Google Cloud Partner
Re-platform your Amazon SageMaker ML estate onto Vertex AI, models, training pipelines, feature stores and serving endpoints, on Google's managed MLOps and Model Garden. Delivered workload by workload by a Google Cloud Partner.
Yes, you can migrate from Amazon SageMaker to Vertex AI. It is a re-platform, not a copy: Codimite, a Google Cloud Partner, moves your trained models to the Vertex AI Model Registry, rebuilds training jobs as Vertex AI Pipelines, re-creates SageMaker endpoints as Vertex AI Endpoints, and rewrites SDK and custom container code from SageMaker to Vertex AI. Model artifacts and data move; pipelines, containers, feature stores and IAM are reworked because the platforms differ.
Why migrate
Vertex AI Pipelines, Model Registry, Feature Store and monitoring give an integrated MLOps platform under one roof.
Gemini, open models and third-party models in one catalog, you are not tied to a single model family.
Train and serve directly against data in BigQuery without heavy ETL.
Vertex AI is where grounded Gemini apps and Codimite's ADK + n8n agents are built.
If you are already moving compute and data to GCP, keeping ML on AWS means cross-cloud egress and split tooling.
What we migrate
| SageMaker component | Migrates to Vertex AI | Notes |
|---|---|---|
| Trained model artifacts | Cloud Storage + Model Registry | Artifacts move; container format may be rebuilt |
| Training jobs | Vertex AI Training / Pipelines | Orchestration reworked (Kubeflow-based) |
| SageMaker Pipelines | Vertex AI Pipelines | Re-authored, different DSL |
| Endpoints (real-time/batch) | Vertex AI Endpoints / Batch Prediction | Re-created and load-tested |
| Feature Store | Vertex AI Feature Store | Schema + ingestion re-built |
| Studio notebooks | Vertex AI Workbench | Environments re-provisioned |
| SDK / custom containers | Vertex AI SDK / containers | Code rewritten from SageMaker SDK |
| IAM & networking | Google IAM / VPC | Re-mapped, roles and VPC differ |
Effort is scoped per workload during assessment, tightly AWS-coupled jobs take more rework.
Our process
We inventory models, pipelines, endpoints, feature stores and AWS dependencies, and rate each workload's effort.
We map each SageMaker component to its Vertex AI equivalent and design IAM, networking and data flow.
We move artifacts and data, rebuild pipelines and containers, and re-create endpoints on Vertex AI.
We compare predictions and metrics against the SageMaker baseline to confirm parity.
Traffic shifts to Vertex AI endpoints in waves, with rollback available.
We tune cost and performance and wire monitoring and MLOps.
Why Codimite
Vertex AI, BigQuery and Google ADK are our core stack, delivered as a Google Cloud Partner: we de-risk by migrating one ML workload at a time, prove parity against the SageMaker baseline, and can layer Gemini grounding and ADK + n8n agents once you're on Vertex AI.
Get a QuoteGoogle Cloud Partner. Vertex AI, BigQuery and Google ADK are our core stack.
Workload-by-workload delivery. We de-risk by migrating and validating one ML workload at a time.
Parity verification. Prediction and metric comparison proves the re-platform did not regress.
Agent-ready. Once on Vertex AI, we can layer Gemini grounding and ADK + n8n agents on your models.
FAQs
Yes, artifacts and data move to Cloud Storage and the Model Registry; training jobs, pipelines, endpoints and SDK code are re-platformed onto Vertex AI.
Medium. Models and data move cleanly; pipelines, custom containers, feature stores and IAM need rework because the platforms differ.
Vertex AI Pipelines (Kubeflow-based). Studio maps to Workbench, Endpoints to Vertex AI Endpoints, Feature Store to Vertex AI Feature Store.
Workloads deeply embedded in AWS take more effort, SageMaker's AWS integration is a genuine strength. We assess those dependencies first and re-map them to GCP services.
Days to weeks for a single model and endpoint; weeks to months for a full ML platform.
Still comparing platforms? Read Vertex AI vs SageMaker: Compared (2026) before you decide.
Codimite, a Google Cloud Partner, re-platforms your SageMaker models, pipelines and endpoints onto Vertex AI workload by workload, with parity verification against your SageMaker baseline. Start with a free quote.
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