SageMaker vs ClearML: Comparison Guide

Once models move beyond notebooks, MLOps choices become important. Training jobs overlap, experiments multiply, and deployments need to run consistently. That’s when teams look at Amazon SageMaker or ClearML.

Both cover the ML lifecycle, but in different ways. SageMaker focuses on managed AWS services, while ClearML focuses on open, infrastructure-controlled workflows. 

Let’s see in this guide how each platform works, the differences, and where each fits in real-world ML workflows.

Overview of SageMaker and ClearML

MLOps platforms eliminate much of the manual work in machine learning workflows. They handle experiment tracking, model training, deployment, and monitoring through automation rather than custom scripts. Amazon SageMaker and ClearML take different approaches to solving these problems.

SageMaker is AWS's fully managed ML platform. You write training code, select instance types, and AWS handles the infrastructure. The service integrates deeply with the AWS ecosystem, making it easier for teams already running on AWS to add ML capabilities. 

AWS launched SageMaker Unified Studio in December 2024, bringing together widely adopted AWS machine learning and analytics capabilities in an integrated experience for analytics and AI. The platform combines SageMaker AI (including HyperPod, JumpStart, and MLOps), generative AI application development through Amazon Bedrock, data processing, and SQL analytics, all accelerated by Amazon Q Developer.

ClearML is an open-source MLOps suite available under Apache License 2.0 that runs on any infrastructure. The platform includes five main modules: 

1. Experiment Manager – tracks experiments.

2. MLOps/LLMOps – orchestrates pipelines and automation.

3. Data Management – version control for object storage.

4. Model Serving – deploys endpoints.

5. Reports – creates shareable documentation.

   
   

ClearML can be self-hosted for free or used as a managed service, with paid tiers starting at $15 per user per month. It currently serves over 1,600 enterprise customers.

The fundamental distinction is control versus convenience. SageMaker abstracts infrastructure, which simplifies setup but ties you to AWS. ClearML gives full control over infrastructure but requires more DevOps effort to maintain.

Key Differences at a Glance

Factor	SageMaker	ClearML
Deployment	Fully managed by AWS	Self-hosted or managed cloud
Cloud Dependency	AWS-native	Cloud-agnostic
Pricing Model	Pay-per-use instances	Free (self-hosted) or subscription
Setup Complexity	Low (managed service)	Medium to high (self-hosting)
Flexibility	Limited to AWS services	Works with any infrastructure
Open Source	No	Yes (Apache 2.0)

Architecture and Core Components

SageMaker Architecture Breakdown

SageMaker provides integrated tools including Jupyter notebooks, distributed training, and real-time endpoints. The platform consists of several components:

SageMaker Studio serves as the unified development environment where you write code, run experiments, and monitor training jobs. The SageMaker Unified Studio provides fully managed notebooks with a built-in AI agent (SageMaker Data Agent) for data analysis that supports SQL, Python, and natural language interactions all within a single environment.

SageMaker AI provides the machine learning tools including model development, HyperPod for distributed training, JumpStart for pre-trained models, and MLOps capabilities. This component focuses specifically on ML workflows.

SageMaker Catalog (built on Amazon DataZone) provides unified governance and access control through entities like domains, projects, and assets. It enables secure discovery, governance, and collaboration on data and AI assets.

Lakehouse Architecture unifies data access across Amazon S3 data lakes, Amazon Redshift data warehouses, and third-party or federated data sources, all with governance built in for enterprise security needs.

Everything runs on AWS infrastructure with tight integration to S3 for storage, CloudWatch for monitoring, and IAM for permissions. This integration simplifies setup but limits portability to other cloud providers.

ClearML Architecture Breakdown

ClearML uses a modular architecture with several main components:

Experiment Manager automates experiment tracking, capturing environments and results. Adding just two lines to your code logs complete experiment setup, full source control info (including uncommitted changes), execution environment, hyperparameters, model snapshots, and full experiment output including stdout, stderr, and resource monitoring.

MLOps/LLMOps provides orchestration, automation, and pipeline solutions for ML/DL/GenAI jobs running on Kubernetes, cloud, or bare-metal infrastructure. The ClearML Agent handles remote execution and reproducibility.

