Machine Learning · head to head
AWS SageMaker vs Checkly
AWS SageMaker
Machine Learning
Build, train, and deploy machine learning models at scale
- From
- Free
- Rated
- -
Checkly
Logging
Active reliability platform combining uptime monitoring, API testing, and incident response
- From
- Free
- Rated
- -
The short version
- Each has a real cost: AWS SageMaker vendor lock-in to AWS ecosystem makes migration to other platforms difficult; Checkly free tier has limited check allocations per month
- They diverge on capability: AWS SageMaker covers Jupyter notebooks, Checkly covers Uptime monitoring.
Where they differ
Only the attributes on which AWS SageMaker and Checkly actually diverge.
| Attribute | AWS SageMaker | Checkly |
|---|---|---|
| Pricing model | Unknown | subscription |
| Platforms | Web | Web, CLI, API |
| Category | Machine Learning | Logging |
| Founded | 2006 | Unknown |
Identical on both: starting price (Free), free tier (Yes), user rating (Not yet rated).
What each one covers
Drawn from each product's published feature list. An absence here means we hold no record of it - not that the product lacks it.
Only in AWS SageMaker
- Jupyter notebooks
- Built-in algorithms
- Automatic model tuning
- One-click deployment
- Model monitoring
- S3
- Lambda
- Step Functions
Only in Checkly
- Uptime monitoring
- Synthetic browser testing
- API monitoring
- Heartbeat monitoring
- Monitoring-as-Code
- Status pages
- Root cause analysis
- Global locations
What people use each for
The jobs each tool is most often brought in to do.
AWS SageMaker
- Machine learningnot Checkly
- Data analysisnot Checkly
- Model trainingnot Checkly
- Predictive analyticsnot Checkly
Checkly
- Monitor API endpoints with custom assertionsnot AWS SageMaker
- Test user journeys with browser automationnot AWS SageMaker
- Detect performance degradation across regionsnot AWS SageMaker
- Verify DNS and TCP connectivitynot AWS SageMaker
- Ensure cron jobs and background tasks completenot AWS SageMaker
Where each one falls short
Documented limitations, not opinions. Every one is a constraint you would hit in normal use.
AWS SageMaker
- Vendor lock-in to AWS ecosystem makes migration to other platforms difficult
- Opaque pricing can lead to unexpected expenses like forgotten EBS volume charges
- Does not include native job scheduling, requiring Lambda or EventBridge integration
Checkly
- Free tier has limited check allocations per month
- Overage charges can add up with high-volume workloads
- Status pages require separate paid tier
- Root cause analysis is separate billing component
Pricing, plan by plan
AWS SageMaker
FreeNo published plan breakdown. See the AWS SageMaker review.
Checkly
Free- HobbyFree
- 10 uptime monitors
- 1,000 browser checks monthly
- 10,000 API checks monthly
- Team$64/month
- 75 uptime monitors
- 12,000 browser checks monthly
- 100,000 API checks monthly
- Enterprise$undefined/custom
- Custom monitor quantities
- All 22 global locations
- 1-second check frequency
Which should you pick?
Choose AWS SageMaker if
- You need jupyter notebooks.
- You want to start without paying.
- You also want built-in algorithms.
Choose Checkly if
- You need uptime monitoring.
- You want to start without paying.
- You work on Web, CLI, API.
- You also want synthetic browser testing.
Questions people ask
- Is AWS SageMaker or Checkly better?
- Neither clearly leads. AWS SageMaker starts at Free and Checkly at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
- Which is cheaper, AWS SageMaker or Checkly?
- AWS SageMaker starts at Free and Checkly at Free.
- Does AWS SageMaker or Checkly run on more platforms?
- AWS SageMaker runs on Web. Checkly runs on Web, CLI, API.
- Can I use AWS SageMaker for free?
- Both have a free tier, so you can try either at no cost before committing.
- What is AWS SageMaker best used for?
- AWS SageMaker is most often used for machine learning, data analysis, model training, predictive analytics. Of those, machine learning and data analysis are not what Checkly is typically brought in for.
- What can AWS SageMaker do that Checkly cannot?
- AWS SageMaker covers Jupyter notebooks, Built-in algorithms, Automatic model tuning, One-click deployment. Checkly covers Uptime monitoring, Synthetic browser testing, API monitoring, Heartbeat monitoring.
Answered from the vendors’ own pages
AWS SageMaker: What is AWS SageMaker used for?
AWS SageMaker is a machine learning service for building, training, and deploying ML models at scale. It provides tools for data preparation, model training, inference endpoints, and performance optimization.
SourceCheckly: What is included in the free Checkly plan?
The free Hobby plan includes 10 uptime monitors, 1,000 monthly browser checks, 10,000 monthly API checks, 6 monitoring locations, and 2-minute minimum check frequency with email, Slack, and webhook alerts.
SourceAWS SageMaker: How is AWS SageMaker priced?
SageMaker uses pay-as-you-go pricing with no upfront costs or long-term commitments. Pricing starts at $0.04 per hour for basic notebook instances and scales based on instance type. ML Savings Plans offer up to 64% off with hourly spend commitments.
SourceCheckly: Can I write monitoring checks in my preferred language?
Yes, Checkly uses TypeScript/JavaScript for monitoring-as-code, integrated with Playwright for browser testing and supporting REST API testing.
SourceAWS SageMaker: Does AWS SageMaker have a free tier?
Yes, the free tier includes 250 hours of notebook usage, 50 hours of training, and 125 hours of hosting on ml.t3.medium instances during the first two months.
SourceCheckly: How much does status page add to my bill?
Status pages cost between $0-$30/month depending on your plan tier, billed separately from core monitoring.
SourceCheckly: What is the minimum check frequency?
The Hobby and Starter plans support 2-minute and 1-minute minimums respectively. The Team plan supports 30-second intervals, while Enterprise offers 1-second minimum frequency.
SourceRelated pages
More on AWS SageMaker
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