Sold by: Flywheel
Medical Imaging Data and AI Development Platform
4
Overview
Flywheel Enterprise streamlines the identification, curation and training of medical imaging data to develop cutting-edge imaging algorithms that accelerate biomarker discovery and deliver advances in patient outcomes. Our secure platform gives you the tools to discover, manage, curate, and compute large amounts of data to enable clinical analytics, cohort discovery, and model training at scale. With Flywheel, you can ingest medical imaging data and curate it to common standards, automate processing and machine learning pipelines, and collaborate securely all while maintaining data privacy and regulatory compliance. With unique strength in medical imaging and data aggregation, Flywheel offers a comprehensive AI development platform that is unmatched in streamlining data ingestion and curation, enabling machine learning and accelerating secure collaboration.
Highlights
- Do Research, Not IT
Details
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Delivery method
Deployed on AWS
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Pricing
Pricing is based on the duration and terms of your contract with the vendor. This entitles you to a specified quantity of use for the contract duration. If you choose not to renew or replace your contract before it ends, access to these entitlements will expire.
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Dimension | Description | Cost/12 months |
|---|---|---|
Sessions | Total number of managed Sessions | $0.001 |
Dimensions summary
Flywheel Enterprise uses a contract pricing model with one dimension: managed Sessions. You commit to a set number of Sessions for your term. A Session represents a unit of managed imaging data activity on the platform. Your cost scales with the total number of Sessions you need. There are no separate tiers or instance sizes to choose from. To increase capacity, you raise the Session count in your contract. This single-dimension structure lets you size your commitment to your organization's imaging research and AI workload.
Top-of-mind questions for buyers
A Session is a unit of managed imaging data activity within the platform. It represents the platform's measure for handling your imaging and related data. Your contract counts the total number of Sessions you commit to managing. Confirm the exact Session definition with the vendor for your specific data workload.
Cost scales with the total number of Sessions in your contract. To manage more imaging data, you raise the committed Session count. There are no separate tiers or instance sizes. The platform is built to scale for growing data needs, so you size your commitment to your workload.
The marketplace pricing has one dimension: Sessions. The platform uses elastic compute scaling and runs on cloud infrastructure. Underlying cloud resource costs may apply separately from the Session commitment. Confirm any infrastructure or compute fees with the vendor for your deployment.
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Resources
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Support
Vendor support
For product support please email us at support@flywheel.io , or you can contact us from our webpage at
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Updated weekly
By Flywheel
By Cirro Bio
By Tag.bio
Top
50
In Data Preparation
Top
25
In Healthcare & Life Sciences
Top
100
In Education & Research
AI generated from product descriptions
Medical Imaging Data Ingestion
Capability to ingest medical imaging data from multiple sources into a centralized platform
Data Curation and Standardization
Automated curation of medical imaging data to common standards for consistency and interoperability
Machine Learning Pipeline Automation
Automated processing and machine learning pipeline execution for algorithm development and model training
Data Privacy and Regulatory Compliance
Built-in security controls and compliance mechanisms to maintain data privacy and meet regulatory requirements
Collaborative Analytics Environment
Secure collaboration tools enabling multiple users to perform clinical analytics, cohort discovery, and model training at scale
Scalable Bioinformatics Pipeline Execution
Provides optimized computational pipelines for data-intensive analysis with cloud-scale processing capabilities without requiring advanced cloud engineering expertise.
Interactive Data Visualization and Exploration
Enables interactive exploration and visualization of biomedical datasets with collaborative capabilities for real-time data analysis among multiple users.
Data Management and Sharing Infrastructure
Supports ingestion, processing, and sharing of biomedical research data with automated storage cost forecasting and data archival capabilities.
HIPAA-Eligible Cloud Infrastructure
Provisions HIPAA-compliant cloud infrastructure on demand with delegated account administration and compute cost controls.
Reproducible Analysis Workflows
Delivers reproducible bioinformatics analysis through pre-configured pipelines and shared workspace collaboration for standardized research workflows.
