SageMaker is a comprehensive end-to-end ML platform integrated with AWS services, while Weights & Biases specializes in experiment tracking, model monitoring, and collaboration with superior visualization tools. SageMaker offers broader infrastructure capabilities; W&B excels at iterative development and team workflows.
AWS SageMaker vs Weights & Biases — Side-by-side comparison on A Versus B
Fully managed machine learning service for building, training, and deploying models at scale on AWS infrastructure.
Lightweight experiment tracking and model registry platform enabling collaboration across any ML framework and cloud environment.
Primary Use Case. AWS Integration. Experiment Tracking Dashboard. Model Registry & Governance. Learning Curve. Pricing Model. Team Collaboration Features
Choose AWS SageMaker if you need a complete ML platform integrated with AWS infrastructure, require automated model deployment pipelines, and are already invested in the AWS ecosystem. Choose Weights & Biases if you prioritize experiment tracking clarity, need a lightweight tool for iterative development, want cloud flexibility, and value team collaboration features—it pairs well with any training framework or deployment platform.
AWS SageMaker: 45-120 minutes | Weights & Biases: 15-30 minutes (cloud account required)
AWS SageMaker: TensorFlow, PyTorch, Scikit-learn, MXNet, Hugging Face (5 major) | Weights & Biases: 100+ (PyTorch, TensorFlow, scikit-learn, XGBoost, Keras, etc.)
AWS SageMaker: $0 (pay-per-use: $0.50-50/hour training) | Weights & Biases: $0-600/month (team seats)
AWS SageMaker: 12-15 basic charts | Weights & Biases: 50+ interactive visualizations
AWS SageMaker: Full end-to-end (1-click production deployment) | Weights & Biases: None (requires external deployment tools)
AWS SageMaker: 200+ AWS services (native) | Weights & Biases: AWS integrations via API (manual setup)
AWS SageMaker: Basic shared notebooks (3 features) | Weights & Biases: Reports, alerts, @mentions, comments (8 features)
AWS SageMaker: 1.2M+ monthly active users | Weights & Biases: 500K+ registered users (2024)
AWS SageMaker: AWS only | Weights & Biases: N/A
AWS SageMaker: $0.23 (ml.t3.medium on-demand) | Weights & Biases: N/A
AWS SageMaker: 50+ algorithms via Autopilot | Weights & Biases: N/A
AWS SageMaker: Up to 5 (with delays) | Weights & Biases: N/A
AWS SageMaker: 70+ (S3, RDS, Glue, Lambda, etc.) | Weights & Biases: N/A
AWS SageMaker: Third-party integration only | Weights & Biases: N/A
AWS SageMaker: Up to 90% savings | Weights & Biases: N/A
SQL Query Performance (sample 1TB table)
AWS SageMaker: 45-60 (via Athena integration) | Weights & Biases: N/A
AWS SageMaker: 70+ algorithms | Weights & Biases: N/A
AWS SageMaker: 3 hours | Weights & Biases: N/A
AWS SageMaker: 37% | Weights & Biases: N/A
AWS SageMaker: 100+ AWS services | Weights & Biases: N/A
AWS SageMaker: 250 hours (m5.large) | Weights & Biases: N/A
AWS SageMaker: Code-based (CloudFormation/CDK) | Weights & Biases: N/A
AWS SageMaker: Limited (third-party via Bedrock) | Weights & Biases: N/A
AWS SageMaker: 500+ models | Weights & Biases: N/A
AWS SageMaker: 25-40 minutes (AWS account setup, IAM roles, notebook configuration) | Weights & Biases: 3-5 minutes (login, install wandb library, 2 lines of code)
