I've listed some pros and cons from experience...
..., as opposed to marketing materials. If I were to guess, I'd say you have a much higher chance to experience all the drawbacks of SageMaker, than any one of the benefits.
Drawbacks
- Cloud vendor lock in: free improvements in the open source projects in the future and better prices in competitor vendors are difficult to get. Why don't AWS invest developers in JupyterLab, they have done limited work in open source. Find some great points here, where people have experienced companies using as few AWS services as possible with good effect.
- SageMaker instances are currently 40% more expensive than their EC2 equivalent.
- Slow startup, it will break your workflow if every time you start the machine, it takes ~5 minutes. SageMaker Studio apparently speeds this up, but not without other issues. This is completely unacceptable when you are trying to code or run applications.
- SageMaker Studio is the first thing they show you when you enter SageMaker console. It should really be the last thing you consider.
- SageMaker Studio is more limited than SageMaker notebook instances. For example, you cannot mount an EFS drive.I spoke to a AWS solutions architect, and he confirmed this was impossible (after looking for the answer all over the internet). It is also very new, so there is almost no support on it, even by AWS developers.
- Worsens the disorganised Notebooks problem. Notebooks in a file system can be much easier to organise than using JupyterLab. With SageMaker Studio, a new volume gets created and your notebooks lives in there. What happens when you have more than 1...
- Awful/ limited terminal experience, coupled with tedious configuration (via Lifecycle configuration scripts, which require the Notebook to be turned off just to edit these scripts). Additionally, you cannot set any lifecycle configurations for Studio Notebooks.
- SageMaker endpoints are limited compared to running your own server in an EC2 instance.
- It may seem like it allows you to skip certain challenges, but in fact it provides you with more obscure challenges that no one has solved. Good luck solving them. The rigidity of SageMaker and lack of documentation means lots of workarounds and pain. This is very expensive.
Benefits
These revolve around the SageMaker SDK (the Sagemaker console and SageMaker SDK) (please comment or edit if you found any more benefits)
- Built in algorithms (which you can easily just import in your machine learning framework of choice): I would say this is worse than using open source alternatives.
- Training many models easily during hyperparameter search YouTube video by AWS (a fast way to spend money)
- Easily create machine learning related AWS mechanical turk tasks. However, mturk is very limited within SageMaker, so youre better off going to mturk yourself.
My suggestion
If you're thinking about ML on the cloud, don't use SageMaker. Spin up a VM with a prebuilt image that has PyTorch/ TensorFlow and JupyterLab and get the work done.
Answer from Ben Butterworth on Stack OverflowAmazon Web Services
aws.amazon.com › machine learning › amazon sagemaker ai › pricing
SageMaker Pricing
1 week ago - Amazon SageMaker JupyterLab Launch fully managed JupyterLab in seconds. Use the latest web-based interactive development environment for notebooks, code, and data. You are charged for the instance type you choose, based on the duration of use.
Top answer 1 of 5
41
I've listed some pros and cons from experience...
..., as opposed to marketing materials. If I were to guess, I'd say you have a much higher chance to experience all the drawbacks of SageMaker, than any one of the benefits.
Drawbacks
- Cloud vendor lock in: free improvements in the open source projects in the future and better prices in competitor vendors are difficult to get. Why don't AWS invest developers in JupyterLab, they have done limited work in open source. Find some great points here, where people have experienced companies using as few AWS services as possible with good effect.
- SageMaker instances are currently 40% more expensive than their EC2 equivalent.
- Slow startup, it will break your workflow if every time you start the machine, it takes ~5 minutes. SageMaker Studio apparently speeds this up, but not without other issues. This is completely unacceptable when you are trying to code or run applications.
- SageMaker Studio is the first thing they show you when you enter SageMaker console. It should really be the last thing you consider.
- SageMaker Studio is more limited than SageMaker notebook instances. For example, you cannot mount an EFS drive.I spoke to a AWS solutions architect, and he confirmed this was impossible (after looking for the answer all over the internet). It is also very new, so there is almost no support on it, even by AWS developers.
- Worsens the disorganised Notebooks problem. Notebooks in a file system can be much easier to organise than using JupyterLab. With SageMaker Studio, a new volume gets created and your notebooks lives in there. What happens when you have more than 1...
- Awful/ limited terminal experience, coupled with tedious configuration (via Lifecycle configuration scripts, which require the Notebook to be turned off just to edit these scripts). Additionally, you cannot set any lifecycle configurations for Studio Notebooks.
- SageMaker endpoints are limited compared to running your own server in an EC2 instance.
