Cut API Latency: Diagnose, Measure and Optimize

Let's say you're a developer working on diagnosing a performance issue in a staging environment, and you've just triggered a request to a critical backend service—maybe to fetch a large configuration file or to simulate a complex user transaction via an API call. You hit 'Send' or execute the command, and you're expecting a response. But then nothing happens.

The terminal cursor blinks, or your testing tool's progress bar seems to hang in limbo. And after a few anxious seconds that feel much longer, the response data finally begins to stream in, or the 'success' message appears.

That lag between initiating your request and the first sign of a response from the API is what we call API latency. And it's one of the most important factors that affect how fast, responsive, and usable an application feels—especially in systems that rely heavily on such inter-service communication or external APIs.

In this article, we’ll break down what API latency really is, what causes it, how it impacts performance, and how to measure it accurately. We’ll also cover the key factors that affect latency across environments and share practical ways to reduce it in production systems.

What is API latency?

API latency refers to the amount of time it takes for a request made to an API to receive a response. It begins the moment a request is sent and ends when the response is fully received.

This duration includes several steps: the time taken for the request to travel over the network, the time the server spends processing that request, and the time it takes for the response to return to the client.

Figure 1: Request-response workflow

Latency is typically measured in milliseconds and is an important factor in the performance of any application that relies on APIs. Lower latency means faster response times, which results in a smoother user experience. High latency means the opposite, and can lead to noticeable delays which might affect the usability of the application.

Components that affect API latency

Several components affect API latency, and they often stack up without you noticing. The following are some of the most common factors:

1. Network delay: This is the time it takes for a request to travel from the client to the server and back. It's influenced by physical distance, the speed of the internet connection, and the number of hops between the client and server.
2. Server processing time: Once the API receives the request, it needs to process it—this includes executing business logic, querying databases, validating data, or performing calculations. The more complex the logic, the longer it takes.
3. Database performance: If the API needs to fetch data from a database, latency can increase depending on how optimized the database queries are, how much data is being requested, and the current load on the database.
4. Third-party dependencies: Some APIs depend on other external services to complete a request. If any of those services are slow or unresponsive, it adds to your overall latency.
5. Load and traffic: If the API server is under heavy load—like handling too many requests at once—responses may be delayed due to limited resources.
6. Caching (or lack of it): When data isn't cached and has to be retrieved fresh every time, it increases latency. Proper caching can significantly reduce the time it takes to respond.

API latency vs. API response time

The terms API latency and API response time are closely related and often used interchangeably, but there is a subtle difference between them—especially in technical discussions.

As established earlier, API latency primarily measures the time until the first byte of the response is received, capturing network travel time and the initial server processing delay.

API response time, on the other hand, is the total time it takes from when a request is sent to when the full response is received and ready to be processed by the client. It includes latency, server processing time, and any time spent transmitting the complete response back to the client.

In short:

• Latency = Time to first byte.
• Response time = Time to complete response.

This distinction is more noticeable when dealing with large payloads or slower networks. A response might have low latency but still, take a long time to finish if the payload is large or the connection is slow.

How to measure API latency

API latency can be measured by recording the time it takes from when a request is sent to when the first byte of the response is received. This can be done in a few ways, depending on your setup and what tools you're using.

1. Using developer tools in the browser: If you're testing an API call from a web application, your browser's network tab (in Chrome or Firefox DevTools) can show the latency.

Look for the "Time" or "Waiting (TTFB – time to first byte)" column. This shows how long the API took to start sending back a response.

In Chrome, this is what it looks like:

Figure 2: Measuring API latency with Chrome DevTools

2. Using curl with the -w flag: You can use curl in the command line to send a request and measure timing. You can use the -w flag to format the output and include specific timing metrics:

curl -o /dev/null -s -w "Total time: %{time_total}\nTTFB: %{time_starttransfer}\n" https://api.example.com/endpoint

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From the code above, time_starttransfer is the latency (time to first byte), and time_total is the full response time.

When you run this command, the response should have the following format:

Total time: 0.716304

TTFB: 0.578939

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3. Using API monitoring tools: Tools like Hoppscotch will display response times automatically when you send a request. Some even break down the timing into DNS lookup, TCP handshake, SSL setup, and server response.

4. Logging on the server side: If you control the API server, you can log timestamps at different stages—when the request is received, when processing starts and ends, and when the response is sent. This gives more visibility into what part of the process is causing delays.

5. APM tools (Application Performance Monitoring): Platforms like Prometheus with Grafana can help track latency over time, break it down by endpoint, and alert you when latency exceeds certain thresholds.

For systems involving multiple microservices, distributed tracing (using standards like OpenTelemetry) becomes essential within these tools to pinpoint delays within complex request paths. 

How to reduce API latency, one layer at a time

Reducing API latency involves optimizing several parts of the request-response cycle—including decisions made early during API development. By building efficient request patterns, limiting unnecessary dependencies, and applying performance best practices from the start, you can prevent latency before it becomes a problem. Here are some common techniques:

1. Implement caching: One of the most effective ways to reduce API latency is by caching frequently requested data. This reduces the need to repeatedly fetch data from a database or an external service. You can implement caching at the client level, on the server, or through an intermediary like a CDN.
2. Optimize backend logic: Complex or inefficient operations on the server side—like unoptimized database queries or heavy computations—can slow down responses. Profiling your backend to find bottlenecks and improving query performance can have a big impact.
3. Reduce payload size: Sending only the necessary data—using fields filtering or pagination—cuts down the time spent serializing, transmitting, and deserializing the data.
4. Use fast and regionally distributed infrastructure: Deploy your APIs closer to users geographically. This reduces physical network distance and speeds up data transfer.
5. Optimize third-party calls: If your API depends on other services, minimize their impact by parallelizing calls, setting appropriate timeouts, and handling retries efficiently.
6. Use persistent connections and HTTP/2: Enable connection reuse (keep-alive) or use HTTP/2 to reduce overhead from repeatedly opening new connections.
7. Tune TLS/SSL settings: Use modern encryption settings and session resumption techniques to reduce the time spent on secure handshakes.

Best practices for maintaining low latency

There are several best practices you can follow to maintain low latency in your APIs. The following are some of the most effective:

1. Monitor latency continuously: Use observability tools to track latency metrics (like p90 or p99 response times) across API endpoints. Set alerts for SLO breaches or significant spikes so you can respond quickly.
2. Profile and benchmark regularly: Periodically test your API under realistic conditions using tools like Apache JMeter, or k6. Identify slow endpoints and investigate bottlenecks.
3. Use caching smartly: Implement cache invalidation strategies to keep responses fast and accurate. Combine short-term (in-memory) and long-term (CDN or Redis) caching where needed.
4. Minimize unnecessary work in each request: Offload non-critical tasks—like logging, analytics, or background jobs—to asynchronous queues (e.g., using RabbitMQ, Celery, or AWS SQS).
5. Avoid over-fetching and under-fetching: Design APIs (especially REST or GraphQL) to allow clients to request exactly the data they need. Avoid sending large payloads by default.
6. Reduce third-party dependencies where possible: Every external call introduces potential latency. Use bulk requests or batch processing if integration is necessary.

Reduce delays, deliver better experiences

API latency plays an important role in the performance of any application that relies on APIs, directly impacting user satisfaction and potentially business metrics like conversion rates By understanding its components, measuring it accurately, and actively applying optimization techniques, developers can ensure their applications remain fast, responsive, and reliable. To maintain this level of performance over time, it's important to continuously monitor and refine latency against established goals—this helps deliver consistently excellent digital experiences.