Monetizing Proprietary Data Through APIs: How to Unlock New Revenue in the AI World
A report by Bloomberg Intelligence projects the AI industry will reach $1.3 trillion by 2032, with proprietary data fueling much of this growth. As businesses increasingly adopt generative AI (genAI) to enhance efficiency, data is rapidly becoming one of the most valuable assets in the digital economy.
Foundational AI models require vast amounts of data for training, and many AI products are now leveraging proprietary datasets alongside these models to power innovative applications and AI agents. These tools have the potential to transform business processes across engineering, sales, support, and beyond.
Chances are, your organization already holds a wealth of proprietary data. Whether it’s internal data supporting a traditional SaaS application or user-generated content, this data isn’t just a strategic asset for internal use — it can also be monetized by selling it to enterprises that need high-quality datasets for training their models or driving their applications.
Monetizing data can bring numerous benefits to an organization. By leveraging their existing data, companies can unlock new revenue streams and gain a competitive edge in the market. But how do you unlock this value while navigating the challenges of monetizing your proprietary data?
Understanding Data Monetization
As a data provider, balancing customer needs with revenue growth is no small feat, and without a well-thought-out data monetization strategy, you risk losing potential revenue, stifling your growth, and limiting your ability to scale. Imagine a worst-case scenario: A customer signs up, downloads all the data they need in a single day, and never returns. Effective data monetization strategy can mitigate these risks by aligning pricing models with customer usage patterns and ensuring sustainable revenue growth.
Unlike API businesses, where API consumption is typically predictable “up and to the right,” data consumption often follows a sporadic pattern. Customers typically consume data only when needed. For example, if you’re providing data to assist marketing teams, they might only need the data ahead of large marketing launches. Similarly, if you are providing financial data around real estate transactions, you may find customers only care about the data during end-of-year planning or ahead of the spring buying season.
Simply charging a flat monthly or yearly fee might not align with the value customers receive, especially when their consumption is irregular. This raises an important question: How can you ensure predictable revenue and cash flow for your business while reducing obstacles for customers whose usage fluctuates and is unpredictable? A well-thought-out API business model is crucial for balancing customer needs with revenue growth.
What is Data Monetization?
Data monetization is the process of generating revenue from data, transforming it from a mere asset into a significant income source. This can be achieved through various methods, including data-as-a-service (DaaS), data licensing, and data analytics. With the exponential growth of data generated daily, companies are increasingly seeking ways to unlock its value and turn it into a revenue stream.
One of the most effective channels for data monetization is through application programming interfaces (APIs). APIs allow companies to offer their data to third parties in a structured and accessible manner. Additionally, data marketplaces and data analytics platforms provide avenues for companies to sell or license their data, further expanding their monetization opportunities.
Benefits of Data Monetization
Data monetization offers several compelling benefits to companies:
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New Revenue Streams: By monetizing data, companies can create new revenue streams, diversifying their income and reducing reliance on traditional sources.
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Improved Decision-Making: Access to valuable insights and analytics derived from monetized data can enhance decision-making processes, driving business growth.
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Competitive Advantage: Companies that effectively monetize their data can gain a competitive edge over rivals, increasing their market share and driving business success.
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Increased Efficiency: Monetizing data can streamline operations and reduce costs, leading to higher efficiency and improved profitability.
Different Common API Monetization Models
One effective approach to monetizing data is usage-based billing (also referred to as consumption-based billing). This model allows customers to pay only for what they use, offering flexibility and avoiding the commitment of a subscription. Moreover, it enables your revenue to scale naturally as customers’ data needs grow.
Cloud providers and API platforms have widely adopted usage-based billing. It’s in practice in both modern SaaS companies like NexHealth and traditional enterprises like Siemens. A typical implementation involves tracking API usage over a billing period (such as a month) and invoicing customers at the end of that period. This model works well if the cost of providing the API is low and the risk of abuse is minimal.
However, data providers often face higher costs of goods sold (COGS) or risks of misuse. For example, a customer might download all the data they need and then cancel their subscription or simply fail to pay their invoice. To mitigate these risks, many providers are adopting a prepaid pay-as-you-go (PAYG) model.
Modern AI companies like You.com and OpenAI, along with telcos such as Sinch and Twilio, are leveraging PAYG to help align usage-based revenue to their usage-based cost. With prepaid PAYG, customers purchase credits upfront, which are then consumed based on a pre-negotiated rate — similar to buying a prepaid phone card. This model reduces the risk of abuse and provides immediate cash flow for your business, making it a win-win for providers and customers.
Another popular strategy is the freemium model, which is effective in lowering entry barriers for developers by providing basic access for free while charging for advanced features. A basic API plays a crucial role in this model by offering developers free access to fundamental functionalities, encouraging experimentation and adoption. This approach is widely used among popular platforms like Spotify and GitHub, driving rapid user base growth but requiring careful management of conversion rates from free to paid tiers.
Overview of API Monetization Models
API monetization models are strategies used by API providers to generate revenue from their APIs. Here are some common API monetization models:
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Pay-per-Use: This model charges developers for each API call they make, aligning costs with usage.
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Subscription-Based: Developers pay a recurring fee for access to the API, providing predictable revenue for the API provider.
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Freemium: A basic version of the API is offered for free, with charges applied for premium features or higher usage limits.
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Revenue Sharing: This model involves sharing revenue with developers who use the API to generate income, fostering a collaborative ecosystem.
These models allow API providers to choose the best fit for their business needs and customer usage patterns.
Developing an API Monetization Strategy
Developing an effective API monetization strategy requires careful consideration of several factors. Here are the steps to follow:
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Identify the Target Market: Understand the needs and requirements of the developers who will be using the API. This involves market research and customer segmentation.
