ForthClover
AI Infrastructure Migration

Move your AI into your own cloud, without losing quality.

We migrate LLM workloads from hosted APIs to models in your own AWS, Azure or Google Cloud account, or to open-weight models on GPUs you control. Every move is proven on your own evals, run in shadow before cut-over, and rolled out in stages with a tested way back.

Book a free fit callSee how it works
When it’s time to move

A hosted API got you to launch. It may not get you to scale.

Calling a provider’s API directly is the fastest way to ship. As volume grows, the trade-offs show up: throttling at peak, a bill that grows with every token, data leaving your environment and model versions you don’t control. These are the questions we answer before anything moves.
Sometimes the honest answer is to stay where you are. If the numbers don't support a move, the audit says so — and you keep the cost model and the eval set either way.
Book a free fit call
  • Will the new model match today’s quality on your real prompts, and how will you prove it?
  • At your volume, is on-demand pricing, provisioned throughput or your own GPUs cheapest?
  • Can you meet data-residency and customer security requirements by keeping data in your account and region?
  • What happens to latency and throttling at peak traffic?
  • How do you cut over without users noticing, and roll back if quality slips?
  • Who runs and patches the models afterwards, and what does that cost in people?
What we build

The migrations we run most often

Most moves follow one of these patterns, and larger ones often combine two. Each is measured against the system you run today.

Hosted APIs to Amazon Bedrock

Run Claude, Llama, Mistral or Amazon Nova models in your own AWS account, behind IAM and VPC endpoints. This is the move we made for Realwork Labs.

We measureEval parity, throttled requests, monthly spend

OpenAI API to Microsoft Foundry

Move GPT workloads into your own Azure tenant with private networking, under the Microsoft agreements you already have.

We measureEval parity, p95 latency, cost per 1,000 requests

Consolidating on Google Cloud

Bring Gemini and Claude calls onto Google's Gemini Enterprise Agent Platform (formerly Vertex AI) when your data, identity and teams already live in Google Cloud.

We measureEval parity, cost per task, providers in use

Self-hosted open-weight models

Serve Llama, Qwen, Mistral or DeepSeek models with vLLM or SGLang on GPUs you control, when volume or data rules justify it.

We measureGPU utilization, tokens per second, cost per million tokens

On-demand to provisioned throughput

Reserve capacity for steady, high-volume traffic and keep spiky work on demand, sized from your real traffic rather than guesses.

We measureReserved-capacity utilization, throttled requests, cost per task

Region and residency moves

Pin inference, logs and storage to the EU or another required region, with retention set to match your customer contracts.

We measureShare of traffic served in-region, retention settings verified

“ForthClover automated our 15-minute workflow to 30 seconds seamlessly.”

Partnering with ForthClover has been an excellent experience. Their team guided us through a seamless migration from our existing OpenAI implementation to a Bedrock-based solution that has transformed the efficiency of our content generation workflow. A task that once took 15 minutes of manual work per job is now automated and runs in under 30 seconds, positioning us to handle larger clients and increasing volumes with ease. The ForthClover team was responsive, patient, and collaborative throughout the process. Even beyond the initial timeline, they continued refining the solution to ensure we had everything we needed. Their communication was clear, and they provided thorough documentation and a smooth code handoff so our team could confidently take the work forward. We're very pleased with the outcome of this project and look forward to continuing our partnership in the next phase.

Brian LuskeyVP of Product, Design, and Engineering

Realwork Labs

How a migration runs

Measure, build, shadow, then switch

Nothing moves until the new path matches the old one on your own evals. You can stop after the audit and keep everything. Afterwards, AI Operations can monitor quality, drift and cost on the new stack.
  1. 01Phase 01

    Readiness audit

    We measure what you run today and model every realistic target before you commit to anything.

