Why is API rate limiting important?

API rate limiting is important for protecting resources, ensuring fair usage, and maintaining service quality. It prevents abuse, such as denial-of-service (DoS) attacks, and manages traffic effectively to avoid overwhelming the API, which could lead to service degradation or outages.

What are the common methods of implementing API rate limiting?

Common methods for implementing API rate limiting include the fixed window approach, which limits the number of requests within a set period; the sliding window method, which tracks requests over a rolling time frame for more even distribution; the leaky bucket algorithm, which processes requests at a steady rate and discards excess; and the token bucket approach, which allows for bursts of traffic by using tokens that are consumed with each request.

How do you choose the right rate limit for an API?

Choosing the right rate limit involves analyzing historical traffic data to understand typical usage patterns, considering peak usage times, and evaluating the API's capacity. It is often advisable to start with conservative limits and adjust them based on real-time monitoring and user feedback to find the most effective balance.

How can API rate limiting be bypassed, and how do you prevent it?

API rate limiting can be bypassed through tactics such as using multiple API keys, rotating IP addresses with proxies or VPNs, or exploiting inconsistent rate limits across different endpoints. To prevent these tactics, you can implement key-level rate limiting that tracks usage across all keys for a user, monitor IP patterns to detect unusual activity, and apply consistent rate limits across all endpoints to close any loopholes.

What is the difference between API rate limiting and throttling?

The key difference between API rate limiting and throttling lies in how they manage traffic. Rate limiting sets a hard cap on the number of requests allowed within a certain time frame, blocking any excess requests once the limit is reached. Throttling, however, slows down the processing of requests after a certain threshold is reached, allowing requests to continue but at a reduced rate.

How can I handle rate limit errors gracefully?

Handling rate limit errors effectively involves returning clear and informative error messages, such as an HTTP 429 status code, along with a Retry-After header that indicates when the user can try again. Giving users tips on how to avoid hitting rate limits in the future, such as by optimizing their API calls or upgrading to a higher service tier, can also improve their experience.

What are the best practices for implementing API rate limiting?

Best practices for implementing API rate limiting include regularly assessing the frequency of API calls to ensure that rate limits align with actual usage patterns, using dynamic rate limits that can adapt to real-time traffic conditions, monitoring user activity to detect and address potential abuse, and combining rate limiting with other traffic management techniques like caching and load balancing to optimize performance.

How can I scale rate limiting to handle large volumes of traffic?

Scaling rate limiting to handle large volumes of traffic can be achieved by using distributed rate limiting, which spreads the load across multiple servers to prevent bottlenecks. Integrating rate limiting with content delivery networks (CDNs) and load balancers can further enhance the system's ability to manage high traffic volumes efficiently while maintaining performance and availability.

Can API rate limiting improve cost-efficiency?

Yes, API rate limiting can improve cost efficiency by reducing the strain on resources and avoiding the need for additional infrastructure to handle excessive traffic. By controlling the number of requests, rate limiting helps prevent the overuse of resources, which can lead to significant cost savings, especially as the API scales.

Mastering API Rate Limiting: Strategies, Challenges, and Best Practices for a Scalable API

What is API Rate Limiting?

API rate limiting is a technique for controlling how many requests a user or an application can send to an API in a timeframe. It is like setting a speed limit on a road. Just as speed limits ensure traffic moves smoothly without accidents, rate limits ensure that APIs handle traffic efficiently without getting overwhelmed.

Rate limiting is important for maintaining the performance and reliability of APIs, especially those that are publicly accessible or serve a large number of users. By limiting the number of requests, developers can prevent any one user or service from consuming too many resources, which could degrade the experience for others or even cause the API to become unavailable. This helps keep the system running smoothly, ensuring everyone gets a fair share of the API’s capabilities.

