SageMaker vs Seldon Core: Comparison Guide

Choosing between a managed MLOps platform and an open-source framework affects how fast you ship models, how much control you have over infrastructure, and what your monthly bill looks like. Amazon SageMaker offers a fully managed experience within AWS, while Seldon Core gives you a Kubernetes-native framework you run yourself. Both solve the same core problem of getting models from notebooks into production, but they take different approaches.

Let's explore what each platform does well, where they fall short, and which fits specific team structures and business needs.

What is Amazon SageMaker?

Amazon SageMaker is AWS's managed machine learning platform. It launched in 2017 to solve the infrastructure headaches that data science teams face when moving models from Jupyter notebooks to production endpoints. The service handles provisioning, scaling, monitoring, and security, letting you focus on model development rather than Kubernetes manifests or load balancer configurations.

SageMaker sits inside the AWS ecosystem, which means it connects directly to S3 for data storage, IAM for access control, and CloudWatch for monitoring. If your company already runs on AWS, SageMaker integrates without additional authentication layers or VPC peering configurations.

Key Features of SageMaker

SageMaker Studio provides a web-based IDE for the full ML workflow. You write code in notebooks, track experiments, and deploy models from the same interface. Autopilot handles automated model training and hyperparameter tuning when you need quick baseline models. Pipelines orchestrates multi-step workflows like data preprocessing, training, and batch inference. The built-in algorithms cover common use cases like XGBoost, linear regression, and image classification without writing training code.

Model Registry tracks versions with metadata tags, approval workflows, and lineage tracking. This matters when you're managing dozens of model iterations across multiple teams. Ground Truth handles data labeling with human reviewers or automated labeling for training data preparation.

Deployment Options

SageMaker deploys models as real-time endpoints, batch transform jobs, or asynchronous endpoints. Real-time endpoints serve predictions with low latency, typically under 100ms. You configure instance types (ml.m5.xlarge, ml.g4dn.xlarge) and autoscaling policies through the console or API calls. Multi-model endpoints let you host multiple models on a single instance, which reduces costs when you have many low-traffic models.

Batch transform processes large datasets offline. You point it at an S3 bucket, specify instance count, and it distributes the workload. Asynchronous endpoints queue requests and process them when traffic spikes, useful for tasks that tolerate 10-15 minute delays.

Target Users & Use Cases

Enterprise data science teams use SageMaker when they need to deploy models quickly without building infrastructure. Financial services companies handling fraud detection or credit scoring use it because AWS handles SOC 2 and PCI compliance certifications. Healthcare organizations working with PHI data rely on SageMaker's HIPAA-eligible configuration.

The platform works well for teams that want to avoid hiring DevOps engineers specifically for ML infrastructure. If you have five data scientists and no one who wants to debug Istio configurations, SageMaker makes sense.

Pricing and Plans

SageMaker follows a pay-as-you-go model with per-second billing. An ml.m5.xlarge instance costs approximately $0.23/hour for training and hosting. Real-time endpoints run continuously and charge even when idle, so an ml.m5.xlarge endpoint costs around $165/month ($0.23 × 24 × 30). For more powerful GPU instances like ml.g5.xlarge, expect $1.21/hour or roughly $872/month for continuous deployment.

Storage follows S3 pricing at $0.023/GB/month. Data transfer within the same region is free, but cross-region transfer costs $0.02/GB. Additional services add complexity:

Feature Store charges for read/write units, Data Wrangler bills for processing time on ml.m5.4xlarge instances, and Model Monitor costs vary based on monitoring frequency.

AWS offers a free tier for the first two months: 250 hours of notebook usage on ml.t3.medium, 50 hours of ml.m5.xlarge for training, and 125 hours of ml.m5.xlarge for inference.

After that, costs accumulate quickly. Running 10 real-time endpoints on ml.m5.xlarge instances costs approximately $1,650/month minimum, not counting traffic, storage, or additional services.

Pros and Cons

SageMaker offers fast setup, automatic scaling, and built-in compliance, making it ideal for teams that prioritize speed and managed infrastructure. However, costs can rise quickly, flexibility is limited, and migration outside AWS is challenging.

Pros	Cons
Zero infrastructure setup – deploy endpoints quickly	Costs add up with always-on endpoints and premium instances
Automatic scaling handles traffic spikes	Limited flexibility – tied to AWS runtime environment
Built-in compliance certifications reduce audit work	Vendor lock-in – migration can be difficult
Integration with AWS services simplifies authentication	Complex pricing requires ongoing monitoring

What is Seldon Core?