Data Management offers fully differentiable data management and version control on top of object storage (S3, GS, Azure, NAS). The clearml-data CLI manages and versions datasets, including creating, uploading, and downloading data.

Model Serving provides cloud-ready scalable model serving with deployment of new endpoints in under 5 minutes. It includes optimized GPU serving backed by Nvidia Triton with out-of-the-box model monitoring.

ClearML Server stores experiment metadata, artifacts, and project information. You can self-host on Kubernetes, bare metal, or use ClearML's managed service. Storage remains separate from ClearML, configurable for S3, GCS, Azure, NFS, or HTTP endpoints.

This architecture works on Kubernetes natively, supports bare-metal deployments, and integrates with any cloud provider. The trade-off is configuration overhead compared to SageMaker's managed approach.

How the Platforms Scale

SageMaker scales through AWS's infrastructure. You select larger instance types or add more instances to endpoints. Auto-scaling policies adjust capacity based on traffic. For real-time inference endpoints, you can integrate Application Auto Scaling and scale-to-zero strategies. The platform handles distribution and load balancing automatically.

For training, SageMaker supports distributed training across multiple instances. You specify the cluster size, and SageMaker configures networking between nodes. Spot instances reduce costs for fault-tolerant workloads.

ClearML scales horizontally by adding more agents. Each agent can handle multiple tasks depending on resource availability. You control the infrastructure, so scaling means provisioning more VMs or Kubernetes nodes and registering new agents.

The queue system distributes work automatically. High-priority tasks go first, and agents pull work based on capacity. This model works well for heterogeneous environments where different experiments need different resources.

Features and Capabilities

Model Training and Experiment Tracking

SageMaker Experiments logs parameters, metrics, and artifacts automatically through the SageMaker SDK. The UI organizes experiments in a table where you can sort by metrics and compare runs. Training jobs run on the instance types you select, while SageMaker manages orchestration.

• Free Tier: 250 hours on sc.t3.medium notebooks for the first two months.

• Costs after free tier: Pay-per-second for compute; notebook storage $0.112 per GB-month.

  
  

ClearML tracks experiments automatically with minimal code. It records hyperparameters, metrics, console output, and artifacts, as well as the execution environment, including installed packages. The platform supports TensorBoard, Matplotlib, Seaborn, and frameworks such as PyTorch (ignite/lightning), TensorFlow, Keras, AutoKeras, FastAI, XGBoost, LightGBM, MegEngine, and Scikit-Learn.

The web UI allows filtering, search, parallel coordinates, scatter plots, and metric graphs. Experiments can be cloned with modified parameters and queued for execution, supporting repeated runs efficiently.

Feature	SageMaker	ClearML
Experiment tracking	SDK + table UI	Automatic via code instrumentation, visualizations
Framework support	PyTorch, TensorFlow, scikit-learn, XGBoost	Broad ML/DL framework support, Python-native
Iteration	Managed jobs and table comparisons	Clone experiments and queue runs

Deployment and Serving

SageMaker endpoints provide real-time inference with automatic scaling. Endpoint configuration defines instance type and count, while SageMaker handles HTTPS, load balancing, and SSL. Batch Transform allows asynchronous batch processing. Production variants enable A/B testing by routing traffic to multiple model versions and tracking metrics.

ClearML deploys models through serving pipelines that run on agents. You define how the pipeline loads the model, processes requests, and returns predictions. It integrates with TensorFlow Serving, TorchServe, or custom Flask APIs, giving teams control over deployment and infrastructure.

AutoML and Customization Options

SageMaker Autopilot automates model building, algorithm selection, and hyperparameter tuning. SageMaker Canvas provides a no-code interface for analysts to create models for tabular, time series, or computer vision tasks. Automation reduces setup effort but limits control over search space and algorithm selection. Full API access allows for custom training scripts.

ClearML does not include built-in AutoML. It orchestrates hyperparameter optimization through Optuna or custom scripts. You control which hyperparameters and algorithms to test, as well as evaluation criteria. This requires more setup but allows precise control over experiments.

Integration with Tools and Frameworks

SageMaker supports PyTorch, TensorFlow, scikit-learn, XGBoost, and other frameworks via pre-built containers. Custom containers are also possible. Git integration allows version-controlled scripts. 