The Importance of API Rate Limiting

Let me tell you why API rate limiting is such a big deal:

Protecting Resource Usage: Let’s face it, resources are finite, and we have to be smart about how we use them. API rate limiting helps protect the server from being overwhelmed by too many requests at once. By capping the number of requests, it ensures that all users get access to the API without any hiccups.
Controlling Data Flow: In a high-traffic environment, data can become a bottleneck. Rate limiting helps manage this flow, ensuring that data is delivered consistently and reliably. This is key to maintaining a smooth user experience, especially when the API is under heavy load.
Maximizing Cost-Efficiency: Running an API, especially at scale, can be expensive. Rate limiting helps you manage those costs by reducing unnecessary requests. This way, you’re not paying for excess server capacity that you don’t need. It’s all about making sure that the API runs efficiently without draining your budget.
Managing Different User Access Levels: Not all users are the same, and rate limiting can help you manage that. For example, you might offer higher limits for premium users compared to those on a free plan. This ensures that your top-tier customers get the service they’re paying for while still allowing access to other users. It’s a balancing act that rate limiting handles quite well.
Preventing DoS and DDoS Attacks: One of the main reasons for rate limiting is to protect against Denial of Service (DoS) and Distributed Denial of Service (DDoS) attacks. These attacks aim to overwhelm your API with traffic, but with rate limits in place, you can significantly reduce their impact. It’s like having a safety valve that releases pressure, keeping the system from crashing under the load.
Improving User Experience: When users interact with your API, they expect it to be fast and reliable. Rate limiting ensures that the API remains responsive, even when demand is high. This consistency is key to a good user experience, making sure that everyone gets what they need without delay.
Reducing Costs: Let’s be honest; no one likes unexpected costs. Rate limiting helps keep your operational costs in check by preventing overuse of the API. By capping the number of requests, you avoid the need to scale up your infrastructure unnecessarily, which can save a lot of money in the long run.

How API Rate Limiting Works

API rate limiting can be implemented using several techniques and algorithms, each tailored to handle different traffic patterns and system requirements. Here’s a breakdown of the most common methods:

Fixed Window: This straightforward technique limits the number of requests allowed within a fixed time period (e.g., 100 requests per minute). It’s easy to implement but can lead to issues like traffic spikes at the start of each new window because users may learn to time their requests to maximize usage.
Sliding Window: Unlike the fixed window, the sliding window technique continuously tracks requests over a rolling time frame, such as the last 60 seconds. This method helps smooth out traffic, making it a better option for handling bursty traffic where requests might cluster at certain times.
Leaky Bucket: This algorithm processes requests at a consistent, controlled rate. Think of it like water dripping steadily out of a bucket. If requests come in faster than they can be processed, they’re either queued or dropped, helping to prevent sudden traffic surges from overwhelming the system.
Token Bucket: Similar to the leaky bucket but with more flexibility, the token bucket algorithm allows bursts of requests as long as there are tokens available. Tokens are added to a bucket at a steady rate, and each request consumes a token. If the bucket is empty, requests are delayed or denied until tokens are replenished. This method is ideal for scenarios where short bursts of high traffic are acceptable.

These techniques are supported by specific algorithms designed to enforce the rate limits effectively:

Fixed Window Counter: Tracks the number of requests within a fixed time window. It’s simple but can result in uneven traffic distribution.
Sliding Log: Logs each request with a timestamp and checks it against the current time to enforce limits, offering more precise control but at the cost of higher resource usage.
Sliding Window Counter: Combines the fixed window and sliding log approaches, applying limits more evenly by continuously adjusting the window as new requests are made.

Methods and Types of API Rate Limiting

API rate limiting can be adjusted to fit different use cases depending on the needs of the service and the type of traffic it handles. Here’s an overview of the various methods and types of rate limiting commonly used:

Key-Level Rate Limiting

Key-level rate limiting focuses on controlling the number of API requests that can be made with a specific API key. This method is particularly useful when you want to manage usage based on individual users or applications. By applying limits at the key level, you can ensure that no single user or application consumes too many resources, which helps maintain the API’s overall performance and availability for everyone. This approach is often used in tiered service models where different users have different levels of access.

API-Level Rate Limiting

API-level rate limiting applies limits across all users accessing a particular API. This method is useful for protecting the API as a whole, especially when there is a risk of overwhelming the service with too many requests at once. By setting a global limit, you can prevent traffic increases that could degrade the performance of the API for all users. API-level rate limiting is often implemented in scenarios where the API serves a large number of users and the risk of excessive simultaneous requests is high.

Resource-Based Rate Limiting

Resource-based rate limiting targets specific resources within an API, such as particular endpoints or data services. This method is especially useful when certain parts of the API are more resource-intensive or have higher demand than others. By limiting the number of requests to these specific resources, you can ensure that they remain available and responsive even under heavy load. This approach helps prevent any single resource from becoming a bottleneck that could slow down the entire API.

