Third-party data now drives forecasting, analytics, and machine learning across modern cloud teams. But acquiring it has long meant custom contracts, delayed access, and limited visibility into how data costs scale inside analytics workflows.
AWS Data Exchange reduces much of that friction by integrating third-party data into the AWS ecosystem.
In this guide, you’ll learn how AWS Data Exchange fits into modern data architectures, how pricing works, and what cost challenges emerge as consumption grows.
What Is AWS Data Exchange?
AWS Data Exchange is an AWS service that enables teams to discover, subscribe to, and consume third-party data products directly inside AWS without manual transfers or custom integrations.
After subscribing, data becomes available through AWS-native delivery methods including S3 exports, Redshift data shares, and managed API endpoints.
How AWS Data Exchange works
After subscribing to a data product, AWS Data Exchange makes it available using AWS-native delivery methods chosen by the provider.
Credit: K21 Academy
File-based products are published as versioned revisions. Each revision represents a fixed snapshot of the dataset at a specific point in time.
Customers export selected revisions to Amazon S3, where the data can be queried by Amazon Athena, processed by AWS Glue, or consumed by custom analytics workloads.
Some data products do not require copying data. Instead, customers get in-place access to provider-managed storage or Amazon Redshift data shares.
This allows teams to query third-party data directly from Redshift while the provider maintains the source dataset.
For API-based products, it exposes managed API endpoints. Amazon API Gateway commonly backs these APIs and returns data on request, rather than as files or tables. This model is used for high-frequency or continuously updated data.
When providers publish updates, they release new revisions. Customers decide when to consume those revisions, keeping control over changes entering analytics or machine learning workflows.
When Should You Use AWS Data Exchange?
AWS Data Exchange is most useful when third-party data becomes a recurring input to analytics, models, or products running in AWS.
It is not built for one-off data downloads.
Recurring market, economic, or environmental data
Some organizations depend on data that changes daily or continuously.
Financial institutions rely on daily market prices, interest rates, and economic indicators to support forecasting and risk models. Logistics and retail teams depend on weather forecasts, geospatial data, and demographic information to optimize routes, inventory levels, and demand planning.
In this case, AWS Data Exchange provides a structured way to consume updated datasets within AWS without rebuilding ingestion pipelines each time data changes.
External data feeding production features or models
AWS Data Exchange is often used when third-party data directly influences application behavior.
Examples include pricing logic, risk scoring, personalization, fraud detection, or demand forecasting. In these scenarios, data has to be reliable, versioned, and auditable, because changes affect customer-facing outcomes.
Treating external data as a managed input inside AWS reduces the risk of silent changes or broken dependencies.
Data that has to live next to analytics and compute
AWS Data Exchange is applicable when third-party data has to be queried alongside internal datasets already stored in Amazon S3 or Amazon Redshift.
Most analytics workloads depend on joins between internal operational data and external reference data. Pulling that data from outside AWS introduces latency, duplication, and operational overhead. Keeping third-party data inside the same environment simplifies querying, transformation, and analysis.
This is especially relevant for organizations standardizing on AWS-native analytics tools.
Replacing brittle, custom ingestion pipelines
Teams often begin by pulling data from vendors using scripts, scheduled downloads, or custom APIs.
Over time, these pipelines break, drift, or require constant maintenance. AWS Data Exchange is adopted when those integrations become operationally expensive or unreliable.
Distributing proprietary data to AWS-based customers
AWS Data Exchange is also used by organizations that produce valuable datasets.
Research firms, SaaS platforms, and data vendors use it when their customers already operate in AWS and expect data to be delivered natively. Instead of building custom delivery systems, providers publish datasets through AWS and let customers consume them in their own environments.
Benefits Of Using AWS Data Exchange
AWS Data Exchange turns third-party data into a native AWS resource, eliminating custom ingestion pipelines and manual file transfers. Key benefits:
- Operates as a serverless service. AWS handles dataset access and entitlements between providers and consumers.
- Predictable data updates. Versioned revisions make data changes explicit and controlled.
- Lower operational overhead. No custom ingestion scripts, vendor pipelines, or manual file transfers.
- Consistent security model. Third-party data follows existing AWS access and permission controls.
- Faster time to insight. Data is immediately usable by AWS analytics and data services.
- Supports data providers. Organizations can distribute datasets to AWS customers without building delivery infrastructure.
What Are the 5 Types Of Data Available On AWS Data Exchange?
AWS Data Exchange organizes data by dataset type. Each type maps to a different delivery pattern in AWS.
1. Files data sets
File-based datasets (most common format): Providers publish data as files stored in Amazon S3. Customers export revisions to their own S3 buckets where the data integrates with existing data lakes and analytics workflows.
AWS Data Exchange functions as a data distribution layer, not a data lake or warehouse. It delivers third-party data into AWS where it feeds downstream analytics services.
This is the most common format. The provider publishes data as files. Those files live as objects in Amazon S3, where they are commonly used as part of S3 data lakes.
Note: AWS Data Exchange is not a data lake or a data warehouse. It is a data distribution layer that delivers third-party data into AWS, where it can feed both data lakes and data warehouses.
2. API data sets
API-based datasets: Providers publish real-time or frequently updated data via managed API endpoints backed by Amazon API Gateway. Customers subscribe and retrieve data on demand through API calls rather than file downloads. AWS Data Exchange provides OpenAPI specifications for programmatic access and integration with existing applications.
3. Amazon Redshift data sets
These datasets are delivered via AWS Data Exchange data shares to Amazon Redshift.
