scoped secrets.
4. 04

  ### Operate the service

  Follow deployments, logs, resource use and security findings with your team and its agents.

 [Follow the product guide](https://docs.ankra.ai/concepts/applications)

The platform behind the workflow

## The pieces, already connected.

[### Applications Turn a Git repository into a deployable application. Ankra reads your code and opens the setup for review.](https://ankra.ai/features#applications)

[### Databases & platform services Deploy open source PostgreSQL, caches, monitoring, storage and private AI gateways, with agents helping investigate and operate the stack.](https://ankra.ai/features#services)

[### Metrics, logs & health Understand the application and the cluster underneath it, with resource views, metrics and logging integrations.](https://ankra.ai/features#observability)

What comes next?

## Keep building from here.

[Build & ship ### Applications and their services Run PostgreSQL, caches, monitoring and other platform services alongside the applications that use them.](https://ankra.ai/use-cases/databases-and-services)

[Grow & move ### Multi-cloud & private infrastructure Use one operational platform across public clouds, managed Kubernetes and private servers.](https://ankra.ai/use-cases/multi-cloud)

[Operate & protect ### An AI platform engineer Agents that read live cluster state, fix what breaks and wait for your approval. Use Ankra's AI team, or connect Claude, Cursor or your own agent over MCP.](https://ankra.ai/use-cases/ai-platform-engineer)

Your next chapter

## You built it.
*Let’s get it live.*

Give your application a path to production.
 Give your team a simpler way to run it.

[Get your app live](https://platform.ankra.app/auth/signup)  [Try a playground](https://ankra.ai/playground)

First 30 vCPU of platform usage included.
Try it without a card; a card unlocks full access and €59 of credit.

From something you built
to something people use.

- Deploy from your repository
- Run on infrastructure you choose
- Operate with your team and agents