> ## Documentation Index
> Fetch the complete documentation index at: https://docs.springwinter.dev/llms.txt
> Use this file to discover all available pages before exploring further.

# Autovid cut infrastructure cost 10% and deploys 3x more often

> Autovid moved video-production and AI workflows from Kubernetes to Springwinter, cutting infrastructure cost by 10% while deploying 3x more often.

Autovid runs a large number of video-production and AI workflows. Kubernetes could run the containers, but the team still had to maintain the platform around them: cluster configuration, deployment manifests, release pipelines, service wiring, and observability. That plumbing sat between the team and the FFmpeg, open-source video systems, AI workers, and Temporal workflows that produced the actual product.

Autovid moved those workloads to Springwinter in its AWS account. Infrastructure cost fell by 10% without materially changing workload behavior or reliability. The team now deploys 3x more often than it did before the move.

## Before

Video production is not one service. Autovid had APIs accepting work, workers processing media with FFmpeg, open-source video-production systems, AI jobs, and Temporal coordinating long-running workflows. Kubernetes scheduled those containers, but operating Kubernetes became another system the team had to build around.

Every new component brought more platform work: manifests, service configuration, secrets, deployment automation, logs, metrics, and the glue required to make one workload discover another. The team could run the software, but each release carried infrastructure work that was separate from improving the video product.

<CardGroup cols={2}>
  <Card title="Kubernetes plumbing" icon="boxes-stacked">
    The team maintained the deployment and operational layer around every workload.
  </Card>

  <Card title="Video processing" icon="film">
    FFmpeg and open-source video systems handled media work across many workers.
  </Card>

  <Card title="AI workflows" icon="sparkles">
    AI processing added more long-running jobs and services to operate.
  </Card>

  <Card title="Temporal" icon="diagram-project">
    Temporal coordinated workflows, while the team maintained the services around it.
  </Card>
</CardGroup>

## What moved

Autovid did not need Springwinter to translate a Kubernetes cluster. The team deployed the systems it already used as containers: web services for APIs, workers for FFmpeg and AI processing, open-source video-production components, and the services and workers used by Temporal.

[Web servers](/deploy/web-servers) and [workers](/deploy/workers) build from GitHub and run as ECS services in Autovid's AWS account. Supporting [databases](/deploy/databases), [caches](/deploy/caches), and storage stay in the same account. The workload remains made of the open-source tools Autovid chose; Springwinter handles the repeated path from repository to running AWS resource.

## Less platform work

A deployment now starts from the repository instead of a set of cluster-specific release steps. Springwinter runs the build, updates the service, and keeps the deployment record together with its output. A branch can run as a [preview](/concepts/previews) before replacing a live service.

[Logs](/observability/logs) and [metrics](/observability/metrics) come from the AWS resources where the workloads run. [Cost](/observability/cost) is visible beside those resources. The team no longer has to assemble a separate operational path for every FFmpeg worker, AI process, or open-source service it adds.

<Steps>
  <Step title="Keep the workload">
    Continue using FFmpeg, open-source video-production software, AI workers, and Temporal as containerized systems.
  </Step>

  <Step title="Leave the cluster plumbing">
    Replace Kubernetes-specific deployment and operational work with Springwinter resources in the existing AWS account.
  </Step>

  <Step title="Deploy from GitHub">
    Build and update web services and workers from their repositories, with previews available for branch changes.
  </Step>

  <Step title="Operate from one project">
    Read deployments, logs, metrics, and cost beside the resources that produce them.
  </Step>
</Steps>

## The result

Autovid reduced infrastructure cost by 10% without materially affecting how its production workloads behaved or their reliability. Removing deployment plumbing also changed how quickly the team could ship: it now makes 3x as many deployments as it did on Kubernetes.

The gain did not come from replacing FFmpeg, Temporal, or the open-source video stack. It came from keeping those systems while removing the platform work that had accumulated around them.

<CardGroup cols={2}>
  <Card title="10% lower cost" icon="arrow-trend-down">
    Infrastructure cost fell without a material service impact.
  </Card>

  <Card title="3x more deployments" icon="rocket">
    The team deploys three times as often as it did before.
  </Card>

  <Card title="Workers" icon="gears" href="/deploy/workers">
    Run FFmpeg, AI processing, and workflow workers in the AWS account.
  </Card>

  <Card title="Logs and metrics" icon="activity" href="/observability/logs">
    Read workload output and operating signals without building another system.
  </Card>
</CardGroup>


This documentation is built and hosted on [Mintlify](https://mintlify.com), a developer documentation platform.