A datashare gives read-only access to objects that the provider shares. That can include tables, views, schemas, and functions. This can be queried directly by BI tools and analytics workloads running on Redshift.
4. Amazon S3 data access data sets
This model provides direct access to third-party files in the provider’s S3 bucket. AWS Data Exchange provisions an S3 access point to simplify the sharing setup.
You can analyze the data in place using services such as Amazon Athena, Amazon EMR, or the SageMaker AI Feature Store.
5. AWS Lake Formation data sets (Preview)
AWS Data Exchange also supports AWS Lake Formation data sets in preview. This format is permission-based. It is aimed at governed sharing in data lake environments.
Limitations Of AWS Data Exchange
AWS Data Exchange is limited to AWS. It is not designed for multi-cloud or non-AWS data delivery.
Other drawbacks include:
- Limited customization of data products. Data products follow standardized packaging and delivery models. Providers cannot fully customize licensing or delivery behavior.
- Dependence on third-party providers. Data quality, update cadence, and continuity depend on the provider. Customers have limited control over upstream changes.
- Service-specific constraints. Some dataset types, such as Redshift data shares, have region- and configuration-specific requirements that both the provider and the consumer must meet.
- Complex cost visibility. AWS Data Exchange itself has no base fee; costs are driven by subscriptions, API usage, and downstream AWS services. This brings us to the next question.
Is AWS Data Exchange Free, And How Much Does It Cost?
AWS Data Exchange does not charge a flat platform fee. Pricing depends on your role and how data is shared or stored.
Pricing for data providers (senders)
Data grant fees (hourly)
AWS charges $0.04167 per active data grant per hour in US East (Ohio), with regional pricing variations. Grants become billable only after the receiver accepts them. Charges stop when the grant expires or is revoked. Example: 10 active grants running continuously for one month cost approximately $300.
Storage fees for file-based datasets
If you upload files to AWS Data Exchange, AWS charges for storage.
- Storage is measured in byte-hours
- Billed monthly
- Pricing varies by Region
Rate (US East – Ohio): $0.023 per GB per month
Tiered fulfillment (Marketplace) fees
When AWS collects revenue for new subscriptions to your public data products, AWS Marketplace applies a tiered fulfillment fee.
Standard AWS Data Exchange public listing fee: 3% of revenue
If you already have customers, you can use Bring Your Own Subscription (BYOS) offers to fulfill existing subscriptions without extra AWS collection fees.
Pricing for data subscribers (buyers)
Dataset price
The data provider sets the dataset price.
- Can be subscription-based or usage-based
- Some datasets are free (for example, Open Data)
Charges appear on your AWS bill as AWS Marketplace charges.
AWS service usage costs
AWS Data Exchange does not include compute, storage, or analytics usage.
You still pay standard AWS rates for services you use, such as:
- Amazon S3 (if data is exported). See: A Complete Guide To Amazon S3 Pricing
- Amazon Athena or AWS Glue
- Amazon Redshift (for data shares)
- API Gateway or downstream services (for API-based products)
Data transfer costs
Data transfer fees may apply when data:
- Moves across Regions
- Is transferred out to the internet
In-place access models (like Redshift data sharing or provider-managed S3 access) help reduce transfer costs.
See: AWS Data Transfer Pricing Guide And How To Reduce Costs
What Next: Manage And Understand AWS Data Exchange Costs With CloudZero
As you’ve seen above, AWS Data Exchange pricing is fragmented by design. Data costs come from providers, AWS Marketplace, and any other AWS service you use.
This makes it difficult to understand what the data actually costs the business once it enters analytics, models, or products.
CloudZero can help:
CloudZero helps teams connect AWS Data Exchange spend to real usage and outcomes, instead of tracking it as a standalone line item.
With CloudZero, you can:
- See third-party data costs alongside S3, Redshift, and API usage in a unified view
- Attribute AWS Data Exchange spend to specific teams, products, features, or customer segments
- Detect cost anomalies from API-based datasets in real time, before they impact margins
- Track unit economics like cost per report generated, cost per ML model inference, or cost per customer served
Some of the world’s most cost-sensitive AWS customers trust CloudZero to make cloud cost intelligence actionable. PicPay saved $18.6M annually. Diaceutics cut AWS spend by 41%. Upstart uncovered more than $20M in cloud cost savings opportunities. Take a product tour or 
to see how these brands did it and how we can help you visualize and control complex cloud costs.
AWS Data Exchange FAQs
What is AWS Data Exchange used for?
AWS Data Exchange is used to discover, subscribe to, and consume third-party datasets directly inside AWS.
Is AWS Data Exchange free?
AWS Data Exchange is not free. Some datasets are free, but many are paid. Costs depend on provider pricing and how data is consumed using AWS services such as Amazon S3, Amazon Redshift, or APIs.
How is AWS Data Exchange different from AWS Marketplace?
AWS Data Exchange focuses specifically on data products and data delivery. AWS Marketplace is broader and also includes software, AMIs, SaaS, and professional services. AWS Data Exchange uses AWS Marketplace for billing, but is purpose-built for data distribution.
Who pays fees in AWS Data Exchange?
Data consumers pay for datasets and downstream AWS usage. Data providers pay tiered fulfillment fees, infrastructure costs, and, if using data grants, per-grant and storage fees. AWS Data Exchange itself does not charge a standalone service fee.
What is AWS DataSync?
AWS DataSync is a data transfer service used to move large volumes of data between on-premises storage, AWS services, and other cloud environments. It is built for data migration and synchronization.
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