Cloud-to-AI Migration
AI projects stall when the data they need lives in systems built for a different decade. We move the right workloads, pipelines and storage onto modern infrastructure, in stages, without breaking what already runs your business.
Why teams bring us in
Migrations fail when they try to move everything at once. The workloads that feed AI (document stores, transaction history, event streams) are usually a small part of the estate. We start there, prove value, and leave the rest for when it makes sense.
An AI roadmap usually depends on a surprisingly small part of the IT estate: the document stores, transaction history and event streams that models need to read. We map those dependencies first, so migration effort goes where it unlocks AI value, not where it merely tidies up.
- Readiness assessment: which data and systems your AI roadmap actually depends on
- Landing zone: network, identity and GPU capacity set up with infrastructure as code
- Data platform: lakehouse storage, vector stores and governed access for model teams
- Staged cutover: dual-running and rollback plans for each workload
- Cost guardrails: reserved capacity, tagging and budgets from the first day
Teams that get the most from this service
Enterprises with legacy data centers
Your most valuable data sits on infrastructure that was never designed for AI workloads.
Companies mid-way through cloud adoption
Some workloads have moved, but data platforms and GPU capacity are still an afterthought.
Teams preparing for AI programs
Leadership has approved AI initiatives and the data foundation needs to be ready first.
Four phases, each with a sign-off.
Each migration wave has a dual-running period, automated data validation and a tested rollback plan. Infrastructure is defined in Terraform and reviewed in your repositories, and GPU capacity is provisioned with the same guardrails as the rest of your cloud: tagging, budgets, least-privilege roles and network segmentation.
| Phase | What happens | You receive |
|---|---|---|
| Assess | Inventory systems, data gravity, compliance scope | Migration wave plan |
| Foundation | Landing zone, identity, networking, GPU pools | Terraform modules in your repo |
| Migrate | Wave-by-wave moves with validation | Cutover sign-offs |
| Enable | Data access for AI teams | Governed feature + vector stores |
What you keep when we're done.
Wave plan
Workloads grouped and sequenced by value to the AI roadmap and migration risk.
Infrastructure as code
Every environment defined in Terraform, reviewed and owned by your team.
AI-ready data layer
Clean, permissioned datasets and vector indexes your models can use.
Tools we use
Chosen per client. We work with what your team already runs wherever it makes sense.
Questions about Cloud-to-AI Migration
Do we have to move everything to the cloud?
No. Many clients keep sensitive systems on-premises and use a hybrid design. We recommend placement per workload based on cost, compliance and data gravity.
How do you avoid downtime?
Change-data-capture keeps old and new systems in sync during a dual-running period. We cut over only after validation passes, and every wave has a rollback plan.
Who owns the infrastructure code?
You do. All Terraform modules and pipelines are committed to your repositories and reviewed by your team.
Often paired with
Private & On-Prem LLM Deployment
Run open-weight models on your own servers or private cloud, with no data leaving your network.
Explore service →SVC-02GPU Infrastructure Optimization
Cut inference and training spend with better scheduling, quantization and right-sized clusters.
Explore service →SVC-04MLOps Pipeline Setup
Automate training, evaluation, deployment and monitoring so models ship like software.
Explore service →Talk to an engineer about Cloud-to-AI Migration.
A 45-minute call with an engineer, not a sales script. You'll leave with a rough architecture, a cost range and the next three steps.