LLM Latency: How to Make Your AI App Faster

To reduce latency in an LLM app, pick the smallest model that meets your quality bar, cut the tokens you send and ask for, and stream the response so users see output immediately. Those three levers handle most of the problem before you need anything more complex.

This guide is for founders and small teams whose AI feature feels slow. It explains what to measure first, which fixes tend to give the biggest gains, and which ones to avoid until later.

What does latency mean for an AI app?

Anthropic defines latency as the time it takes for the model to process a prompt and generate an output, influenced by the size of the model, the complexity of the prompt and the infrastructure behind it. In its guide to reducing latency, it separates overall response time from time to first token, which is how long the model takes to produce the first piece of its answer and matters most when you stream.

For users, these feel different. A chat feature with a fast first token feels responsive even if the whole answer takes a while. A background task, such as summarising a document, cares about total time. Decide which one your feature is before you optimise anything.

Should you optimise latency before quality?

No. Anthropic’s advice is to first engineer a prompt that works well without model or prompt constraints, then apply latency strategies, because reducing latency too early can stop you discovering what top performance looks like. Get the feature right on your test set, then make it faster and re-run the same tests to check that quality held.

If you do not yet have a test set, build one first. Our guide to testing AI products with evals shows how, and it is what lets you make speed changes without guessing.

How does model choice affect response time?

Model choice is the most direct lever. Anthropic describes choosing the appropriate model for your use case as one of the most direct ways to reduce latency, and recommends its fastest tier for speed-critical applications. Smaller, faster models cost less and respond sooner, and for many routine tasks such as classification, extraction and short answers they are good enough.

A practical pattern is to route by task: send simple requests to a fast model and reserve a larger model for the hard ones. Our guide to choosing the right AI model for your app covers how to test whether a smaller model passes your own quality bar.

How do prompt and output length change speed?

Fewer tokens mean faster responses. Anthropic’s guidance is to minimise the tokens in both your input prompt and the expected output while keeping performance high. It suggests being clear but concise, asking for shorter responses, and setting appropriate output limits.

Two details are worth knowing. First, the guide notes that asking for an exact word count is less effective than asking for paragraph or sentence limits. Second, it warns that setting a hard maximum on output tokens will cut the response off, possibly mid-sentence, so it suits short or multiple-choice answers rather than free text. Trim instructions that repeat themselves, remove examples you no longer need, and send only the context the task actually requires. Sending less also means sending less customer information to a model provider, and the OAIC’s guidance on commercially available AI products is the right starting point if your prompts contain personal information.

Why does streaming make an app feel faster?

Streaming lets the model start sending back its response before the full output is complete. Anthropic notes this can significantly improve perceived responsiveness, because users can see output in real time, and your interface can process it as it arrives.

Streaming does not shorten the total generation time, but it removes the blank wait. For any user-facing chat or writing feature, it is usually the highest-value change per hour of effort. Make sure your error handling still works mid-stream, and show a clear state if a response is cut short.

What else should you check?

Look at the parts of your system that are not the model. Retrieval steps, database queries, tool calls and sequential chains of requests often add more delay than generation does. Run independent steps in parallel, avoid unnecessary round trips, and measure each stage separately so you know where the time goes. Our guide to AI observability and monitoring explains how to trace each step. If cost is also a concern, shorter prompts help there too, as covered in AI API cost optimisation.

If you are building an AI product and want a local team to tune it for production, book a free scoping call with the Neomeric studio.

Frequently asked questions

What is latency in an LLM app?

Anthropic defines latency as the time it takes for the model to process a prompt and generate an output, influenced by model size, prompt complexity and infrastructure.

What is the fastest way to reduce LLM latency?

Choosing an appropriate, faster model for the task is one of the most direct ways, followed by shortening prompts and outputs and streaming responses.

Does streaming make the model faster?

Streaming does not shorten total generation time, but it lets users see output as it arrives, which Anthropic notes can significantly improve perceived responsiveness.

Should I limit output tokens to speed things up?

Only for short answers. Anthropic warns that a hard output limit can cut a response off mid-sentence, so it suits multiple-choice or short-answer tasks.

When should I start optimising latency?

After your prompt works well on your test set. Anthropic advises engineering a good prompt first, because optimising too early can hide what top performance looks like.

Neomeric, a Melbourne-based AI product and consulting company — and the team behind NeoMind, Australia’s onshore AI teammates platform — ships AI products daily and tunes model choice, prompts and streaming so that features feel fast in production.

Sources

• Anthropic — Reducing latency
• OAIC — Guidance on privacy and the use of commercially available AI products
• Anthropic — Streaming messages