IP Throttling

IP throttling limits the number of requests based on the user’s IP address. This method is very effective in scenarios where you need to reduce the risk of abuse from specific sources, such as automated bots or potential malicious actors. By tracking requests by IP address, you can prevent a single source from overwhelming the API, which is especially useful for defending against DoS attacks. IP throttling is often used in conjunction with other rate-limiting methods to provide a layered defence strategy.

Rate Limiting vs. API Throttling

Key Differences

While rate limiting and throttling are often mentioned together, they are not the same thing, though they do serve similar purposes in managing API traffic.

Rate Limiting: This involves setting a cap on the number of requests that can be made to an API within a specified time frame. If the limit is exceeded, further requests are blocked until the limit resets. This approach is useful for ensuring that the API remains available and responsive by preventing overuse.
API Throttling: Throttling, on the other hand, doesn’t necessarily block requests completely but rather slows down the rate at which requests are processed. Instead of being denied, a user’s following requests are queued or delayed when they exceed the permitted request rate. This can help manage traffic more gracefully, allowing legitimate users to continue accessing the API, albeit at a reduced speed.

Use Cases for Each

Rate Limiting: Best used when you need to strictly enforce limits to prevent any single user from overloading the system. This is particularly important in scenarios where resource availability is critical and where allowing excessive requests could degrade service for everyone.
Throttling: Ideal for situations where you want to manage traffic without outright blocking users. Throttling is useful for maintaining service availability during traffic spikes, as it ensures that requests are processed more slowly rather than denied, which can help maintain a better user experience during peak times.

Implementing API Rate Limiting

How to Implement Rate Limiting in API Environments

Implementing rate limiting in an API environment involves choosing the right strategy and configuring it to meet the needs of your application. Here’s a step-by-step guide on how to do it:

Choose the Right Algorithm: Depending on your API’s traffic patterns and usage needs, select the appropriate rate-limiting algorithm (Fixed Window, Sliding Window, Leaky Bucket, or Token Bucket). Each has its own advantages, so it’s important to understand your specific requirements before deciding.
Set Limits Based on API Usage: Determine the appropriate limits based on historical data and expected traffic. Consider factors like peak usage times, the average number of requests per user, and how often you expect bursts of traffic. This data will help you set realistic and effective limits.
Configure the API Gateway or Middleware: Most modern API gateways, such as Kong, NGINX, or AWS API Gateway, support rate limiting out of the box. Configure your chosen gateway to enforce the limits you’ve set. If your API doesn’t use a gateway, you may need to implement rate-limiting logic directly within your application code.
Implement Dynamic Rate Limits: Consider implementing dynamic rate limits that can adjust based on real-time conditions, such as server load or the number of active users. This approach can help your API adapt to changing conditions and maintain performance even under heavy load.
Monitor and Adjust: After implementing rate limiting, continuously monitor the API’s performance to ensure that the limits are effective and that they don’t negatively impact legitimate users. Use metrics and logs to track how often rate limits are being hit and adjust as necessary to balance performance and user experience.

How to Test API Rate Limiting

Testing is a crucial part of implementing rate limiting. Here’s how to do it effectively:

Simulate High Traffic: Use tools like Apache JMeter or Postman to simulate high-traffic scenarios and observe how your API responds to reaching the rate limit. This will help you identify any issues with your configuration and ensure that the API behaves as expected under load.
Check for Edge Cases: Test how your API handles edge cases, such as just before and just after the rate limit is hit. This will help ensure that users experience a smooth transition and that legitimate requests aren’t mistakenly blocked.
Monitor API Responses: Pay attention to the HTTP status codes your API returns. When the rate limit is exceeded, the API should return a 429 status code (“Too Many Requests”) along with a message indicating the limit has been reached. Ensure that this behavior is consistent across different endpoints and user types.
User Feedback: Implement user-friendly error messages and headers that inform users when they’ve hit the rate limit and when they can retry. This improves the user experience and clarifies why requests are denied.