The platform integrates with HyperPod (distributed training), JumpStart (pre-trained models), and AWS services including S3, CloudWatch, IAM, Lake Formation, and Glue Data Catalog. Storage is typically S3 for data lakes and Redshift for warehouses, with support for federated sources.

ClearML integrates with any Python framework via SDK instrumentation. Additional modules include:

• clearml-session – remote JupyterLab or VSCode-server.

• clearml-task – remote code execution with logging.

• AWS Auto-Scaler – scales EC2 instances based on workload.

• Slack integration – progress updates.

ClearML also supports hyperparameter optimization, automation pipelines, and pipelines of pipelines. Git integration captures repository, branch, and commit hash for reproducibility. Metadata is stored separately, allowing data to reside on S3, GCS, Azure Blob, or NAS.

Real-World Use Cases and Examples

Enterprise Adoption Examples

SageMaker is often chosen by large enterprises that are already using AWS. Its integration with the AWS ecosystem simplifies connecting ML pipelines to existing data systems. Common applications include:

• Fraud detection: scoring transactions and monitoring risk.

• Demand forecasting: predicting inventory or sales trends.

• Predictive maintenance: tracking equipment to schedule maintenance.

ClearML is used by organizations that need flexible experiment management and infrastructure control. Examples include:

• BlackSky: uses ClearML to manage and scale AI/ML model training for space-based imagery analytics.

• Nucleai: adopted ClearML for experiment tracking and data monitoring

ClearML’s modular approach allows teams to configure infrastructure on-premises or in the cloud based on their requirements.

Startups and Small Teams

Startups with limited DevOps resources benefit from SageMaker's managed infrastructure. You don't need to maintain Kubernetes clusters or manage GPU servers. The downside is the complexity of SageMaker's pricing, with 12 components, four instance classes, and dozens of instance combinations complicating cost visibility.

ClearML works well for cost-conscious startups. The open-source version is free, and you only pay for compute infrastructure. ClearML's Community plan is free with up to 3 users, 100GB artifact storage, and 1M API calls per month. This lets teams start without upfront costs.

Self-hosting ClearML adds DevOps complexity. You need to set up servers, configure storage, and manage agents. Teams with DevOps experience can gain cost efficiency and more control.

Feature	SageMaker	ClearML
Cost	Pay-per-use compute, managed services	Free OSS version, pay for compute or managed tiers
Setup	Quick, managed	Requires DevOps setup if self-hosted
Flexibility	Limited to AWS ecosystem	Can run on any infrastructure, modular pipelines

AI Research and Prototyping

Academic teams often prefer ClearML because it's open source and they control the infrastructure. Universities can run ClearML on on-premise clusters without AWS costs. The platform supports custom workflows that research often requires.

SageMaker works for research teams with AWS credits or those prioritizing rapid experimentation over infrastructure control. The managed service lets researchers focus on experiments rather than DevOps.

Pricing Models and Cost Considerations

AWS SageMaker Pricing Overview

SageMaker uses a pay-as-you-go model where you pay separately for each AWS service you use through the platform. Pricing varies by service component:

SageMaker Unified Studio Free Tier:

• Core setup, project, and user management are free.

• 250 hours of sc.t3.medium notebook instances for the first two months.

• AWS Free Tier allocations apply.

SageMaker Catalog (built on Amazon DataZone):

• Free usage: 20 MB metadata storage, 4,000 API requests, 0.2 compute units per month.

• After free tier: $0.40 per GB metadata storage, $10 per 100,000 requests, $1.776 per compute unit.

• Core APIs (CreateDomain, CreateProject, Search) are always free

Notebooks:

• Instance-based pricing depending on type selected.

• Storage: $0.112 per GB-month.

SageMaker Data Agent:

• $0.04 per credit (pay-as-you-go).

• Simple prompts consume less than 1 credit.

• Complete data transformation pipelines cost 4-8 credits ($0.15-$0.30).

Training and Inference:

• Costs vary by instance type and region.

• Additional charges for storage, data processing, and deployment.

For accurate estimates, AWS recommends reviewing individual pricing for each service component you plan to use.

ClearML Pricing Overview

ClearML offers multiple deployment options with different pricing:

Community (Free):

• Up to 3 users

• 100GB artifact storage

• 1GB metric events

• 1M API calls per month

• Includes all core features: dataset versioning, model training, experiment management, model repository, artifacts, pipelines, agent orchestration, CI/CD automation, reports.

