Seldon vs SageMaker

Side-by-side — pricing, strengths, tradeoffs, and fit.

Seldon

Efficiently move ML projects from POC to production with Seldon.

SageMaker

Build, train, and deploy ML models quickly with AWS SageMaker.

At a glance

Open SourceFree Tier
● Established·From $0
Open SourceFree Tier
◌ Declining·From Freemium

Overview

One-liner

Efficiently move ML projects from POC to production with Seldon.

One-liner

Build, train, and deploy ML models quickly with AWS SageMaker.

Maturity

Established

Maturity

Declining

Open source

✓ Yes

Open source

✓ Yes

Pricing

Free tier

✓ Available

Open source — free to use

Free tier

✓ Available

250 hours of t2.medium notebook usage per month for the first two months.

Starts at

$0

Starts at

Freemium

Model

Flat rate

Model

Usage-based

Enterprise

✕ None

Enterprise

✕ None

Best for

✓ Best for

Teams needing a robust platform for deploying ML models into production environments, especially those using Kubernetes.
Organizations that require automated monitoring and management of their deployed models.

✓ Best for

Teams needing a fully managed service for the entire ML lifecycle
Projects requiring integration with other AWS services
Developers who want to leverage built-in algorithms without writing custom code

Not ideal for

✕ Not for

Projects with very limited budgets as it requires significant setup and maintenance effort.
Small-scale projects or proofs-of-concept where a simpler deployment method is sufficient.

✕ Not for

Small projects that require minimal cost and can't afford usage-based pricing
Scenarios where on-premises or self-hosted solutions are preferred over cloud services

Strengths

↑ Strengths

Seamless deployment of ML models in Kubernetes environments
Automated model monitoring and management
Support for multiple machine learning frameworks
Integration with popular CI/CD pipelines

↑ Strengths

Fully managed service for ML model lifecycle management
Built-in algorithms and support for custom algorithms
AutoML capabilities with Amazon SageMaker Autopilot
Integration with AWS services like S3, Lambda, and DynamoDB

Weaknesses

↓ Weaknesses

Limited support for non-Kubernetes environments
Resource-intensive at scale

↓ Weaknesses

Vendor lock-in due to proprietary service
Expensive at scale for large datasets and models
Limited integrations outside AWS ecosystem
Full Seldon profile →Full SageMaker profile →