Machine Learning Interface
End-to-end machine learning interface with developer studio for model development and deployment
Data Integration and Processing
ETL pipelines specifically designed for healthcare and life sciences datasets with data product and service layers
Generative AI Capabilities
AI and Generative AI applications and solutions integrated into the platform for data analysis and insights
Authentication and Authorization
AuthN/AuthZ and SSO capabilities for secure access control and identity management
CI/CD Pipeline Integration
Continuous integration and continuous deployment pipelines for automated workflow management and code deployment
Standard contract
No
Customer reviews
reviewer2902968
Platform has standardized diverse medical imaging data and supports efficient AI development
Reviewed on Sep 26, 2026
Review from a verified AWS customer
What is our primary use case?
I have been actively using Flywheel.io for the last two years.
My main use case for Flywheel.io ranges from data ingestion to algorithm development, and primarily for the last year, we have been using it for standardizing our data and improving our ingestion pipelines.
A specific example of how I have used Flywheel.io for improving our ingestion pipelines is that we have used multiple gears, which allow us to standardize our data during ingestion. These gears range from DICOM-based ingestion to NIfTI-based ingestion, and we have some QC gears and some metadata extraction gears that we run in a chain to improve our data ingestion.
Apart from this, we also use Flywheel.io for some of our algorithm development. For example, we use it for removing text from images or for concatenating images, and there are multiple use cases in our company that we run on Flywheel.io.
What is most valuable?
Based on our experience, the best features Flywheel.io offers are its great flexibility to run the gears and interact with Flywheel.io. For example, it offers an SDK and the CLI, which makes it a very complete product.
The SDK and CLI have been helpful for us because we primarily use the SDK, and we use the CLI for local testing of our gears. While we are developing them, it really helps us to debug issues and speed up the development process. The SDK is always useful when we want to interact with or search data in our projects that contain a very large amount of data. The SDK really helps us to understand our data or to identify any problems with the data.
Flywheel.io has positively impacted our organization because it helps us to standardize our data coming from different CROs, which is organized in different ways. The most important benefit we receive from Flywheel.io is that it ensures our data is ready for algorithm analysis once it goes through the checks in Flywheel.io.
While it is hard to quantify in a measurable sense, it saves the time that would be required if the data were not standardized. Without standardization, developing our AI-based models would require approximately 70 to 80 percent of the time needed just to clean up the data, and that time is saved when the data is in proper formats.
What needs improvement?
One way Flywheel.io needs to improve is by having more availability of computational power required in this time of AI. It needs to understand those requirements dynamically and should use GPU-intensive models wherever they are available to speed up the process. Secondly, when we run multiple jobs, it sometimes becomes a difficult and slow process, especially for larger algorithms.
The user interface on Flywheel.io's side is quite good, and I would not add anything there.
Regarding Flywheel.io's AI capabilities, I think the governance part is still laggy from our experience and needs improvement. The security part is somewhat good.
What do I think about the stability of the solution?
Flywheel.io is quite stable.
What do I think about the scalability of the solution?
Flywheel.io scales well. The only issue I have experienced is that when there are a lot of jobs, it sometimes feels laggy because running many jobs can block the system overall for other users, which affects scalability.
How are customer service and support?
We always receive good support, and the customer support is fantastic. I rate the customer support at 10.
What was our ROI?
While it is hard to quantify in a measurable sense, it saves the time that would be required if the data were not standardized. Without standardization, developing our AI-based models would require approximately 70 to 80 percent of the time needed just to clean up the data, and that time is saved when the data is in proper formats.
What other advice do I have?
I would certainly advise others to use Flywheel.io for medical imaging tasks.
Flywheel.io is deployed in our organization as a hybrid cloud system.
We use multiple cloud providers, including Amazon Web Services and Google Cloud Platform, which are all three main players. I rate this review 8 out of 10.
Which deployment model are you using for this solution?
Hybrid Cloud
If public cloud, private cloud, or hybrid cloud, which cloud provider do you use?
Amazon Web Services (AWS)