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Define the Value Proposition: Clearly articulate the benefits and features of the API and how they will be delivered to the target market. This helps in positioning the API effectively.
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Determine the Pricing Strategy: Decide on the pricing model, pricing tiers, and revenue sharing model. This involves balancing affordability for customers with profitability for the provider.
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Develop a Revenue Sharing Model: Establish the revenue sharing percentage and payment terms. This fosters a collaborative relationship with developers and incentivizes usage.
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Implement the API Monetization Strategy: Set up the API infrastructure, develop comprehensive API documentation, and launch the API to the target market. Continuous monitoring and optimization are crucial for long-term success.
By following these steps, API providers can develop a robust API monetization strategy that drives revenue and business growth.
How to Meter API Usage and Data Consumption
Even with a PAYG model, determining how to charge customers requires careful consideration. Metering by API calls alone is often ineffective, as customers prioritize efficient batch queries to maximize throughput. A single API call could result in the export of massive datasets. Accurate metering can also highlight the api provider benefits, as it aligns provider earnings with the value delivered to customers, encouraging collaboration and maximizing both usage and revenue.
For example, if you are offering a financial data enrichment API, your customers may want to enrich thousands or millions of records in a large batch. In this case, the ideal flow would be that the customer submits a batch job for all the entities needing enrichment. Since this is a batch job, some items may not be found or fail. Customers shouldn’t be charged for missing or low-quality data.
To address this, it’s important to align your billable consumption metrics with customer value. For example, you could charge per successful data element or row accessed, excluding rows that are incomplete or of poor quality. Tracking and metering such granular data usage can be complex and typically requires additional monitoring tools to analyze API consumption effectively.
Managing Asynchronous Jobs
For APIs that involve backend jobs (like exporting large datasets), asynchronous processing adds another layer of complexity to monetization. You must decide when to deduct credits from a customer’s balance and how to handle failure scenarios. Artificial intelligence can enhance the efficiency and accuracy of managing asynchronous jobs, optimizing the overall API performance.
A common approach is to “lock” the credits until the job completes, ensuring customers cannot trigger excessive jobs that would cause their balance to drop into negative territory.
Job Handling Scenarios
| Job Status | Action |
| Job completes successfully | No change to customer’s balance |
| Job completes partially | Missing items are refunded back to the customer’s balance. Artificial intelligence can be used to predict and manage job outcomes, ensuring better handling of partial and failed jobs. |
| Job fails entirely | 100% of the credits are refunded back to the customer’s balance |
Example: Partial Completion
- Customer makes an API call to fetch 1,000 items.
- Customer balance reduced by $1000.
- Backend job triggered to export 1,000 items.
- Job completes, but 100 items are missing.
- Customer credited back $100 for the missing items.
By leveraging artificial intelligence, businesses can predict and manage partial completions more effectively, ensuring accurate credit adjustments.
Example: Failure Case
- Customer makes an API call to fetch 1,000 items.
- Customer balance reduced by $1000.
- Backend job triggered to export 1,000 items.
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Job fails.
- Customer notified and credited back the entire $1,000 due to job failure. Utilizing artificial intelligence can help in predicting and managing such job failures, ensuring accurate and timely credit refunds.
Measuring Data Quality and API Experience
Providing high-quality data through a seamless API experience is essential for retaining customers. Leveraging data analytics and artificial intelligence can help you measure and improve this quality, ensuring a seamless API experience.
Unlike traditional APIs, where success can be measured by [HTTP response codes](https://nordicapis.com/what-do-the-http-status-codes-mean/) (like 200 vs. 400), evaluating data quality is far more nuanced. A common approach is to apply a different number of credits depending on the quality.
| Price | Description |
| $0.05 per exact match | Data item had an exact match to the query |
| $0.02 per fuzzy match | Data item was a “best guess” but may not be correct |
| No cost when not found | Could not find the item |
A recommended approach is to assign a Response Quality Score using the formula:
Response Quality Score = sum( accuracy of row * relevancy of row ) / total rows
This scoring system helps developers understand where their API descriptions may fall short and how they can improve alignment.
Security and Regulatory Considerations
Selling proprietary data via APIs comes with regulatory and security responsibilities, especially when offering data as a service (DaaS) through the cloud. Before diving in, ensure you have the legal right to sell the data. Some datasets may be governed by copyright laws or regulations like HIPAA, PCI, or GDPR. For example, GDPR’s “right to be forgotten” requires mechanisms to delete specific data upon request. Artificial intelligence can help in ensuring compliance with security and regulatory standards, protecting sensitive information.
Additionally, it’s critical to implement robust security measures to protect sensitive information and build customer trust. This includes encrypting data with either server-side encryption or client-side encryption, securing API endpoints, and maintaining compliance with relevant data privacy standards.
The Future of Data Monetization in the Artificial Intelligence World
In an era where generative AI drives innovation across industries, proprietary data has become an increasingly valuable resource. In fact, data marketplaces are becoming crucial platforms for buying and selling data. By monetizing your data, you can transform it from a cost center into a profit center, unlocking new revenue streams while fueling advancements in AI. API services play a significant role in the future of data monetization, transforming from mere technological assets into valuable products integrated into various applications. Additionally, cloud service providers leverage APIs for automatic scaling of computational resources, allowing for flexible revenue models and precise billing based on actual customer usage. APIs are also crucial for developers building mobile apps, emphasizing their importance in driving revenue through mobile applications.
With the right monetization model and a focus on delivering high-quality, valuable data, you can position your organization at the forefront of this rapidly evolving landscape. The concept of revenue share is essential in future data monetization models, as it creates mutually beneficial relationships by dividing revenue from transactions conducted through the API between the API provider and the developer.
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