AWS SageMaker: 8 major frameworks (TensorFlow, PyTorch, MXNet, etc. via SageMaker SDK) | Weights & Biases: 500+ integrations and automatic logging across frameworks
Monthly Cost for Small Team (2-5 users, light usage)
AWS SageMaker: $200-500 (notebook instance $0.25/hour + storage) | Weights & Biases: $0 (free tier includes team collaboration)
AWS SageMaker: 2-5 minutes (one-click SageMaker endpoint deployment) | Weights & Biases: N/A (requires external deployment tool like Docker + Kubernetes)
Experiment Comparison Dashboard Features
AWS SageMaker: 5-7 features (custom metrics, parameter comparison via SageMaker Experiments) | Weights & Biases: 15+ features (parallel coordinates, parameter importance, system metrics, artifact diffs)
AWS SageMaker: 1 (free tier limited to personal use only) | Weights & Biases: Unlimited (free tier includes full team collaboration)
AWS SageMaker: AWS only (100% dependent on single vendor) | Weights & Biases: Any cloud (AWS, GCP, Azure, on-premise supported equally)
AWS SageMaker: 15-25 lines (import SageMaker, configure session, define job) | Weights & Biases: 2-3 lines (import wandb, wandb.init(), wandb.log())
AWS SageMaker: N/A | Weights & Biases: $0-$600+
AWS SageMaker: N/A | Weights & Biases: 4.7/5
AWS SageMaker: N/A | Weights & Biases: 5-10 minutes
AWS SageMaker: N/A | Weights & Biases: 80-200ms
AWS SageMaker: N/A | Weights & Biases: Yes (native)
AWS SageMaker: N/A | Weights & Biases: No (SaaS only)
AWS SageMaker: N/A | Weights & Biases: 500,000+
AWS SageMaker: N/A | Weights & Biases: 100 GB
AWS SageMaker: N/A | Weights & Biases: 150+
AWS SageMaker: N/A | Weights & Biases: $5,000
AWS SageMaker: N/A | Weights & Biases: 60% (W&B Local limited)
AWS SageMaker: N/A | Weights & Biases: Unlimited
AWS SageMaker: N/A | Weights & Biases: 5x (W&B Sweeps)
AWS SageMaker: N/A | Weights & Biases: $12
AWS SageMaker: N/A | Weights & Biases: 20+
AWS SageMaker: N/A | Weights & Biases: 7-10
AWS SageMaker: N/A | Weights & Biases: 100
AWS SageMaker: N/A | Weights & Biases: 1
AWS SageMaker: N/A | Weights & Biases: Yes (Sweeps with Bayesian optimization)
AWS SageMaker: N/A | Weights & Biases: Cloud-hosted (multi-region available)
AWS SageMaker: N/A | Weights & Biases: 100GB/month
AWS SageMaker: N/A | Weights & Biases: 10 concurrent
AWS SageMaker: N/A | Weights & Biases: 100 max
AWS SageMaker: N/A | Weights & Biases: 40+
AWS SageMaker: N/A | Weights & Biases: $200M total
AWS SageMaker: N/A | Weights & Biases: 8,500+
AWS SageMaker: N/A | Weights & Biases: 3 minutes
AWS SageMaker: N/A | Weights & Biases: 18,000+
AWS SageMaker: N/A | Weights & Biases: 45 ms (cloud API)
AWS SageMaker: N/A | Weights & Biases: 100+
AWS SageMaker: N/A | Weights & Biases: Yes (via paid plans)
AWS SageMaker: N/A | Weights & Biases: No (metadata only)
AWS SageMaker: N/A | Weights & Biases: $0 (freemium)
AWS SageMaker: N/A | Weights & Biases: $60-495 (Team/Pro plan)
AWS SageMaker: N/A | Weights & Biases: 0.1 days (5-10 minutes)
AWS SageMaker: N/A | Weights & Biases: 200+
AWS SageMaker: N/A | Weights & Biases: Lineage tracking, governance, alias management, stage transitions
AWS SageMaker: N/A | Weights & Biases: 11,200
AWS SageMaker: N/A | Weights & Biases: Unlimited (cloud-managed)
AWS SageMaker: N/A | Weights & Biases: 6 (Bayesian, random, grid, population-based, etc.)