Seldon Core is an open-source MLOps framework for deploying machine learning models on Kubernetes. The project started as a CNCF sandbox project and focuses on giving you control over model serving infrastructure. Version 2 of Seldon Core introduced multi-model serving, allowing multiple models to run on shared infrastructure for better resource utilization.

You define models as Kubernetes custom resources, and Seldon's operator handles deployment, scaling, and traffic routing. The framework works with any model you can containerize, supporting TensorFlow, PyTorch, scikit-learn, XGBoost, ONNX, and custom Python code. Seldon Core 2 uses a data-centric approach designed for real-time deployment complexity in use cases like search, fraud detection, and recommendations.

Key Features of Seldon Core

Seldon handles model serving with support for canary deployments, A/B testing, and shadow deployments. You route traffic percentages between model versions, gradually shifting based on performance metrics. The framework includes explainability through Alibi integration, providing SHAP values, anchor explanations, and counterfactuals alongside predictions.

Metrics export to Prometheus automatically, tracking request latency, prediction counts, error rates, and custom model metrics. Integration with KServe provides standardized inference protocols across frameworks. Models deploy as REST endpoints, gRPC services, or both. Seldon Core 2 supports pipelines that compose multiple models with Kafka for real-time data streaming between components.

Multi-model serving consolidates multiple models on shared inference servers, reducing infrastructure costs through resource pooling. Autoscaling works with native Kubernetes HPA or custom logic based on traffic patterns. The framework includes overcommit capabilities to maximize resource utilization while maintaining latency requirements.

Deployment Options

You deploy Seldon Core on any Kubernetes cluster, whether that's EKS, GKE, on-premise infrastructure, or your local minikube setup. Helm charts handle installation, and you configure models through YAML manifests. Each model runs as a deployment with configurable replicas, resource requests, and liveness probes.

The containerized approach means you package models with their exact dependencies. You build a Docker image with your model file, inference code, and required libraries, then reference that image in your Seldon deployment manifest.

Target Users & Use Cases

DevOps teams with Kubernetes experience adopt Seldon Core because it matches their operational patterns. Research organizations use it when experimenting with custom serving logic or model architectures not supported by managed platforms. Tech companies with multi-cloud strategies use Seldon to maintain portability across AWS, GCP, Azure, and on-premise infrastructure.

The framework fits teams running production microservices on Kubernetes who want to apply the same operational practices to ML models. Banking, finance, automotive, and insurance industries use Seldon when they need complete control over model deployment infrastructure. Organizations deploying data-critical, real-time models where milliseconds matter choose Seldon for its performance optimization features.

Pricing Model

Seldon Core offers flexible deployment options, from a free lightweight server to commercial enterprise plans. Costs vary based on infrastructure, model count, and optional modules, making it potentially cost-efficient at scale but requiring careful planning.

Tier	Description	Cost Notes
MLServer	Open-source lightweight inference server	Free (Business Source License)
Seldon Core 2	Commercial license	Depends on model count & infrastructure
Seldon Core+	Enterprise support	Includes Customer Success Manager, SLAs, optional modules
Optional Modules	LLM Module, Metrics, Alibi Detect/Explain	Separate license required
Infrastructure	Kubernetes cluster (e.g., 3×t3.medium nodes)	~$75/month compute; 10 models ≈ $500–1,000/month plus license

Pros and Cons

Seldon Core gives full control and flexibility for teams experienced with Kubernetes. While it can reduce costs with optimized resource use, it requires handling all operational tasks and navigating a steep learning curve.

Pros	Cons
Full control over infrastructure & serving logic	Steep learning curve
No vendor lock-in	Must handle security, monitoring, and incidents
Flexible Kubernetes-native deployment	Documentation assumes cloud-native familiarity
Lower costs if resources optimized	Support mainly via GitHub issues

Detailed Feature Comparison: SageMaker vs Seldon Core

Machine Learning Model Deployment

SageMaker provides REST APIs that handle deployment details. You call create_endpoint() with a model artifact and instance type, and AWS provisions the infrastructure. The endpoint URL works immediately after deployment completes. Updates happen through blue-green deployments that SageMaker orchestrates automatically.

Seldon requires you to build a container image, push it to a registry, and create a SeldonDeployment custom resource. You specify replica counts, resource limits, and traffic routing rules in YAML. Updates require applying new manifests, and Kubernetes handles the rollout based on your deployment strategy.

SageMaker optimizes for speed and simplicity. Seldon optimizes for control and customization. The trade-off comes down to whether you value getting endpoints live quickly or having fine-grained control over serving behavior.