Handling Rate Limit Errors (e.g., “API Rate Limit Exceeded”)

When users exceed the API’s rate limits, it’s important to handle the situation in a way that maintains a good user experience:

Return Meaningful Errors: Ensure that your API returns clear and informative error messages when rate limits are exceeded. The 429 status code should be accompanied by a message explaining that the rate limit has been reached and when the user can try again.
Provide Retry Information: Use the Retry-After header to specify how long the user should wait before making another request. This can be very handy for applications that need to handle rate limit errors programmatically.
Offer Guidance: If applicable, provide users with guidance on how they can avoid hitting the rate limits in the future, such as upgrading to a higher tier of service or optimizing their API calls.

Best Practices for API Rate Limiting

To make the most out of API rate limiting, follow these best practices:

Assess API Call Frequency: Regularly review your API call frequency to ensure your rate limits are aligned with actual usage patterns. This helps you set limits that are neither too restrictive nor too lenient.
Use Dynamic Rate Limits: Implement dynamic rate limits that can adjust based on server load or user behavior, helping to maintain performance during unexpected traffic spikes.
Monitor User Activity: Keep an eye on how users are interacting with your API. Monitoring tools can help you detect patterns that might indicate abuse or excessive use, allowing you to adjust limits accordingly.
Set Appropriate Timeouts: Set timeouts that are reasonable for your API’s typical use cases. This helps prevent users from getting locked out for too long, improving their overall experience.
Use Caching: Implement caching strategies to reduce the number of requests that need to hit the API directly. This can help alleviate some of the load and reduce the likelihood of users hitting rate limits.
Provide Feedback on Rate Limit Errors: Make sure your API users are aware of when they’ve hit rate limits and what they can do next. This transparency can reduce frustration and help users manage their usage more effectively.
Have a Backup Plan: Consider having fallback strategies in place for when rate limits are hit, such as redirecting users to cached data or providing alternative API endpoints that are less heavily used.

Advanced Rate Limiting Strategies

As APIs grow in complexity and scale, basic rate limiting might not be sufficient to handle all the challenges that arise. Advanced strategies help manage traffic more effectively, ensuring fairness, performance, and a positive user experience. Let’s explore some of these advanced strategies:

Handling Sudden Traffic Spikes

Sudden spikes in traffic can occur for various reasons, such as a popular event, marketing campaign, or unexpected user activity. These spikes can overwhelm your API if not properly managed.

Dynamic Rate Limiting: This approach adjusts the rate limits in real time based on current traffic and server load. By dynamically scaling the rate limits, your API can handle traffic spikes more smoothly, preventing service disruptions. Dynamic rate limiting is particularly useful when traffic patterns are unpredictable, allowing the API to adapt on the fly.

Minimizing False Positives

False positives occur when legitimate traffic is incorrectly identified as abusive, leading to unnecessary throttling or blocking. Users may become frustrated and have a terrible experience as a result.

Fine-Tuning Rate Limiting Rules: Regularly review and adjust your rate-limiting rules based on actual usage patterns. By fine-tuning these rules, you can minimize the risk of legitimate traffic being blocked and ensure that your rate limits are both fair and effective.

Scaling to Handle Large Traffic Volumes

As your API scales, it’s crucial to ensure that your rate-limiting strategies can handle the increased traffic without becoming a bottleneck.

Distributed Rate Limiting: This approach spreads the load across multiple servers or instances, preventing any single point from becoming a bottleneck. Distributed rate limiting allows your system to scale horizontally, handling more traffic without sacrificing performance. It’s especially useful for large-scale APIs that need to maintain high availability under heavy load.
Integrating with CDNs and Load Balancers: Leveraging Content Delivery Networks (CDNs) and load balancers can further enhance your rate-limiting strategy. By distributing traffic and caching content closer to users, these tools can reduce the strain on your API and improve response times, even under heavy load.

Examples of API Rate Limiting in Action

To better understand how these concepts work in real-world scenarios, let’s look at some examples of how major platforms implement API rate limiting:

Facebook

Facebook uses rate limiting to control the number of requests third-party developers can make to its API. This helps ensure that the platform remains stable and that resources are fairly distributed among all users and applications. Facebook’s API limits can vary depending on the type of data being accessed and the user’s privacy settings. This helps the platform manage its resources efficiently while maintaining user privacy and data security.

Google Maps

Google Maps uses API rate limiting to manage access to its extensive location data. Developers can monitor their usage and quotas through the Google Developer Console, which provides detailed reports and alerts. This transparency allows developers to manage their API usage effectively and avoid unexpected charges. Google also offers the option to purchase additional quotas or credits if the default limits are not sufficient for a project’s needs.

