SageMaker vs Vertex AI: Which Platform for Model Inference?
- Leanware Editorial Team
- 1 hour ago
- 9 min read
Choosing between AWS SageMaker and Google Vertex AI for ML inference affects your team's velocity, operational costs, and ability to scale. Both platforms handle model deployment and serving, but they vary in how they integrate with their cloud ecosystems, manage resources, and price their services.
Let’s compare both platforms on the key aspects of production inference: deployment patterns, scaling behavior, cost structures, and the engineering experience of running models at scale.
What are SageMaker and Vertex AI?
Both platforms provide managed infrastructure for the full ML lifecycle, but most teams evaluate them specifically for inference workloads.
Amazon SageMaker
SageMaker covers the entire ML workflow from data labeling through training to deployment. It integrates with AWS services like S3, Lambda, and CloudWatch.
The platform offers pre-built algorithms, notebook environments through SageMaker Studio, and deployment options that work across EC2 instance types.
For deployment, SageMaker provides real-time endpoints, batch transform jobs, and asynchronous inference.
A newer feature called inference components lets you deploy multiple models to a single endpoint with granular control over CPU, GPU, and memory allocation per model. This approach can reduce inference costs by up to 80% compared to single-model deployments when you have multiple models with moderate traffic.
Google Vertex AI
Vertex AI combines Google's earlier AI Platform with AutoML capabilities. It connects directly to BigQuery for data access and Dataflow for preprocessing. The platform emphasizes integration with Google's data stack and provides access to both GPUs and TPUs, though TPU availability varies by region.
Vertex AI supports custom training, AutoML, and multiple deployment types including online prediction (real-time) and batch prediction. The platform includes built-in feature stores and model monitoring capabilities through Vertex AI Model Registry.
Key Features for Model Inference
Inference requirements differ from training. You need consistent latency, predictable costs, and the ability to handle traffic patterns that change throughout the day.
Model Deployment Types (Real-Time, Batch)
SageMaker offers three modes: real-time endpoints for sub-second latency, batch transform for offline large datasets, and asynchronous inference via SQS with results in S3 for variable processing times.
Vertex AI provides online prediction for real-time results and batch prediction for datasets in Cloud Storage or BigQuery. It doesn’t offer a managed async inference option like SageMaker. SageMaker’s async approach works well if you use AWS messaging, while Vertex’s BigQuery integration simplifies batch processing on tabular data.
Multi-Model Endpoints and Containers
Multi-model endpoints (MMEs) host multiple models on a single endpoint, reducing cost and overhead. SageMaker dynamically loads models from S3 and keeps popular models in memory. Its inference components let you control CPU, memory, and accelerators per model, scale independently, and scale components down to zero.
Vertex AI lacks built-in MME support; achieving it requires custom containers with manual model routing and caching logic.
Integration with Cloud Ecosystem and Data Services
SageMaker integrates naturally with AWS: models pull from S3, log to CloudWatch, trigger Lambda, and work with Step Functions. IAM manages permissions. Vertex AI connects with Google Cloud: train on BigQuery data, preprocess with Dataflow, deploy with Cloud Storage, and use Vertex AI Workbench. IAM controls access, and VPC supports network isolation.
Your existing cloud footprint matters: migrating data between clouds adds latency and cost, so native tools provide the most efficiency.
Workflow Comparison
Developer Workflow: From Model Creation to Deployment
On SageMaker, you typically train using built-in algorithms or custom containers, save the model to S3, create a model resource, configure an endpoint, and deploy.
SageMaker Studio provides a visual interface for this workflow with options to select pre-built containers or bring your own. You can also script everything with boto3 or the SageMaker Python SDK.
# SageMaker deployment with inference components
from sagemaker.compute_resource_requirements.resource_requirements import ResourceRequirements
resources = ResourceRequirements(
requests = {
"num_cpus": 2,
"num_accelerators": 1,
"memory": 8192, # in MB
"copies": 1,
},
)
predictor = model.deploy(
initial_instance_count = 1,
instance_type = "ml.p4d.24xlarge",
endpoint_type = EndpointType.INFERENCE_COMPONENT_BASED,
resources = resources,
)The model and endpoint must be compatible by having the same IAM role, VPC settings (including subnets and security groups), and network isolation configuration. Studio prevents you from deploying models to incompatible endpoints and shows alerts when settings conflict.
Vertex AI follows a similar pattern. You create a model resource, upload it to Cloud Storage or Artifact Registry, and deploy it to an endpoint. The Vertex AI SDK handles the deployment with simpler configuration options than SageMaker.