Pro ($15 per user/month):

• Up to 10 users

• All Community features plus cloud auto-scaling (AWS, GCP, Azure), hyperparameter optimization, pipeline triggers and automations, dashboards

• 120GB artifact storage (20% more than Community)

• 1.2GB metric events

• 1.2M API calls per month

• Pay-as-you-go for additional usage: $0.1 per GB storage, $0.01 per MB metric events, $1 per 100K API calls, $0.04/hr per application

Scale (Custom Quote):

• For organizations with 8–48 GPUs

• VPC deployment only

• Pro features plus Hyper-Datasets, fine-tuning, IDE launcher, vector database integration, Kubernetes integration, task scheduling, alerts, SSO, private Slack support, SLA

• Includes Infrastructure Control Plane: hardware and cloud-agnostic orchestration, job scheduling, multi-cluster support, fractional GPUs

Enterprise (Custom Quote):

• For organizations with multiple large projects

• VPC or on-premise cluster

• Scale features plus ClearML custom apps, configuration vault, Slurm/PBS integration, LDAP, role-based access control, white-glove support, professional services

• Advanced scheduling, dynamic fractional GPUs, resource allocation policy management, on-premise, air-gapped, multi-cloud, or hybrid setups

Self-hosting is 100% open source on GitHub under Apache License 2.0, with costs depending entirely on your infrastructure.

Cost Optimization Tips

For SageMaker, use SageMaker Lifecycle Configurations to automatically stop notebooks after 30–60 minutes of inactivity. This prevents leaving instances running over weekends. Spot instances reduce training costs by up to 70% for fault-tolerant workloads.

Configure auto-scaling for endpoints to reduce capacity during off-hours. Teams running daytime-only workloads often cut endpoint costs in half through schedule-based scaling.

For ClearML, optimize compute costs by rightsizing agent resources. Use CPU-only instances for data processing and reserve GPUs for training. The queue system lets you mix instance types efficiently.

Choosing Based on Budget and Needs

Choose SageMaker when you need rapid deployment, have AWS budget, and want minimal DevOps overhead. The managed service costs more per compute hour but reduces engineering time. Teams with limited ML engineers benefit from SageMaker's automation.

Choose ClearML when you need cost control, multi-cloud flexibility, or have DevOps resources. The self-hosted option eliminates platform fees. Teams with existing Kubernetes expertise can deploy ClearML quickly.

Pros and Cons Summary

When SageMaker Is Better

• Teams already running infrastructure on AWS.

• Companies wanting managed infrastructure without DevOps overhead.

• Organizations in highly regulated industries requiring AWS compliance certifications.

• Teams prioritizing rapid deployment over cost optimization.

• Enterprises with AWS Enterprise Support contracts.

SageMaker simplifies the path from experiment to production when you're already in AWS. The managed service handles infrastructure complexity, letting ML teams focus on models rather than DevOps.

When ClearML Is Better

• Startups and teams prioritizing cost control.

• Organizations requiring multi-cloud or on-premise deployment.

• Teams with existing DevOps expertise.

• Academic institutions and research labs.

• Companies avoiding vendor lock-in.

ClearML provides flexibility that SageMaker can't match. You control the infrastructure, choose any cloud provider, and avoid platform fees by self-hosting.

Getting Started

Choosing between SageMaker and ClearML depends on your infrastructure, team, and project requirements:

• Cloud: SageMaker for AWS; ClearML for multi-cloud or on-prem.

• Budget: ClearML self-hosted; SageMaker handles billing.

• Team: Small teams: SageMaker; DevOps teams: ClearML.

• Privacy: ClearML on-prem; SageMaker meets AWS compliance.

• Scaling: SageMaker auto-scales; ClearML allows manual control.

You can also reach out to us to discuss your ML infrastructure and get guidance on selecting, configuring, or integrating SageMaker and ClearML for your projects.

Frequently Asked Questions

How long does it take to migrate from SageMaker to ClearML (or vice versa)?

Migration time depends on how tightly your code couples to each platform's SDK. Basic workloads using standard frameworks (PyTorch, TensorFlow) take 2–4 weeks to migrate. You'll rewrite training scripts to use the target platform's APIs and reconfigure data pipelines.