AWS SageMaker: N/A | Weights & Biases: $0 (free tier); $500+ (team)
AWS SageMaker: N/A | Weights & Biases: 3 users + unlimited service accounts
AWS SageMaker: N/A | Weights & Biases: 50+ (line, bar, scatter, heatmap, confusion matrix, ROC, custom HTML)
AWS SageMaker: N/A | Weights & Biases: AWS S3, GCS, Azure Blob, Hugging Face Hub
AWS SageMaker: N/A | Weights & Biases: ~7,500 stars
AWS SageMaker: N/A | Weights & Biases: 100-500ms (network dependent)
AWS SageMaker: N/A | Weights & Biases: $300/month (Pro plan at $12/user)
AWS SageMaker: N/A | Weights & Biases: 5
AWS SageMaker: N/A | Weights & Biases: 9 (PyTorch, TensorFlow, JAX, Keras, scikit-learn, XGBoost, Hugging Face, Spark, FastAI)
AWS SageMaker: N/A | Weights & Biases: 6 (shared workspace, comments, permissions, alerts, notifications, activity feed)
AWS SageMaker: N/A | Weights & Biases: 6 (Bayesian, random, grid, early stopping, learning rate finder, population-based training)
AWS SageMaker: N/A | Weights & Biases: ~500,000+ (cloud SaaS estimates)
AWS SageMaker: N/A | Weights & Biases: Yes (cloud platform)
How to choose between AWS SageMaker and Weights & Biases
Compare AWS SageMaker vs Weights & Biases
AWS SageMaker vs Weights & Biases — Structured Comparison Data
Machine-readable comparison dataset for AWS SageMaker vs Weights & Biases. Contains 90 attribute comparisons, 5 FAQ pairs.
AWS SageMaker vs Weights & Biases — Attribute Category Vocabulary
Taxonomy of attribute categories used to classify comparison data for AWS SageMaker vs Weights & Biases.
Can I use Weights & Biases with SageMaker?
Yes. Many teams use W&B for experiment tracking during development, then deploy trained models via SageMaker. W&B integrates with SageMaker through custom logging scripts. This hybrid approach gives you W&B's superior tracking UI + SageMaker's production deployment automation.
Weights & Biases is cheaper for teams <5 people ($0-50/month with free tier). SageMaker's pay-per-use model becomes unpredictable; a single training job on a p3.2xlarge GPU costs $24.48/hour. For occasional experiments, W&B's flat subscription dominates.
Does SageMaker track experiments like W&B?
SageMaker has SageMaker Experiments for tracking, but it's primitive compared to W&B. SageMaker Experiments lacks interactive dashboards, custom charts, and real-time collaboration. Most users pair SageMaker with W&B for tracking, then use SageMaker only for production deployment.
Can I deploy models trained in W&B to production?
W&B doesn't deploy models directly. You must export trained models and deploy via SageMaker, Ray Serve, Hugging Face Spaces, or custom containers. This is a workflow step, not a limitation—many prefer this separation of concerns.
Which scales better for large teams (50+ ML engineers)?
SageMaker scales better for large enterprises due to governance, audit trails, and AWS compliance integrations (HIPAA, FedRAMP). W&B scales through team organization and reports, but lacks fine-grained access control. For 50+ engineers, SageMaker's governance wins; pair it with W&B for tracking.
How to Choose Between AWS SageMaker and Weights & Biases
Primary Use Case: AWS SageMaker has End-to-end ML pipeline (data prep, training, deployment), while Weights & Biases has Experiment tracking, monitoring, and team collaboration.
AWS Integration: AWS SageMaker has Native integration with 200+ AWS services, while Weights & Biases has Cloud-agnostic, requires manual AWS configuration.
Experiment Tracking Dashboard: AWS SageMaker has Basic built-in tracking, limited visualization, while Weights & Biases has Industry-leading interactive dashboards with 50+ chart types.
Model Registry & Governance: AWS SageMaker has Full MLOps governance with automated deployment, while Weights & Biases has Model registry with version control, lighter governance.
Learning Curve: AWS SageMaker has Steep, requires AWS ecosystem knowledge, while Weights & Biases has Shallow, integrates with existing ML workflows (5-min setup).
Pricing Model: AWS SageMaker has Pay-per-use (training, hosting, storage separate), while Weights & Biases has Flat subscription ($0-600/month per team seat).
Team Collaboration Features: AWS SageMaker has Basic shared workspaces, limited real-time features, while Weights & Biases has Reports, alerts, @mentions, and real-time notifications.