- It may seem like it allows you to skip certain challenges, but in fact it provides you with more obscure challenges that no one has solved. Good luck solving them. The rigidity of SageMaker and lack of documentation means lots of workarounds and pain. This is very expensive.
Benefits
These revolve around the SageMaker SDK (the Sagemaker console and SageMaker SDK) (please comment or edit if you found any more benefits)
- Built in algorithms (which you can easily just import in your machine learning framework of choice): I would say this is worse than using open source alternatives.
- Training many models easily during hyperparameter search YouTube video by AWS (a fast way to spend money)
- Easily create machine learning related AWS mechanical turk tasks. However, mturk is very limited within SageMaker, so youre better off going to mturk yourself.
My suggestion
If you're thinking about ML on the cloud, don't use SageMaker. Spin up a VM with a prebuilt image that has PyTorch/ TensorFlow and JupyterLab and get the work done.
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You are correct about EC2 being cheaper than Sagemaker. However you have to understand their differences.
- EC2 provides you computing power
- Sagemaker (try to) provides a fully configured environment and computing power with a seamless deployment model for you to start training your model on day one
If you look at Sagemaker's overview page, it comes with Jupyter notebooks, pre-installed machine learning algorithms, optimized performance, seamless rollout to production etc.
Note that this is the same as self-hosting a EC2 MYSQL server and utilizing AWS managed RDS MYSQL. Managed services always appears to be more expensive, but if you factor in the time you have to spent maintaing server, updating packages etc., the extra 30% cost may be worth it.
So in conclusion if you rather save some money and have the time to set up your own server or environment, go for EC2. If you do not want to be bothered with these work and want to start training as soon as possible, use Sagemaker.
Sagemaker pricing and spot instances
I ask this as it seems surprising there's ~40% markup in cost for using this managed service versus EC2, and despite what the AWS Report on TCO says, I can't quite see it saving me that amount of money versus us setting up a EKS/ECS solution for this problem. Yes but I am still using SageMaker async inference because it has benefits if you are mostly serverless. No need to deal with VPC, AMI, OS and so on AWS manages and builds a the async queue for you and distributes work You get autoscaling for free and can even scale to zero My BE is serverless (Lambda, S3, ...) and thanks to the SageMaker async inference it stays "serverless" because I don't have to deal with EC2/EKS/ECS + Autoscaling + SQS + VPC. More on reddit.com
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April 8, 2024SageMaker costs for AI model
To optimize costs, consider using the endpoint only when needed and shutting it down when not in use. You could also explore using smaller instance types or SageMaker's serverless inference if your workload allows it. More on repost.aws
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March 18, 2025How to do cost estimation for Amazon Sagemaker
If you're bringing your own model, you don't need to use SageMaker. I've got mixtral-8x7b.Q5_K_M running on a g5.2xlarge (24GB of VRAM, 32 GB RAM), and it's $1.212 per hour in us-east-1. Just be sure to pick the Ubuntu deep learning AMI, as it has the required GPU drivers. Be aware that you're paying for the server whenever it's running - not just when you're interacting with the model. If you forget to shut it off and leave it for a month, that's $860... More on reddit.com
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March 15, 2024Quick question about Sagemaker studio pricing. Is one charged for "ready" apps as running instances? Or are they booted up when you start using them?
This is one of the disadvantages of a higher-level managed solution. Each of those components seems to create a separate instance and are not truly “serverless”. I also run my ML pipelines and inference services on EKS and considered SageMaker, but meh. Can’t do everything I need easily and do not understand the entire cost structure. Some of the new features such as the Feature Store are attractive but seem very unpolished and lacking as of now. The fact that everything is integrated with Studio (i.e. Jupyter Lab) is just weird too. More on reddit.com
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November 18, 2020Videos
22:18
Choose the right instance for inference deployment with SageMaker ...
31:34
AWS re:Invent 2020: How to choose the right instance type for ML ...
09:39
Fully Managed Notebook Instances with Amazon SageMaker - YouTube
09:01
Amazon SageMaker overview | Amazon Web Services - YouTube
33:21
How to Set Up an AWS SageMaker Notebook Instance for Machine Learning ...
SageMaker Studio: ML Development Overview - AWS
Amazon Web Services
amazonaws.cn › en › sagemaker › pricing
SageMaker Pricing
1 week ago - She uploads a dataset of 100 GB ... The sub-total for 200 GB of general purpose SSD storage = ¥ 0.0242; The total price for this example would be ¥ 3.0022....