Complex pipelines with heavy AWS dependencies (Step Functions, Lambda triggers, SageMaker-specific features) take 2–3 months. You need to re-architect workflows to work outside AWS or rebuild them in ClearML's orchestration system.

Run parallel testing environments during migration. Keep the old system running while validating the new one. This prevents disruption if migration takes longer than expected.

What breaks most often in SageMaker vs ClearML production deployments?

In SageMaker, common issues include IAM misconfigurations, endpoint scaling problems, and outdated containers. Policies that work in development may fail in production, endpoints can hit throttle limits, and older container images may break when AWS updates infrastructure. 

For ClearML, self-hosted setups often face agent disconnections, Kubernetes network restrictions, or storage misconfigurations. In both platforms, proper monitoring is critical: CloudWatch for SageMaker and agent plus server logs for ClearML.

Can I use ClearML with SageMaker endpoints simultaneously?

Yes. Use ClearML for experiment tracking and orchestration while deploying models to SageMaker endpoints. This hybrid setup works when teams want ClearML's open-source flexibility but need AWS scalability for inference.

The workflow involves training models with ClearML, exporting them to S3, then creating SageMaker endpoints pointing to those model artifacts. ClearML tracks experiments and metadata while SageMaker handles serving.

This approach adds complexity because you manage two systems. The benefit is leveraging strengths of both platforms.

How do I debug failed training jobs in each platform?

SageMaker: Check CloudWatch logs first. Every training job sends logs to CloudWatch automatically. Use SageMaker Studio debugger for real-time insights during training. The debugger captures tensors, detects issues like vanishing gradients, and provides profiling data.

Built-in metrics show resource utilization. If jobs fail due to out-of-memory errors, you'll see memory usage spike before failure.

ClearML: Check agent logs where the job ran. Agents capture console output and errors. The ClearML web UI shows experiment console output in real-time. Job artifacts include logs, so you can review them after completion.

For debugging, ClearML offers more flexibility since you control the infrastructure. You can SSH into the machine running the agent and inspect the environment directly.

How do I convince management to choose ClearML over SageMaker?

Focus on three points: cost efficiency, avoiding vendor lock-in, and control over infrastructure.

Cost efficiency: Show the difference between self-hosting ClearML ($0 platform fees + infrastructure costs) versus SageMaker (platform fees + infrastructure costs). For teams running many experiments, platform fees add up significantly.

Vendor lock-in: Explain that SageMaker tightly couples to AWS. If the company wants to use GCP or Azure later, SageMaker code doesn't transfer. ClearML works on any infrastructure.

Control: Emphasize that ClearML gives you full control over infrastructure, security, and data. Some industries require this level of control for compliance.

Back up claims with ClearML's enterprise customer list and GitHub engagement statistics showing active development and community support.

What's the disaster recovery process for each platform?

SageMaker: DR is built into AWS infrastructure with multi-AZ support and automated backups. Training jobs and endpoints fail over automatically if an availability zone goes down. Model artifacts stored in S3 have 99.999999999% durability with cross-region replication available.

For complete DR, enable S3 cross-region replication for model artifacts and use SageMaker in multiple regions. This requires configuring endpoints in each region and managing traffic routing.

ClearML: DR depends on how you deploy the server. For Kubernetes deployments, use persistent volumes with backup to object storage. The ClearML server stores metadata in MongoDB or PostgreSQL, which you need to backup regularly.

For self-hosted setups, plan DR around your database backup strategy. ClearML itself is stateless (the server component), so you can redeploy it quickly. The critical data is the database and artifacts in object storage.

How many DevOps engineers do I need for each platform?

SageMaker: Often minimal if sticking to managed services. One DevOps engineer can support multiple ML teams. They handle IAM policies, VPC configuration, and integration with other AWS services. As usage scales, you might need 1–2 engineers per 20-30 ML practitioners.

ClearML: Self-hosted setups require at least 1-2 DevOps or SRE engineers dedicated to managing infrastructure, scaling, and updates. They handle Kubernetes cluster management, agent provisioning, storage configuration, and monitoring setup. Managed ClearML reduces this need to minimal oversight similar to SageMaker.