Scalability and Performance

SageMaker autoscaling monitors CloudWatch metrics like CPU usage or request count. You set target values (like 70% CPU), and SageMaker adds or removes instances. The scaling latency is typically 2–5 minutes, which works for gradual traffic increases but struggles with sudden spikes.

Seldon uses Kubernetes Horizontal Pod Autoscaler, which scales based on CPU, memory, or custom metrics from Prometheus. Scaling latency depends on your cluster's node autoscaling configuration. With cluster autoscaler properly configured, you can achieve sub-minute scaling for pod additions within existing nodes.

For batch inference, SageMaker distributes work across multiple instances automatically. You specify instance count, and it partitions your S3 dataset. Seldon requires you to implement batch processing logic in your serving container or use separate job orchestration.

Integration Capabilities

SageMaker integrates deeply with AWS services. S3 for data storage, IAM for access control, CloudWatch for logs, EventBridge for workflow triggers, and Step Functions for complex pipelines. If you use AWS, these integrations reduce configuration overhead. If you use GCP or Azure, you'll need to set up cross-cloud networking and authentication.

Seldon works with any tool that speaks Kubernetes. MLflow for experiment tracking, Prometheus for metrics, Grafana for dashboards, ArgoCD for GitOps deployments, and Kubeflow for pipeline orchestration. The open ecosystem means you choose tools based on requirements rather than vendor compatibility.

Security & Compliance

SageMaker inherits AWS's security model. IAM controls who can create endpoints, VPC isolation keeps traffic private, and encryption at rest uses KMS. AWS maintains compliance certifications (SOC 2, ISO 27001, HIPAA, PCI DSS), which reduces audit burden. You configure security through AWS console policies rather than network policies or pod security contexts.

Seldon security depends on your Kubernetes setup. You configure network policies to control pod-to-pod communication, use RBAC for access control, and set up mTLS for encrypted traffic. Certificate management requires tools like cert-manager. Meeting compliance requirements means you handle documentation and controls yourself, though you have full visibility into what's happening.

Community and Support

SageMaker support comes through AWS support plans (Developer at $29/month, Business at $100/month minimum). You get access to AWS engineers who handle platform issues. Documentation includes tutorials, API references, and example notebooks. The closed-source nature means you can't fix bugs yourself or see implementation details.

Seldon Core has 4,200+ GitHub stars with active maintainers responding to issues. Documentation covers deployment patterns, troubleshooting guides, and API specifications. Community support happens through GitHub issues and Slack channels. For enterprise support, Seldon offers paid contracts with SLAs and dedicated engineering help.

Which Is Better for Your Business?

Use Case Scenarios

Fast Startup Prototyping: If you're a three-person startup validating an ML product idea, SageMaker gets you live faster. You don't have time to learn Kubernetes or debug cluster networking. The higher costs matter less than speed to market.

Regulated Enterprise Deployment: Financial services companies with strict compliance requirements benefit from SageMaker's pre-certified environment. The audit trail, encryption defaults, and compliance documentation reduce your security team's workload.

Multi-Cloud Strategy: Companies avoiding vendor lock-in or running workloads across AWS and GCP use Seldon Core for portable deployments. You can run identical model serving configurations on different cloud providers.

High-Volume Inference with Cost Sensitivity: Organizations serving millions of predictions daily often find Seldon Core cheaper at scale. The ability to optimize instance types, use spot instances, and pack multiple models per node reduces infrastructure costs by 40-60% compared to SageMaker's pricing.

Cost vs Benefit Analysis

SageMaker's total cost includes AWS charges plus reduced engineering overhead. If hiring a DevOps engineer costs $150,000/year and SageMaker saves 50% of their time, you're getting value even at $3,000/month infrastructure costs.

Seldon Core's total cost includes infrastructure plus engineering time. A Kubernetes cluster might cost $800/month, but if debugging Seldon issues takes 10 hours/month at $100/hour loaded cost, you're at $1,800 total. The break-even point depends on model count, traffic volume, and team expertise.

For 2–3 models with moderate traffic, SageMaker usually wins on total cost. For 20+ models with high traffic and an experienced DevOps team, Seldon Core becomes cheaper.

Ease of Use

SageMaker's web console lets data scientists deploy models without writing infrastructure code. The Studio interface provides notebooks, experiment tracking, and deployment in one place. You can go from trained model to production endpoint in 30 minutes.

Seldon Core requires comfort with command-line tools, Docker, and Kubernetes manifests. The learning curve is steep if you're coming from pure data science backgrounds. Once you understand the patterns, deployments become reproducible and version-controlled through GitOps workflows.

