Self-Hosted Models to Vertex AI Migration Services

Why Vertex AI?

Reduce Infrastructure Work and Scale AI Delivery

Managed Model Serving

Deploy models to managed endpoints without maintaining inference servers, Kubernetes clusters, or custom autoscaling infrastructure.

Flexible Model Support

Run custom-trained models, open models, proprietary frameworks, and specialized inference code using prebuilt or custom containers.

Centralized Model Operations

Manage model versions, deployments, metadata, evaluations, and approvals through Vertex AI Model Registry.

Scalable AI Infrastructure

Use managed CPU, GPU, and TPU resources for training and inference based on workload performance and cost requirements.

How We Migrate

A Structured Self-Hosted Model Migration

1

Assess

Review model artifacts, frameworks, containers, inference servers, hardware, dependencies, endpoints, traffic, security, monitoring, and data access.
2

Design

Define the Vertex AI architecture for model storage, registry, containers, endpoints, compute, networking, autoscaling, monitoring, and governance.
3

Package and Migrate

Move model artifacts and container images to Google Cloud and adapt serving code, dependencies, health checks, and prediction interfaces.
4

Deploy and Validate

Deploy models to Vertex AI endpoints and compare predictions, latency, throughput, resource usage, reliability, and security.
5

Cut Over and Optimize

Transition production traffic gradually, enable monitoring, tune compute and autoscaling, and retire approved self-hosted infrastructure.

Why Codimite?

End-to-End Model Infrastructure Modernization

Codimite combines Google Cloud architecture, machine learning engineering, containerization, MLOps, and production operations expertise.

Start Your Migration

Infrastructure and Model Discovery. We identify model formats, runtime dependencies, inference servers, hardware requirements, scaling behavior, and operational risks.
Deployment Architecture Selection. We choose managed endpoints, custom containers, Model Garden deployment, batch prediction, or another suitable Vertex AI serving option.
Performance-Led Migration. We benchmark prediction quality, latency, throughput, memory, accelerator usage, and cost before production rollout.
Controlled Production Transition. We reduce risk through shadow traffic, parallel serving, staged routing, health checks, monitoring, and rollback planning.

Platform Comparison

Self-Hosted Model Infrastructure and Vertex AI at a Glance

Both approaches can support production AI, but Vertex AI provides managed infrastructure, centralized operations, and deeper Google Cloud integration.

Area	Self-Hosted Models	Vertex AI
Platform approach	Models deployed on customer-managed servers, VMs, Kubernetes, or private infrastructure	Managed AI platform for training, registering, deploying, evaluating, and monitoring models
Infrastructure	Teams provision, configure, patch, secure, and maintain compute infrastructure	Google manages core serving and training infrastructure
Model support	Model compatibility depends on custom runtime and infrastructure design	Supports custom-trained models, supported open models, prebuilt containers, and custom containers
Serving framework	Uses custom APIs, inference servers, Kubernetes services, or framework-specific serving tools	Managed endpoints with prebuilt or custom prediction containers
Container support	Teams build, host, scan, deploy, and operate container images	Custom images can be stored in Artifact Registry and deployed through Vertex AI
Scaling	Requires custom autoscaling rules, capacity planning, and infrastructure automation	Managed endpoints support configurable compute and autoscaling
Accelerators	Teams select, provision, schedule, and maintain GPU or accelerator infrastructure	Managed CPU, GPU, and TPU options are available for supported workloads
High availability	Requires custom redundancy, load balancing, recovery, and failover design	Managed serving infrastructure supports scalable endpoint deployment and traffic management
Model registry	Often uses custom repositories, object storage, MLflow, or manual version tracking	Vertex AI Model Registry centralizes models, versions, metadata, and deployment workflows
Online inference	Operated through customer-managed endpoints and networking	Managed online endpoints support production prediction workloads
Batch inference	Requires custom batch jobs, schedulers, or processing pipelines	Vertex AI Batch Prediction supports managed offline inference
Monitoring	Teams build logging, metrics, drift detection, alerts, and dashboards	Vertex AI Model Monitoring integrates with Cloud Logging and Cloud Monitoring
Evaluation	Uses custom scripts, notebooks, benchmarks, and reporting	Vertex AI supports managed model evaluation for registered and custom-trained models
Security	Teams manage identity, network controls, secrets, patching, and auditability	IAM, service accounts, private networking, encryption, audit logs, and organization policies
Open models	Teams download, verify, package, optimize, and operate open models independently	Model Garden supports discovering, testing, customizing, and deploying selected open and partner models
Generative AI serving	Requires custom inference servers such as vLLM, TGI, or other runtimes	Supports managed and self-deployed open-model options, including custom vLLM containers
MLOps integration	Requires combining separate tools for pipelines, registries, evaluation, and deployment	Integrates with Vertex AI Pipelines, Experiments, Model Registry, evaluation, and monitoring
Data integration	Data connectivity depends on custom application and network design	Closely integrated with BigQuery, Cloud Storage, Dataflow, Dataproc, and Google Cloud databases
Operations	Teams handle upgrades, scaling, failures, capacity, observability, and security	Google manages core infrastructure while teams focus on models and applications
Best suited for	Workloads requiring complete control over hardware, runtime, and deployment infrastructure	Teams seeking managed, scalable, and governed AI delivery on Google Cloud

FAQs

Self-Hosted Models to Vertex AI Migration FAQs

What can be migrated from self-hosted models to Vertex AI?

Codimite can migrate model artifacts, Docker containers, inference code, online endpoints, batch jobs, model registries, monitoring, pipelines, and connected AI applications.

Which self-hosted machine learning models can move to Vertex AI?

Many TensorFlow, PyTorch, scikit-learn, XGBoost, Hugging Face, MLflow, custom-framework, and open-weight models can be migrated when their artifacts, dependencies, licenses, and compute requirements are compatible.

Can existing trained models and Docker containers be reused?

Often yes. Reuse depends on the model format, framework version, runtime dependencies, prediction interface, hardware requirements, and container compatibility with Vertex AI.

Do self-hosted models need to be retrained before migration?

Not always. Compatible model artifacts can often be imported and deployed directly. Retraining may be required when feature pipelines, runtimes, frameworks, or model dependencies change.

How are self-hosted endpoints, monitoring, and model registries migrated?

Endpoints are redesigned using Vertex AI online or batch prediction. Model versions and metadata can move to Vertex AI Model Registry, while logs, drift checks, alerts, and performance monitoring are rebuilt using Vertex AI Model Monitoring and Google Cloud operations tools.

How do you validate a self-hosted model migration to Vertex AI?

We compare model outputs, evaluation metrics, latency, throughput, concurrency, memory and accelerator usage, autoscaling, failure recovery, security, and business acceptance criteria before production cutover.