Social media platforms like X (formerly Twitter) and Instagram implement rate limits to protect their services from abuse and to ensure that third-party applications do not negatively impact the user experience. These limits are typically enforced across all endpoints and are adjusted based on the specific API being accessed and the type of data requested. By implementing strict rate limits, these platforms can maintain the integrity of their services while allowing third-party developers to build on their ecosystems.

Rate Limiting Challenges and Solutions

Implementing rate limiting in APIs can be complex, especially as the API scales and the number of users increases. Here are some common challenges and their potential solutions:

Identifying the Appropriate Rate Limit

Challenge: Setting the correct rate limit is critical but can be difficult. If the limit is too low, legitimate users may be blocked from accessing the service. If it’s too high, it may not effectively prevent abuse or protect the system from being overwhelmed.

Solution: To identify the appropriate rate limit, start by analyzing historical data to understand typical usage patterns. Monitor the average number of requests per user and peak usage times. This data can inform your decision on where to set the initial limits. It’s also helpful to start with a conservative limit and gradually adjust it based on real-time monitoring and feedback.

Requirements of an Efficient Rate Limiting System Design

Challenge: Designing a rate limiting system that is both efficient and scalable can be complex. The system needs to be robust enough to handle varying traffic patterns without becoming a bottleneck itself.

Solution: An efficient rate-limiting system should include the following key components:

Scalability: Use distributed rate limiting to spread the load across multiple servers, ensuring that no single point of failure can disrupt the system.
Flexibility: Implement dynamic rate limits that can adjust based on current traffic conditions, allowing the system to respond to spikes in demand without manual intervention.
Accuracy: Ensure that your rate-limiting algorithms are precise, avoiding both false positives (where legitimate traffic is blocked) and false negatives (where abusive traffic is allowed through).

Combining Rate Limiting with Other Traffic Management Techniques

Challenge: Rate limiting alone might not be sufficient to manage all aspects of API traffic, especially in large-scale systems that handle diverse and unpredictable traffic.

Solution: Combine rate limiting with other traffic management techniques to create a comprehensive traffic management strategy:

Caching: Reduce the number of API requests by caching responses to frequently requested data. This not only improves performance but also reduces the load on the API.
Load Balancing: To avoid overloading any one server, distribute incoming traffic among several servers. This helps the API to maintain high availability and dependability.
Traffic Shaping: Control the flow of traffic by prioritizing certain types of requests or users, ensuring that critical operations are not delayed by less important tasks.

Bypassing API Rate Limits

While API rate limiting is essential for maintaining service quality and preventing abuse, there are methods that some users or developers may use to bypass these restrictions. Understanding these techniques and implementing safeguards is crucial to ensure that your API remains secure and fair for all users.

Ethical Considerations

API rate limits are in place to ensure fair usage, protect resources, and maintain the overall stability of the system. Bypassing these limits can lead to resource strain, degraded performance for other users, and even legal issues. It’s important to communicate clearly with your users about the purpose of rate limits and the potential consequences of bypassing them. Offering flexible pricing plans or usage tiers can help meet the needs of different users, reducing the temptation to circumvent limits.

Common Bypassing Techniques

Using Multiple API Keys: Some users may try to bypass rate limits by acquiring multiple API keys and distributing their requests across these keys. This can effectively increase their request capacity beyond the intended limit.
- Countermeasure: Implementing key-level rate limiting and tracking usage across all API keys associated with a single user or account can help mitigate this tactic. By monitoring for patterns that suggest multiple keys are being used to circumvent limits, you can take action, such as suspending keys or accounts that violate terms.
IP Address Rotation via Proxies or VPNs: Users might rotate their IP addresses by using proxy servers or VPNs to avoid hitting IP-based rate limits. This method allows them to appear as multiple users, each with its own rate limit.
- Countermeasure: To counter this, you can implement more sophisticated IP tracking methods that analyze patterns in IP usage, such as frequent changes in IP addresses associated with the same user. Additionally, combining IP-based rate limiting with key-level or user-level limits can create multiple layers of defence, making it more difficult for users to bypass limits by changing their IP address.
Exploiting Inconsistent Rate Limiting Rules: If different endpoints within an API have inconsistent or poorly enforced rate limits, users might exploit these differences to bypass overall restrictions. For example, they might target endpoints with higher or non-existent limits.
- Countermeasure: Ensure that rate limits are consistently applied across all endpoints and that more sensitive or resource-intensive endpoints have appropriate limits. Regular audits of rate-limiting configurations can help identify and correct any inconsistencies.