Amazon Web Services
aws.amazon.com › machine learning › amazon sagemaker › pricing
SageMaker pricing - AWS
1 week ago - Pricing is based on the number of nodes and instance hours used, with costs varying by node type. The model includes options for on-demand and reserved instances, with the latter providing cost savings for long-term commitments. For the most accurate and detailed pricing information, consult Amazon Redshift pricing. SageMaker Data Processing, which brings together capabilities from Amazon Athena, Amazon EMR, AWS Glue, and Amazon Managed Workflows for Apache Airflow (Amazon MWAA), offers a flexible, pay-as-you-go pricing model without upfront commitments or a minimum fee.
Reddit
reddit.com › r/aws › sagemaker pricing and spot instances
r/aws on Reddit: Sagemaker pricing and spot instances
April 8, 2024 -
I've been looking into whether it would be sensible to use AWS Sagemaker for my GPU inference workload on a T4 GPU. It can tolerate multiple minutes of downtime and delays, and has large binary multimedia files as input to the model. Autoscaling is a requirement based on load.
AWS Sagemaker Asynchronous endpoints look very attractive in that they manage the container orchestration, autoscaling, queueing of requests and provide various convenience utilities for maintaining, monitoring and upgrading models in production.
In us-east-1, I calculate the following:
| Service | Instance Type | Hourly Price | Monthly Price |
|---|---|---|---|
| EC2 On Demand | g4dn.xlarge | $0.526 | $378 |
| EC2 Annual Reservation | g4dn.xlarge | $0.309 (upfront) | $229 |
| EC2 Spot | g4dn.xlarge | ~$0.22 | ~$156 |
| Sagemaker On Demand | ml.g4dn.xlarge | $0.7364 | $530 |
| Sagemaker On Demand with Saving Plan | ml.g4dn.xlarge | $0.4984 | $358 |
From what I can see however, it would come with a significant cost (likely prohibitively high) to use Sagemaker versus using EKS/ECS to host the model and SQS to provide the queueing of requests. I appreciate that's the price one pays for a managed service, but I wanted to confirm a few things with the community to make sure I'm not missing anything in my cost estimations:
Is it correct that Sagemaker does not support spot instances for inference at all? (I appreciate they support it for training)
Is it correct that one can apply a savings plan to inference endpoints and that it would be Service classified as "Hosting" on this page https://aws.amazon.com/savingsplans/ml-pricing/ ? It's confusing as "Hosting Service" is not a term they use in the development docs to describe inference endpoints per say.
Is it correct one cannot reserve instances for a year for Sagemaker like with EC2 to cut costs, and thus the above Savings Plan is the cheapest you can get a T4 GPU.
I ask this as it seems surprising there's ~40% markup in cost for using this managed service versus EC2, and despite what the AWS Report on TCO says, I can't quite see it saving me that amount of money versus us setting up a EKS/ECS solution for this problem. I can see however that TCO report is also largely considering training infra, which indeed does likely bring a lot of value not relevant here.
Noise
noise.getoto.net
Noise | The collective thoughts of the interwebz
Price-performance – Necessary for sustainable, long-term scaling · The capabilities of Amazon SageMaker Unified Studio and Amazon SageMaker Catalog aligned with Bayer’s vision of decentralized mesh execution combined with centralized discovery and governance.
Medium
medium.com › @mahammadkhadir2 › lets-understand-how-amazon-pricing-works-sagemaker-best-practices-for-right-sizing-compute-a3aaef150531
Let’s Understand how Amazon SageMakerpricing works, SageMaker best practices for right-sizing compute resources for different stages of an ML project. | by Khadir Mahammad | Medium
January 25, 2024 - Amazon SageMaker Studio is a fully integrated ML development environment with a managed Jupyter Notebook app experience, now accessible for free, with payment only for used AWS services. Notebook Instances are fully managed compute instances running Jupyter Notebook, handling ML workflows. Prices for compute instances are the same for both Studio and on-demand instances.
Cloudforecast
cloudforecast.io › home › aws pricing & cost optimization › aws sagemaker pricing guide – cost breakdown & optimization tips
AWS SageMaker Pricing Guide - Cost Breakdown & Optimization Tips | CloudForecast
October 29, 2025 - For example, if you initially pick a ml.p3.2xlarge ($3.825/hour) for training, but see that GPU utilization is consistently below 30%, you might decide to switch to the ml.g5.2xlarge ($1.515/hour) instead.
AWS
calculator.aws
AWS Pricing Calculator
AWS Pricing Calculator lets you explore AWS services, and create an estimate for the cost of your use cases on AWS.
AWS re:Post
repost.aws › questions › QUTgC3561MQKaWwyazLh_YXQ › sagemaker-costs-for-ai-model
SageMaker costs for AI model | AWS re:Post
March 18, 2025 - Thank you for providing details about your SageMaker deployment. I'll explain the pricing structure and address your questions. For your deployment using an ml.g5.xlarge instance, you will be charged based on the time your endpoint is running, regardless of whether it's processing requests or idle.