# Vertex AI deployment
DEPLOYED_NAME = model.display_name + "-endpoint"
MACHINE_TYPE = "n1-standard-4"
endpoint = vertex_model.deploy(
deployed_model_display_name=DEPLOYED_NAME,
traffic_split={"0": 100},
machine_type=MACHINE_TYPE,
)Both platforms abstract the underlying infrastructure, but SageMaker gives you more control over instance types and configurations. Vertex AI's deployment feels more automated, which helps teams without deep infrastructure knowledge but limits fine-tuning options.
Configuring Endpoints: Synchronous vs Asynchronous Predictions
SageMaker real-time endpoints handle synchronous requests with millisecond to sub-second latency. Asynchronous endpoints queue requests via SNS and SQS, process them, and store results in S3. This works well for models with variable processing times or when immediate responses aren’t needed. Timeout values for model download (up to 3600 seconds) and container startup health checks can be configured.
Vertex AI supports synchronous online predictions only. Async workflows require building your own queue with Pub/Sub and Cloud Functions or using batch prediction for offline processing. Online predictions use REST or gRPC APIs with batching, caching, and streaming options.
Autoscaling and Resource Management
SageMaker autoscaling uses metrics like InvocationsPerInstance or custom CloudWatch metrics. You set min and max instances, and SageMaker adjusts capacity, though scaling can take several minutes. Inference components let each model have separate scaling policies, with optimal placement handled automatically.
Vertex AI autoscaling responds to CPU and request counts, with min and max replicas configurable. It can scale faster but may over-provision if thresholds aren’t tuned. Traffic splitting is available for A/B testing.
Both platforms face cold start challenges: sudden traffic spikes can increase latency as models load into memory. Maintaining a minimum instance count reduces this risk but raises baseline costs.
Performance & Scalability
Autoscaling: Challenges and Solutions
Autoscaling ML endpoints differs from web services because model loading takes time - new instances may need 30-60 seconds to download the model from S3 or Cloud Storage, causing initial request timeouts.
SageMaker requires tuned scaling policies based on traffic. Aggressive scale-out reduces latency but increases costs. Using CloudWatch alarms for proactive scaling during predictable traffic periods works better than reactive scaling.
The LEAST_OUTSTANDING_REQUESTS routing strategy helps distribute traffic evenly during scale-out.
Vertex AI reacts faster but can oscillate if thresholds overlap with traffic variance. Setting a minimum replica count and longer stabilization windows prevents rapid scaling cycles.
Synchronous vs Asynchronous Inference Patterns
Synchronous inference fits interactive applications like recommendations, content moderation, or fraud detection. Both platforms deliver sub-second response times via standard HTTP endpoints.
Asynchronous inference suits batch scoring or workflows where results can be retrieved later. SageMaker supports this natively with async endpoints, queueing, and S3 storage. Vertex AI requires batch prediction or custom async handling via Pub/Sub.
Multi-Model Endpoints: Support and Limitations
SageMaker's traditional MMEs reduce cost for many models with intermittent traffic, managing loading and caching automatically. Performance may degrade if too many models are requested simultaneously.
Inference components offer more control: specify CPU, memory, and accelerator allocation per model. Models can scale to zero when idle, freeing resources and reducing costs, which has shown up to 8x savings in real-world deployments. MMEs work best when models are similar; mixing large and small models wastes resources and can cause performance issues.
Vertex AI lacks native MME support. You can deploy multiple models to separate endpoints or build custom routing logic. This provides isolation and independent scaling but increases operational complexity and baseline costs.
Quotas, GPU/TPU Availability & Infrastructure Constraints
SageMaker provides GPU instances (P3, P4, G4, G5) across most regions. Availability varies, and quota increases for larger instances require support requests. Training and inference quotas differ, so check both.
The platform offers options from cost-effective instances to high-performance multi-GPU like ml.p4d.24xlarge.
Vertex AI provides GPU and TPU access. TPUs benefit TensorFlow models and transformers but are available in fewer regions, with more restrictive quotas than GPUs.
Quota increases are needed for production workloads. Standard CPU machine types and GPU-accelerated instances handle intensive inference.
Both platforms can face capacity constraints during high demand. Reserved capacity or savings plans can help but require commitment.
Cost & Pricing Considerations
SageMaker charges for instance hours. Real-time endpoints running 24/7 incur full hourly costs even when idle. Inference components reduce costs by sharing infrastructure and allowing models to scale to zero. Asynchronous endpoints only charge during processing, making them cheaper for intermittent workloads.
Vertex AI charges per node-hour, but autoscaling to zero isn’t fully supported (minimum replicas usually 1), so you pay for baseline capacity. Batch prediction charges per node-hour without persistent endpoints, making it cost-effective for offline workloads.