Conclusion

API rate limiting is not just a technical safeguard; it’s a fundamental part of maintaining a reliable, secure, and fair API service. By setting appropriate limits, implementing advanced strategies, and continuously monitoring usage, you can protect your API from abuse and ensure that it scales effectively as your user base grows.

As APIs become more central to digital services, the importance of robust rate limiting cannot be overstated. Whether you’re preventing traffic spikes, managing different user access levels, or defending against attempts to bypass limits, a well-designed rate-limiting strategy is key to delivering a consistent and reliable user experience.

In summary, by carefully planning, implementing, and maintaining your rate-limiting strategies, you can ensure that your API remains a valuable and sustainable resource for all users.

Frequently Asked Questions

We got an answer for your questions

What is API rate limiting?

API rate limiting is a technique that controls how many requests a user or an application can send to an API within a specified time frame. This practice helps maintain the API's availability and performance by preventing any single user or service from consuming excessive resources.
Why is API rate limiting important?

API rate limiting is important for protecting resources, ensuring fair usage, and maintaining service quality. It prevents abuse, such as denial-of-service (DoS) attacks, and manages traffic effectively to avoid overwhelming the API, which could lead to service degradation or outages.
What are the common methods of implementing API rate limiting?

Common methods for implementing API rate limiting include the fixed window approach, which limits the number of requests within a set period; the sliding window method, which tracks requests over a rolling time frame for more even distribution; the leaky bucket algorithm, which processes requests at a steady rate and discards excess; and the token bucket approach, which allows for bursts of traffic by using tokens that are consumed with each request.
How do you choose the right rate limit for an API?

Choosing the right rate limit involves analyzing historical traffic data to understand typical usage patterns, considering peak usage times, and evaluating the API's capacity. It is often advisable to start with conservative limits and adjust them based on real-time monitoring and user feedback to find the most effective balance.
How can API rate limiting be bypassed, and how do you prevent it?

API rate limiting can be bypassed through tactics such as using multiple API keys, rotating IP addresses with proxies or VPNs, or exploiting inconsistent rate limits across different endpoints. To prevent these tactics, you can implement key-level rate limiting that tracks usage across all keys for a user, monitor IP patterns to detect unusual activity, and apply consistent rate limits across all endpoints to close any loopholes.
What is the difference between API rate limiting and throttling?

The key difference between API rate limiting and throttling lies in how they manage traffic. Rate limiting sets a hard cap on the number of requests allowed within a certain time frame, blocking any excess requests once the limit is reached. Throttling, however, slows down the processing of requests after a certain threshold is reached, allowing requests to continue but at a reduced rate.
How can I handle rate limit errors gracefully?

Handling rate limit errors effectively involves returning clear and informative error messages, such as an HTTP 429 status code, along with a Retry-After header that indicates when the user can try again. Giving users tips on how to avoid hitting rate limits in the future, such as by optimizing their API calls or upgrading to a higher service tier, can also improve their experience.
What are the best practices for implementing API rate limiting?

Best practices for implementing API rate limiting include regularly assessing the frequency of API calls to ensure that rate limits align with actual usage patterns, using dynamic rate limits that can adapt to real-time traffic conditions, monitoring user activity to detect and address potential abuse, and combining rate limiting with other traffic management techniques like caching and load balancing to optimize performance.
How can I scale rate limiting to handle large volumes of traffic?

Scaling rate limiting to handle large volumes of traffic can be achieved by using distributed rate limiting, which spreads the load across multiple servers to prevent bottlenecks. Integrating rate limiting with content delivery networks (CDNs) and load balancers can further enhance the system's ability to manage high traffic volumes efficiently while maintaining performance and availability.
Can API rate limiting improve cost-efficiency?

Yes, API rate limiting can improve cost efficiency by reducing the strain on resources and avoiding the need for additional infrastructure to handle excessive traffic. By controlling the number of requests, rate limiting helps prevent the overuse of resources, which can lead to significant cost savings, especially as the API scales.

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