Getting Started

SageMaker is great if you want speed, minimal infrastructure hassle, and are already in AWS. It suits teams without Kubernetes experience or those prioritizing compliance over cost.

Seldon Core works for teams comfortable with Kubernetes, multi-cloud setups, or optimizing infrastructure costs. It takes some learning but gives more control and flexibility.

You can also connect with us for guidance on choosing the right MLOps platform or optimizing your ML workflows for faster, cost-effective deployment.

Frequently Asked Questions

Is Amazon SageMaker better than Seldon Core?

SageMaker offers easier onboarding with managed infrastructure, while Seldon Core provides more control over deployment architecture. Better depends on your team's Kubernetes experience and whether you prioritize speed or flexibility. Neither is objectively superior across all use cases.

Can Seldon Core replace SageMaker?

Yes, especially for teams comfortable with Kubernetes. You'll handle more operational details like monitoring setup and security configuration. The trade-off is gaining deployment flexibility and avoiding vendor lock-in. Migration requires containerizing models and learning Seldon's deployment patterns.

What is the difference between SageMaker and Seldon Core?

SageMaker is a managed platform from AWS that handles infrastructure provisioning, scaling, and monitoring automatically. Seldon Core is an open-source framework built for Kubernetes where you control infrastructure configuration and deployment strategies. SageMaker optimizes for convenience, Seldon for customization.

Which is more suitable for startups or enterprises?

Startups benefit from SageMaker's fast setup and managed operations when engineering resources are limited. Enterprises with existing Kubernetes infrastructure and DevOps teams gain more value from Seldon's flexibility and lower infrastructure costs at scale. Resource availability matters more than company size.

How do I migrate from SageMaker to Seldon Core (step-by-step)?

First, export your trained model artifacts from S3. Second, create a Docker container with your model, inference code, and dependencies. Third, push the container to a registry like ECR or Docker Hub. Fourth, write a SeldonDeployment manifest specifying your container image and resource requirements. Finally, apply the manifest to your Kubernetes cluster and test the new endpoint before redirecting production traffic.

What is the actual monthly cost for running 10 models on each platform?

SageMaker costs $1,500-3,000/month for 10 real-time endpoints using ml.m5.xlarge instances with autoscaling. Seldon Core infrastructure runs $600-1,200/month for a Kubernetes cluster with appropriate sizing, assuming efficient resource allocation. These estimates assume moderate traffic. Use AWS Pricing Calculator for precise SageMaker costs based on your instance types and usage patterns.

What breaks most often in Seldon Core deployments and how do I fix it?

Ingress misconfigurations cause 404 errors when routing traffic to model endpoints. Check your ingress controller annotations and service configurations. Container crashes from OOM errors happen with insufficient memory limits. Increase resource requests in your deployment manifest. Missing Prometheus metrics result from incorrect annotations. Verify your pod has the required monitoring labels.

Can I use SageMaker models with Seldon Core inference?

Yes. Download model artifacts from SageMaker's S3 bucket, then package them into a Docker container with inference logic compatible with Seldon's prediction protocol. You'll need to implement the predict method that Seldon expects, but you can reuse the trained model weights and architecture from SageMaker.

How long does it take to deploy first model on each (with timeline)?

SageMaker takes 30-90 minutes from account setup to live endpoint using the console or SDK. Seldon Core requires 4-8 hours assuming an existing Kubernetes cluster. This includes writing the deployment manifest, building the container image, pushing to a registry, and troubleshooting initial configuration. Add several days if you're provisioning Kubernetes infrastructure from scratch.

Can I run both SageMaker and Seldon Core in parallel?

Yes. Many teams train models in SageMaker using its managed training infrastructure, then export artifacts for deployment on Seldon Core. This hybrid approach works well during migration periods or when you want managed training with self-hosted inference. You can also use SageMaker for some models and Seldon for others based on requirements.

How do I monitor costs in real-time on each platform?

SageMaker costs appear in AWS Cost Explorer with daily granularity and CloudWatch dashboards showing instance utilization. Set up billing alerts in AWS Budgets. For Seldon Core, use your cloud provider's billing dashboard (AWS, GCP, Azure) combined with Prometheus metrics for resource usage. Grafana dashboards help correlate usage with costs, but requires manual setup.

How do they handle model versioning differently?

SageMaker provides Model Registry with built-in version tracking, approval workflows, and metadata tagging. You register models through the SDK, and the registry maintains lineage. Seldon Core uses container image tags for versioning combined with GitOps practices. You track versions through your container registry and deployment manifests in source control, giving more flexibility but requiring manual processes.