Skip to content
Contact us
Discover strategy scope

Dispatch Commander

Dispatch spreadsheets replaced by a route solver

See case study

  • Securing AI agents: permissions, prompt injection and audit trails

    Read more

  • What an AI model really costs in production

    Read more

  • Getting retail AI ready for peak season

    Read more
Run

Live, and getting better.

Monitoring, evaluation and model updates, on infrastructure we run on AWS, Azure and Google Cloud.

Book an AI audit
CodePromptsModelsGPUsPipelineLive

Models change every few months.

  • Watch

    See how it behaves in production.

  • Improve

    Every change tested before it ships.

  • Scale

    Inference that scales without surprises.

After launch, we stay and run it.

We watch it in production

Tracing, cost and quality alerts, with the people who built it on call.

Analytics charts on a laptop screen

Pick a starting point.

What do you need?

  • Inference endpoints that grow with demand and stay fast under load.

    • Inference endpoints for hosted APIs and self-hosted open-source models.
    • Autoscaling GPU workloads on Kubernetes that follow real traffic.
    • Routing, caching and fallbacks between model providers.
    Talk to an engineer

  • GPU and model environments defined in code on AWS, Azure and Google Cloud.

    • Infrastructure as Code: Terraform, Pulumi, and CloudFormation for repeatable environments.
    • Configuration management: Ansible and Packer images for consistent servers.
    • Policy as code: guardrails that keep every environment auditable.
    Talk to an engineer

  • Pipelines that test and ship code, prompts and models on every change.

    • Pipeline design: build, test, and deploy stages that match your workflow.
    • Automated testing and security scanning on every change.
    • Deployment orchestration: GitOps releases with rollback across environments.
    Talk to an engineer

What we've shipped

All case studies

Built, then run by the same team.

Person working across several monitors

  • Senior-led from start to finish.

    The people you meet build it.

  • We run what we build.

    easyvisas is live, and we operate it.

  • Monitoring and cost in view.

    Metrics, logs and model spend tracked in production.

  • One-off or ongoing.

    A setup sprint, or managed operations month to month.

Every step agreed upfront.

  1. Launch

    Monitoring and alerts from day one.

  2. Evaluate

    A regression suite before every change.

  3. Improve

    Model upgrades and new use cases.

Questions

What does Run include after launch?
Monitoring with tracing, plus cost and quality alerts. Model upgrades, guardrail tuning and new use cases follow as your needs change.
What happens when models change?
A regression suite runs before any prompt or model change ships. We then handle the model upgrade and tune the guardrails.
Where should our models run?
There are three common options. A hosted model API is the fastest way to start and suits most teams while usage is still taking shape. Self-hosting open-source models on your own cloud GPUs gives you more control over cost and behavior once traffic is steady. A private deployment inside your own network fits data that cannot leave your environment. We help you choose based on your data rules, traffic and budget, and many teams end up combining more than one.
Which cloud providers does Meterra support?
Meterra engineers on AWS, Google Cloud, and Microsoft Azure. We also design multi-cloud architectures where workloads need to span providers — for example, AI workloads on Google Cloud Vertex AI alongside existing infrastructure on AWS.
How does Meterra serve models in production?
We run your models behind inference endpoints that scale with demand, whether they are hosted APIs, open-source models on your own GPUs or a mix of both. GPU workloads autoscale on Kubernetes to follow real traffic, with routing, caching and fallbacks between model providers, and request tracing and alerts from prompt to response.
What does infrastructure as code cover?
We define your GPU and model environments in code on AWS, Azure and Google Cloud, using Terraform, Pulumi or CloudFormation. Ansible and Packer images keep servers consistent, and policy as code adds guardrails that keep every environment auditable.
What do CI/CD and MLOps pipelines include?
Pipelines test and ship code, prompts and models on every change. They cover build, test and deploy stages that match your workflow, automated testing and security scanning, and GitOps releases with rollback across environments.
How do we keep model cost and quality in view once it is live?
We track metrics, logs and traces for each request, token spend and GPU hours per feature, and response times and output quality, with alerts when latency or quality slips. You can take this as a one-off setup sprint or as managed operations month to month.