Machine Learning · head to head
AWS SageMaker vs DynamoDB
AWS SageMaker
Machine Learning
Build, train, and deploy machine learning models at scale
- From
- Free
- Rated
- -
DynamoDB
Databases
AWS-only managed key-value and document database with fixed per-partition throughput limits and no ad hoc queries.
- 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; DynamoDB access patterns must be designed into the key schema before launch; a query nobody anticipated needs a new global secondary index, which is a full extra copy of the projected attributes billed as storage and as writes, or an offline migration.
- They diverge on capability: AWS SageMaker covers Jupyter notebooks, DynamoDB covers Managed and serverless.
- Prices and features above were last checked on 30 August 2026.
Where they differ
Only the attributes on which AWS SageMaker and DynamoDB actually diverge.
| Attribute | AWS SageMaker | DynamoDB |
|---|---|---|
| Pricing model | Unknown | usage-based |
| Platforms | Web | AWS |
| Category | Machine Learning | Databases |
Identical on both: starting price (Free), free tier (Yes), user rating (Not yet rated), founded (2006).
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 DynamoDB
- Managed and serverless
- Predictable latency
- On-demand or provisioned capacity
- Global secondary indexes
- Transactions
- DynamoDB Streams
- Global tables
- Point-in-time recovery
What people use each for
The jobs each tool is most often brought in to do.
AWS SageMaker
- Machine learningnot DynamoDB
- Data analysisnot DynamoDB
- Model trainingnot DynamoDB
- Predictive analyticsnot DynamoDB
DynamoDB
- High-volume keyed workloads such as sessions, shopping carts, device state or user profiles where the access pattern is fixed and knownnot AWS SageMaker
- Traffic that spikes unpredictably, where on-demand capacity absorbs a burst without a capacity-planning exercisenot AWS SageMaker
- Serverless applications on Lambda, where an HTTP-based datastore avoids the connection pooling problem relational databases havenot AWS SageMaker
- Event or telemetry ingestion where writes vastly outnumber reads and each record is retrieved by a known identifiernot 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
DynamoDB
- Access patterns must be designed into the key schema before launch; a query nobody anticipated needs a new global secondary index, which is a full extra copy of the projected attributes billed as storage and as writes, or an offline migration.
- Global secondary indexes are eventually consistent and cannot be read strongly, so a read-after-write against an index can legitimately miss the item that was just written, and application code must be written to tolerate that.
- Per-partition throughput is capped at roughly 3,000 read and 1,000 write units, so a hot key throttles even when the table has spare capacity overall, and the only real fix is changing the key design to spread the load.
- Items are limited to 400 KB and query results paginate at 1 MB, so large or list-shaped data has to be split, offloaded to S3 with a pointer, or read through pagination loops that complicate every consumer.
- It runs only on AWS and the API is proprietary rather than a standard, so moving the data layer means rewriting it; ScyllaDB's Alternator is the only meaningfully compatible target and it brings a much smaller ecosystem.
Pricing, plan by plan
AWS SageMaker
FreeNo published plan breakdown. See the AWS SageMaker review.
DynamoDB
Free- On-Demand Capacity$null/usage-based
- Pay-per-request pricing with automatic scaling
- Read: 0.5 RRU per 4 KB (eventually consistent), 1 RRU per 4 KB (strongly consistent), 2 RRU per 4 KB (transactional)
- Write: 1 WRU per 1 KB
- Provisioned Capacity$null/hourly
- Fixed hourly charges based on reserved capacity
- RCU rate: $0.00013 per hour (Standard)
- WCU rate: $0.00065 per hour (Standard)
- Standard Table Class Storage$0.25/per GB/month
- $0.25 per GB/month after free tier
- First 25 GB free per month (free tier)
- Standard-Infrequent Access Table Class$0.1/per GB/month
- $0.10 per GB/month
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 DynamoDB if
- You need managed and serverless.
- You want to start without paying.
- You work on AWS.
- You also want predictable latency.
Questions people ask
- Is AWS SageMaker or DynamoDB better?
- Neither clearly leads. AWS SageMaker starts at Free and DynamoDB at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
- Which is cheaper, AWS SageMaker or DynamoDB?
- AWS SageMaker starts at Free and DynamoDB at Free.
- Does AWS SageMaker or DynamoDB run on more platforms?
- AWS SageMaker runs on Web. DynamoDB runs on AWS.
- 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 DynamoDB is typically brought in for.
- What can AWS SageMaker do that DynamoDB cannot?
- AWS SageMaker covers Jupyter notebooks, Built-in algorithms, Automatic model tuning, One-click deployment. DynamoDB covers Managed and serverless, Predictable latency, On-demand or provisioned capacity, Global secondary indexes.
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.
SourceDynamoDB: On-demand or provisioned capacity?
On-demand suits unpredictable or spiky traffic and removes capacity planning. Provisioned with autoscaling is considerably cheaper for steady high-volume workloads. Tables can be switched between them, though not arbitrarily often.
AWS 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.
SourceDynamoDB: Can I run DynamoDB outside AWS?
No. DynamoDB Local exists for development and testing only. For a production-compatible alternative elsewhere, ScyllaDB's Alternator implements the DynamoDB API, but it is a different system with a different ecosystem.
AWS 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.
SourceDynamoDB: Can I run ad hoc queries or analytics?
Not on the table itself. Scans are slow and expensive at scale. The usual pattern is to export to S3 or stream changes out and query them in Athena, Redshift or another analytical engine.
DynamoDB: Is single-table design necessary?
It is the pattern that gets the most from DynamoDB when access patterns are well known, because it lets related items be retrieved in one query. It also makes the model harder to evolve, so many teams reasonably choose multiple simpler tables and accept extra requests.
DynamoDB: What are the real limits I should design around?
400 KB per item, 1 MB per query or scan page, 100 items per transaction, roughly 3,000 read and 1,000 write units per partition, and eventual consistency on global secondary indexes.
Related pages
More on AWS SageMaker
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