ForthClover
DevOps & AI Infrastructure

AI infrastructure your team can ship on.

We set up the cloud foundation for AI systems in your account: infrastructure as code, containers or Kubernetes with GPU autoscaling where you need it, CI/CD with eval gates, an LLM gateway, and the tracing and cost alerts to run it all. Everything lives in your repo and your accounts.

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Where AI infrastructure breaks

Your model works. The platform around it doesn’t yet.

Most AI teams start with a hosted API, a few scripts and manual deploys. That holds until real customers, real volume and security reviews arrive. These are the questions we hear when it stops holding.
We build the foundation once, in your cloud, with code your team owns, so your engineers can get back to the product — at a fixed price agreed before we start.
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  • Can you rebuild every environment from code, or does it live in someone’s head?
  • Does a prompt or model change pass tests before it reaches users?
  • What happens when your model provider throttles you or goes down?
  • Why did last month’s GPU or token bill jump, and which feature caused it?
  • Would you know a release made answers worse before a customer told you?
  • Will your IAM, secrets and tenant isolation pass an enterprise security review?
What we build

What we set up

Pick the gaps you have and we scope only those. Each one comes with the numbers we use to show it’s working.

Infrastructure as code

Every environment, network, model endpoint and permission defined in Terraform or OpenTofu, reviewed in pull requests and reproducible from scratch.

We measureShare of infrastructure under code, time to stand up a new environment

CI/CD with eval gates

Build, test and deploy on every merge, with your eval suite blocking any prompt, model or retrieval change that lowers quality.

We measureDeploy frequency, failed-change rate, eval score per release

GPU and inference scaling

Kubernetes GPU node pools or managed endpoints that scale with queue depth and down to zero when idle. Our Gulp.ai work is an example.

We measureGPU utilization, queue wait time, cost per 1,000 requests

LLM gateway

One entry point for every model call, with routing, fallback between providers, rate limits, caching and per-team keys.

We measureFallback success rate, throttled requests, spend per team

Observability

OpenTelemetry traces from user request to model call, Grafana dashboards for the platform and Langfuse for prompts, outputs and quality.

We measurep95 latency, error rate, time to find an incident’s cause

Security and cost controls

Least-privilege IAM, secrets in a managed vault, tenant isolation, and cost tags with budget alerts per feature and customer.

We measureUntagged spend, open IAM findings, budget alerts

“ForthClover streamlined our DevOps process and positioned us to better support future AI workloads.”

Working with ForthClover was a fantastic experience. Their team brought deep cloud expertise and made a significant impact on our infrastructure, helping us transition from manual AMI-based deployment to a modern, scalable architecture using ECS, Docker, and SQS. The shift has streamlined our DevOps process and positioned us to better support future AI workloads. Our assigned engineer was an incredible partner — knowledgeable, hardworking, and always responsive. His support with Terraform, cloud deployment, and infrastructure tuning was invaluable. We learned a lot throughout the engagement and were able to move significantly faster thanks to the foundation ForthClover provided.

David AltmanHead of AI, PathPilot

pathpilot

How we work

Assess, prove, build, hand off

You can stop after any phase and keep everything: code in your repo, infrastructure in your cloud. If you want us to keep watching it, AI Operations starts at $990 a month.
  1. 01Phase 01

    Assess

    We review what you run today and agree the target in writing.

    • Architecture, deploy process and on-call reviewed
    • Cloud and model spend broken down by service and feature
    • Security gaps: IAM, secrets, network, tenant isolation
    • Target architecture sized for today and 10× volume

    DeliverableCurrent- and future-state diagrams, fixed-price plan

  2. 02Phase 02

    Prove

    We build the riskiest piece first, so the design is tested before it’s rolled out everywhere.

    • One service deployed end to end through the new pipeline
    • Load test against your real traffic pattern
    • Cost per request measured, not estimated
    • Single- vs. multi-tenant decision settled

    DeliverableWorking slice in your cloud with measured cost and latency

  3. 03Phase 03

    Build

    We roll the proven pattern out to every service and environment.

    • Terraform or OpenTofu modules for each environment
    • CI/CD with eval gates and staged rollouts
    • Autoscaling, including GPU node pools where needed
    • Dashboards, alerts and runbooks

    DeliverableProduction infrastructure in your accounts, all as code

  4. 04Phase 04

    Hand off

    Your team owns it from here. We stay available if you want us.

    • Walkthroughs and pairing sessions with your engineers
    • Docs and runbooks in your repo
    • Failover and rollback tested before we step back
    • Optional monitoring and tuning through AI Operations

    DeliverableSigned hand-off checklist

What we build with

Cloud & compute
AWSAzureGCPKubernetesECS
Delivery
TerraformOpenTofuGitHub ActionsArgo CD
Inference & routing
vLLMAmazon BedrockMicrosoft FoundryLiteLLM
Observability & cost
OpenTelemetryGrafanaLangfuseBudgets & cost tags

“ForthClover provided clear, actionable recommendations to streamline our architecture, ensure scalability, and drive cost efficiency.”

Working with ForthClover has been an excellent experience. Their team brought the right expertise to deepen our cloud architecture planning and identify new ways to amplify our behavioral data with machine learning and AI. The engagement provided clear, actionable recommendations to streamline our architecture, ensure scalability, and drive cost efficiency. The team exceeded our expectations with clear communication and responsiveness. We also valued their ability to highlight the right AI approaches and frame complex concepts in a way that supported our team's strategic thinking. Overall, this project sharpened our strategy and accelerated our plans to evolve Churnkey's platform with AI.

Nick FogleFounder & CEO, Churnkey

churnkey

Case study · Gulp.ai

How we built Gulp.ai’s LLM fine-tuning pipeline on AWS.

  • Improved GPU utilization and reduced training time during model training runs
  • Substantially reduced compute costs through dynamic batch sizing and efficient GPU usage
  • Abstracted infrastructure management so the team could focus on model development
Read the case study
FAQ

Questions we hear on fit calls

Do we need Kubernetes?
Not always. If you call hosted models and run a handful of services, managed containers such as ECS or Cloud Run are simpler and cheaper to run. Kubernetes earns its keep when you serve your own models on GPUs or run many services. We recommend the smallest setup that does the job.
What is an eval gate?
A CI step that runs your eval suite on every prompt, model or retrieval change and blocks the release if quality falls below the agreed bar, the same way unit tests block broken code.
How do you keep GPU and token costs under control?
First we make spend visible: every resource and model call is tagged by feature and customer, with budget alerts. Then we tune the big items: scaling to zero when idle, batching, caching, routing simple requests to smaller models, and reserved capacity where traffic is steady.
Which clouds do you work in?
AWS, Azure and Google Cloud, always in your own accounts. We build on the services you already use rather than adding new vendors.
Will our team be able to run it?
Yes, that is the point. Everything is code in your repo, documented and walked through with your engineers. If you would rather not run it, AI Operations covers monitoring, evals and tuning from $990 a month.

Ready to stop fighting your infrastructure?

Bring your current setup to a free 45-minute fit call. You leave with the gaps that matter most, what we would fix first, and whether we are the right team to do it.

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