Hidden costs include data transfer (especially cross-region), model storage, and logging. Both platforms charge for endpoint predictions, and networking costs can add up for high-volume, multi-region deployments.
Limitations & Considerations
SageMaker is flexible but complex, requiring AWS knowledge and a steep learning curve around IAM, VPCs, and deployment choices.
Vertex AI simplifies workflows but offers less control, and TPU availability and quotas can be limiting. Both platforms create ecosystem lock-in, making migration non-trivial even if models are portable.
Which Platform to Choose?
Choose SageMaker if you’re already on AWS, need inference components for efficient multi-model hosting, or require asynchronous inference without custom infrastructure. It offers flexible deployment and suits teams familiar with AWS services. Inference components are especially useful for deploying multiple foundation models with varying traffic.
Choose Vertex AI if your data lives in BigQuery, you want tighter integration with Google Cloud tools, or need TPU access for certain model architectures. It’s simpler for teams with limited DevOps experience and provides smoother integration with Google’s ML ecosystem.
Alternatives and Multi-Cloud Considerations
Azure ML offers managed inference on Microsoft Cloud. Hugging Face Endpoints simplify transformer deployments.
Kubernetes-based solutions (KServe, Seldon) provide multi-cloud portability but require more operational expertise.
Containerized serving (TorchServe, TensorFlow Serving) eases migration but needs platform-specific automation. Running inference on both platforms adds redundancy but increases operational overhead.
Getting Started
Choosing between SageMaker and Vertex AI depends on your cloud setup, workload, and team experience. SageMaker offers flexible deployment and inference components, while Vertex AI gives simpler workflows and TPU support.
For practical guidance on deployment or migration, connect with our ML engineers to plan the right approach for your setup.
Frequently Asked Questions
Can I migrate from SageMaker to Vertex AI (or vice versa) without downtime?
Migration requires planning but doesn't require downtime. Deploy your model to both platforms, gradually shift traffic using DNS or load balancer weights, and monitor performance on the new platform before decommissioning the old deployment. This dual-deployment period increases costs temporarily but eliminates user impact. Containerizing your model with standard serving frameworks makes the process smoother.
Does SageMaker/Vertex AI support model versioning and A/B testing?
Both platforms support versioning. SageMaker lets you deploy multiple models or inference components behind a single endpoint with traffic splitting for A/B tests. You configure the split percentages and route requests accordingly. Each inference component can be updated independently without affecting others on the same endpoint.
Vertex AI provides similar traffic splitting across model versions deployed to the same endpoint. You specify traffic percentages when deploying models, and the platform handles request routing. Both platforms track metrics separately for each model version, making it easy to compare performance.
Can I deploy the same model to both platforms for redundancy?
Yes, though it requires managing two deployment pipelines. Containerizing your model with a standard serving framework makes this easier. You'll maintain separate endpoint configurations, IAM roles, and monitoring for each platform, but the core model artifacts stay consistent. This approach provides redundancy against platform-specific outages but doubles your operational complexity and baseline infrastructure costs.
Which platform is easier to use for teams without DevOps expertise?
Vertex AI has a slight edge for teams new to ML infrastructure. Its integration with Google Cloud's managed services reduces configuration decisions, and the deployment process involves fewer steps. The platform handles many networking and security settings automatically.
SageMaker Studio provides a visual interface that helps, but the underlying AWS complexity remains. You still need to understand IAM roles, VPC configuration, and security groups.
However, SageMaker's ModelBuilder class can automatically capture dependencies and infer serialization functions, reducing some complexity for standard frameworks like PyTorch and XGBoost.
Both platforms benefit from dedicated DevOps knowledge for production deployments, especially when optimizing costs and handling autoscaling policies.
Which platform is better for LLM inference (GPT, Llama, etc.)?
It depends on your specific needs. Vertex AI works well for Google's foundation models and benefits from TPU optimization for large transformers. If you're using Google's pre-trained models or need TPU performance for specific architectures, Vertex AI is the natural choice.
SageMaker offers more flexibility for open-source LLMs with access to various GPU types (P4, G5, P5) and instance configurations. The inference components feature is particularly valuable for LLM deployments, allowing you to host multiple large models on the same infrastructure with independent scaling policies. Companies like Salesforce achieved up to 8x cost reduction by deploying multiple LLMs using inference components instead of separate endpoints.
For custom or open-source LLMs (Llama, Mistral, Falcon), SageMaker's broader instance selection and inference components typically provide better cost-performance options and more granular resource control.