All 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 freeRead the guide

For teams building AI products and internal AI services.

Application deliveryIllustrative workflow
my-application / main Ready to deploy
  1. Read the code01
  2. Build & test02
  3. Scan the image03
  4. Deploy the app04
https://app.example.com
PostgreSQL connected

Why it matters

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.

How it works

AI workloads connected to an operational platform.

  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

What it runs on

The application and its supporting stack, managed together.

Related use cases

Services, infrastructure choices and an 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.

Start freeTry a 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