    • Inventory of model calls: volume, tokens, latency and spend per feature
    • Eval set built from your real prompts and outputs
    • Candidate models scored on quality, latency and cost
    • Data-residency, security and contract requirements mapped

    DeliverableCost model, target architecture and a go/no-go

  2. 02Phase 02

    Landing zone

    We build the target environment in your account, as code, before any traffic moves.

    • Terraform or OpenTofu for models, networking and IAM
    • Private endpoints and least-privilege access
    • LLM gateway for routing, fallback and rate limits
    • Tracing and cost tags on every request

    DeliverableTarget environment in your cloud, code in your repo

  3. 03Phase 03

    Shadow & parity

    Live traffic runs through both paths while your users still see the old one.

    • Production traffic mirrored to the new models
    • Side-by-side evals on quality, latency and cost
    • Prompt and parameter tuning to close any gaps
    • Load tests at your peak and above it

    DeliverableParity report you sign off before cut-over

  4. 04Phase 04

    Cut-over & hand-off

    We move traffic in stages, with a tested way back at every step.

    • Staged rollout by feature, tenant or percentage of traffic
    • Automatic fallback to the old provider on errors
    • Runbook, dashboards and cost alerts in your accounts
    • Old path retired only on your sign-off

    DeliverableProduction running on the new stack

What we build with

Managed models
Amazon BedrockMicrosoft FoundryGemini Enterprise Agent PlatformProvisioned throughput
Self-hosted serving
vLLMSGLangKubernetes GPU node poolsOpen-weight models
Routing & evals
LiteLLMCustom eval suitesLangfuseOpenTelemetry
Infrastructure
TerraformOpenTofuAWSAzureGCP

“ForthClover's adaptability and expertise solved our critical scaling challenges.”

ForthClover's engagement helped us successfully address critical scaling challenges on our platform. Their team implemented a FIFO queue scaling policy for our ECS workloads and designed a blue-green deployment strategy for zero-downtime traffic redirection. While the blue-green approach ultimately proved incompatible with our application, the ForthClover team showed exceptional adaptability by quickly identifying and resolving the issues, and still delivering all project objectives on schedule. Communication was consistently clear and professional and their expertise in ECS scaling optimization and health check configuration has meaningfully strengthened our cloud operations.

Luciano de la IglesiaCo-founder & CTO, Opentug

opentug

FAQ

Questions we hear on fit calls

Will migrating save us money?
Often, but not always. At low or spiky volume a hosted API can be the cheapest option. The audit models on-demand, provisioned and self-hosted costs on your real traffic, including the people needed to run it, and recommends the cheapest path that meets your quality bar. If that means staying put, the report says so.
How do you make sure quality doesn’t drop?
We build an eval set from your own prompts and past outputs, score the new models against it, and mirror live traffic in shadow before any user sees a change. Cut-over waits until the new path matches or beats today’s numbers.
Should we self-host open-weight models?
Only when volume, latency or data rules justify it. Serving models on vLLM or SGLang gives you the most control, but you take on GPUs, scaling and patching. For most teams, models in their own Bedrock, Microsoft Foundry or Google Cloud account are the better first step.
Does our data stay in our account?
With managed models in your cloud account, or models you host yourself, prompts and outputs stay in your account and the region you choose, under your own IAM and logging. We check each provider’s data and retention terms for the models and regions you pick.
What about HIPAA, SOC 2 and customer security reviews?
Running models in your own cloud account usually lets you rely on agreements you already have with that provider, such as a BAA, instead of adding another vendor. We design the network, access and logging around the evidence your auditors and customers ask for.
How is this different from the AI System Audit?
The $4,500 AI System Audit reviews a live AI system end to end: data, agent behavior, reliability and cost. The Migration Readiness Audit answers a narrower question: should you move your model calls, where to, and how. The fit call will tell you which one you need.

Find out whether moving makes sense.

Bring your current model usage to a free 45-minute fit call. You leave with a straight answer on whether a migration is worth it, the likely target, and what the audit would cost.

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