Amazon Web Services
amazonaws.cn › home › products › amazon sagemaker
Amazon SageMaker
1 week ago - Amazon SageMaker makes it easy to deploy your trained model into production with a single click so that you can start generating predictions for real-time or batch data. You can one-click deploy your model onto auto-scaling Amazon ML instances across multiple availability zones for high redundancy. Just specify the type of instance, and the maximum and minimum number desired, and SageMaker takes care of the rest.
Saturn Cloud
saturncloud.io › sagemaker-pricing
Amazon SageMaker Pricing | Saturn Cloud
The details of Amazon SageMaker’s free tier pricing are in the table below. The Saturn Cloud price is the price per hour for the Saturn Cloud component, while the hosting price is the charge for the underlying AWS EC2 instances that the resources run on. Both of these will be charged directly through Saturn Cloud to the credit card attached to the hosted account. Available on the Saturn Cloud Hosted Free tier. To enable these instance types...
CloudOptimo
cloudoptimo.com › home › blog › mastering amazon sagemaker pricing
Mastering Amazon SageMaker Pricing
March 13, 2025 - Amazon Elastic Block Store (EBS) provides block storage for EC2 instances, including SageMaker notebook instances. EBS volumes offer high-performance storage that can be attached to your notebook instances, allowing for efficient data access and manipulation. Pricing for EBS volumes depends on the volume type (e.g., General Purpose SSD, Provisioned IOPS SSD, or Magnetic) and the storage capacity used. General Purpose SSD (gp3): Provides a balance of price and performance.
nOps
nops.io › blog › sagemaker-pricing-the-essential-guide
SageMaker Pricing: The Essential Guide | nOps
November 18, 2025 - Applies to multiple workloads: Savings Plans cover SageMaker Studio notebooks, training, real-time and batch inference, Data Wrangler, processing jobs, and more. Flexible instance usage: You can switch between instance types, sizes, and regions without losing your discounted rate.
AWS
docs.aws.amazon.com › amazon sagemaker › developer guide › machine learning environments offered by amazon sagemaker ai › amazon sagemaker studio › amazon sagemaker studio classic › use amazon sagemaker studio classic notebooks › available resources for amazon sagemaker studio classic notebooks › instance types available for use with amazon sagemaker studio classic notebooks
Instance Types Available for Use With Amazon SageMaker Studio Classic Notebooks - Amazon SageMaker AI
. For information about pricing for these instance types, see Amazon EC2 Pricing ... For information about available Amazon SageMaker Notebook Instance types, see CreateNotebookInstance.
Amazon Web Services
aws.amazon.com › machine learning › amazon sagemake ai › amazon sagemaker canvas pricing
No-code Machine Learning - Amazon SageMaker Canvas Pricing - AWS
1 week ago - The pricing for real-time inference is based on the Amazon SageMaker Pricing for Hosting: Real-Time Inference , which depends on the instance type and duration of usage. Batch Inference: For batch predictions, the charges depend on the type of model and the size of the dataset.
Cloudexmachina
cloudexmachina.io › blog › sagemaker-pricing
AWS SageMaker Pricing: The Developer’s Guide to Smart ML Spending
September 16, 2025 - SageMaker charges for various compute activities beyond training and inference. Notebook instances, often used for model experimentation and data exploration, incur hourly charges based on the underlying instance type.
Holori
holori.com › accueil › blog › ultimate aws sagemaker pricing guide
Holori - Ultimate AWS Sagemaker pricing guide
October 23, 2024 - SageMaker Studio Notebooks: Pre-built, collaborative Jupyter notebooks that can be customized without server management. Costs are based on the compute instance type selected and the number of hours it’s running, plus any storage volumes attached to the notebooks.
Concurrencylabs
concurrencylabs.com › blog › sagemaker-ai-cost-savings
How To Keep SageMaker AI Cost Under Control and Avoid Bad Billing Surprises when doing Machine Learning in AWS - Concurrency Labs
December 4, 2024 - SageMaker does not support the equivalent of EC2 Reserved Instances, only Savings Plans. If you want to know more details about how to save money with Savings Plans in general, here is an article I wrote about this topic. Choose the right Instance Family for the type of processing requirements, given there can be substantial price differences for instances of similar size, but different families (in some cases, close to 10x).
Scribd
scribd.com › document › 841284723 › Amazon-SageMaker-Pricing-Amazon-Web-Services-AWS
Amazon SageMaker Pricing - Amazon Web Services (AWS)
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