What is Network Latency? Meaning and examples

What is Network Latency?

Network Latency: Network latency is the time it takes for data to travel from one point to another on the network, measured in milliseconds, and it impacts how fast AI applications respond.

Updated on 11/8/2026 · By Marcelo Santiago

How it works

In AI applications, network latency adds up the time to send the prompt, processing on the server, and returning the response. Factors include physical distance, connection quality and server load.

For agents that make multiple API calls, latency accumulates. An agent that makes 5 sequential calls with 200ms latency each will have at least 1 second of wait just from network.

Latency is not the same as throughput. Latency is the time for one request. Throughput is how many requests per second the system processes. A system can have high latency and high throughput (processes many requests, but each one takes a while).

Practical example

An agent that makes 5 sequential calls to an API with 200ms latency each will have at least 1 second of wait just from network. Reducing latency (using closer servers or parallel calls) improves the experience.

For real time chatbots, high latency causes a poor experience. The user types and waits several seconds for the response. Solutions include streaming (sending tokens as they are generated), response caching and geographically close servers.

What it's for

Optimize AI applications, choose deployment regions and design efficient agents. Understanding latency helps decide between remote API and local model, and to design flows that minimize sequential calls.

For developers, this means measuring latency in production and optimizing bottlenecks. For architects, it means choosing infrastructure based on response time requirements.

Difference between latency and throughput

Latency is the time for a single request to go and come back. Throughput is how many requests the system processes per second. A system can have low latency and low throughput (responds fast, but few requests). Or high latency and high throughput (takes long, but processes many at the same time).

In AI, batching increases throughput but can increase latency (the request waits for the batch to form). Streaming reduces perceived latency (the user sees tokens as they are generated) without changing total latency.

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Frequently asked questions about Network Latency

What is network latency?

It is the time it takes for data to travel from one point to another on the network, measured in milliseconds. It affects the response speed of AI applications.

How to reduce latency in AI applications?

By using servers closer to users, parallel calls instead of sequential ones, token streaming, and response caching.

Does latency affect the quality of AI responses?

It does not affect content quality, but it does affect user experience. High latency makes the system feel slow, even if the response is good.