Migrating AI Workloads Between Clouds: from SageMaker to Vertex AI
You built a pipeline on SageMaker, but your GPU spot instance bills are rising 40% month over month. Or a regulator demanded data residency in a region without Azure. Migrating an AI workload between clouds is not just copying files. Each case is unique: different stacks, compliance requirements, data volumes. We tailor a strategy to your specific case: transferring models, pipelines, data, and managed services while preserving latency p99 and inference quality. With over 5 years of expertise in AI workload migration and 15+ successful projects, our team ensures smooth transitions. According to a 2024 Gartner report, cloud cost optimization is a top priority for enterprises.
Why Migrate an AI Solution Between Clouds?
Main drivers: cost optimization (AWS spot instances up to 60% cheaper), avoiding vendor lock-in, access to specific hardware (NVIDIA H100 on GCP). Below is a comparison of key parameters.
| Criteria | AWS | GCP | Azure |
|---|---|---|---|
| GPU spot discount | up to 60% | up to 50% | up to 40% |
| H100 availability | limited | high | medium |
| Managed ML | SageMaker | Vertex AI | Azure ML |
Our experience shows that a proper migration reduces TCO by 30-50% and eliminates vendor lock-in. For one client, we reduced monthly costs from $80k to $45k by migrating to GCP spot instances. Another project saved $12,000 monthly by migrating to Azure ML with optimized GPU utilization. A typical migration saves $50k-$200k annually.
How to Minimize Downtime During Migration?
The key pattern is Strangler Fig: we spin up parallel infrastructure in the new cloud and switch traffic canary-style. Shadow deployment runs in the first phase — inference executes in both clouds, comparing prediction distributions. A difference >0.1% is a stop signal. Additionally, we use Blue/Green deployment for critical services. Shadow deployment is 3x safer than direct traffic switch.
# Cloud-agnostic storage abstraction
from abc import ABC, abstractmethod
class ObjectStorage(ABC):
@abstractmethod
def upload(self, local_path: str, remote_path: str): ...
@abstractmethod
def download(self, remote_path: str, local_path: str): ...
class S3Storage(ObjectStorage):
def __init__(self, bucket: str, region: str = "us-east-1"):
self.client = boto3.client('s3', region_name=region)
self.bucket = bucket
def upload(self, local_path: str, remote_path: str):
self.client.upload_file(local_path, self.bucket, remote_path)
class GCSStorage(ObjectStorage):
def __init__(self, bucket: str):
self.client = storage.Client()
self.bucket = self.client.bucket(bucket)
def upload(self, local_path: str, remote_path: str):
blob = self.bucket.blob(remote_path)
blob.upload_from_filename(local_path)
# Switching via config
storage = S3Storage("my-bucket") if CLOUD == "aws" else GCSStorage("my-bucket")
Before starting migration, we always audit cloud-specific dependencies: proprietary APIs (SageMaker, Vertex AI), managed services (Feature Store, ML Pipelines), IAM roles, network connectivity (VPC peering, PrivateLink), and data residency requirements. This allows us to identify and abstract all integration points. Our migration process typically involves 5 stages over 4-8 weeks. We have completed migrations for datasets up to 10 TB.
How to Abstract from Cloud-Specific APIs?
The first risk when changing clouds is proprietary APIs. Managed services like SageMaker or Vertex AI are hard to abstract. We use cloud-agnostic interfaces — example above. For ML pipelines, we adopt Kubeflow, which works consistently on all clouds. Feature Stores are abstracted via Feast. This allows switching backends without rewriting code. For MLOps, we use Weights & Biases, which supports any platform. Monitoring is set up with Prometheus + Grafana, providing a unified observability layer regardless of the cloud.
What Data Is Transferred During Migration?
Data volumes can reach tens of terabytes. Typical set:
- Models (weights, configs, model cards)
- Training datasets (raw, transformed, feature stores)
- Containers (Docker images)
- ML pipelines (Kubeflow, Airflow DAGs)
- Experiment results (MLflow, Weights & Biases)
- Monitoring configurations and secrets
For large datasets (>5 TB), we use physical transfer: Snowball (AWS), Transfer Appliance (GCP), or Azure Data Box. For smaller ones, gsutil rsync or AWS CLI.
# AWS S3 → GCS via Storage Transfer Service (Google)
gcloud transfer jobs create \
--source-agent-pool=aws-pool \
--aws-source-bucket=my-aws-bucket \
--destination-bucket=my-gcs-bucket \
--do-not-delete-from-source
# For large datasets (>TB): gsutil -m rsync -r
gsutil -m rsync -r s3://source-bucket gs://dest-bucket
Comparison of Migration Strategies
| Strategy | Downtime | Risk | Complexity |
|---|---|---|---|
| Strangler Fig | Minimal | Low | High |
| Blue/Green | Zero | Medium | Medium |
| Big Bang | High | High | Low |
We recommend Strangler Fig for production systems with critical SLA. Strangler Fig reduces downtime by 10x compared to Big Bang.
What's Included in the Work
- Audit of cloud-specific dependencies and cost modeling
- Development of migration strategy (Strangler Fig, blue/green)
- API abstraction using cloud-agnostic interfaces
- Data and model transfer with integrity checks
- Infrastructure deployment in the new cloud (IaC)
- Shadow deployment and A/B metric comparison
- Validation of experiment reproducibility (MLflow, Weights & Biases)
- Documentation and training of the client's team
- Post-migration support for 2 weeks
Migration Process
- Analytics (1-2 weeks): dependency audit, cost modeling.
- Design (1-2 weeks): strategy selection, API abstraction.
- Implementation (2-4 weeks): data transfer, infrastructure deployment.
- Testing (1 week): shadow deployment, A/B metric comparison.
- Deployment (1 week): canary migration, rollback plan.
Timelines range from 4 to 8 weeks depending on complexity. Cost is determined individually after the audit.
Get a consultation: write to us — we'll send an audit checklist and timelines. Contact us for a preliminary audit — we'll assess the scope of work and choose the optimal strategy.