AI GLOSSARY

Latency

Latency is the time between a request to an AI system and the response. It determines user experience, scalability, and costs. Those who ignore latency build applications that perform well in the lab but fail in real-world use.

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3

Time Ranges
Sub-second, seconds, minutes

4

Influencing Factors
Model, Context, Infrastructure, Network

6

Approaches
Streaming, caching, smaller models

6

Best Practices
for Low Latency

Why Latency Is Critical for AI Applications

Users give AI applications just a few seconds. Anything that takes longer loses their attention and acceptance. Latency is therefore not a peripheral issue—it is a key success factor for every productive AI application.
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User Acceptance

Response times longer than 5 seconds are perceived as too slow. Adoption plummets.

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Competitive Advantage

Faster applications attract users—latency is a key differentiator.

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Scaling Limit

High latency limits the number of concurrent users—the infrastructure must grow disproportionately.

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Cost Factor

Latency optimization often reduces costs as well—faster models are usually more cost-effective.

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Business Processes

In real-time processes (fraud, trading), milliseconds are critical.

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SLA Commitments

B2B customers demand guaranteed response times—latency is becoming a contractual issue.

What is latency in AI?

Latency is the time between sending a request and receiving a response. In AI applications, it encompasses several components: network latency, model processing time, database access (in RAG), response generation, and transmission.

Typical time ranges: sub-second (classification, embeddings, text extraction), 1–5 seconds (standard LLM queries, RAG responses), 5–30 seconds (complex reasoning tasks, large contexts, AI agents using tools), minutes (batch processing, in-depth analysis workflows).

Factors affecting performance: model size (smaller models respond faster), context window (more input, longer processing time), infrastructure (GPU availability, proximity to the region), provider utilization (slower during peak times), network (faster on-premises than via the cloud across continents).

For small and medium-sized businesses, latency design is practically relevant: What response time does the user expect? What is the acceptable limit? The answers to these questions drive model selection, architecture, provider choice, and costs. Latency is therefore a business decision, not just a technical one.

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Techniques for Latency Optimization

These eight techniques form the backbone of fast AI applications:

Streaming

Return responses token-by-token — perceived latency drops dramatically.

Prompt Caching

Cache repeated parts of the prompt — this reduces processing time for subsequent requests.

Smaller models

For simple tasks, choose this model over the larger one—it's faster and more affordable.

Regional Deployment

Operate models close to the user — network latency decreases.

Batching

Combine multiple requests — significantly faster when running on a GPU.

Speculative Decoding

Small models predict, large models verify — accelerating model generation.

Query Optimization

With RAG: faster vector search using appropriate indices.

Asynchronous Processing

Long background queries, with a response provided later — for non-interactive cases.

Best Practices for Low Latency

These six principles have proven effective:

  • Measure from the start: Track latency per component—not just total latency.
  • Streaming-first: Always use streaming for interactive applications—perceived latency is cut in half.
  • Choose the right model: Bigger isn’t always better—smaller is often faster and sufficient.
  • Control prompt length: Shorter prompts are faster—every token costs time.
  • Use caching: Cache recurring requests and prompt parts—this significantly speeds things up.
  • Define an SLA: Set clear targets for p50, p95, and p99 latency—measurable and negotiable.
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Range 1

Sub-second

Classification, embeddings. The user doesn't notice any delay. Standard for simple tasks.

Instant

Range 2

1–5 seconds

Standard LLM chat. Acceptable with streaming. Good for most applications.

Chat

Section 3

10+ seconds

Complex reasoning, agents using tools. Async UX or status display required.

Complex

Common Mistakes Related to Latency

We often see these pitfalls:

  • No streaming: The user waits 10 seconds for a complete response—with streaming, they would see something immediately.
  • Usingthe Largest Model Unnecessarily: GPT-4 for a task that GPT-4o-mini can handle—5 times slower.
  • Unfiltered RAG: Too many chunks in the context—each request takes longer than necessary.
  • Wrong region: Model in the U.S., user in Germany—network latency adds 100+ ms.
  • No monitoring: Latency issues aren’t discovered until users complain—too late.

Time-to-First-Token vs. Total Latency vs. Throughput

Three key latency metrics:

  • Time-to-First-Token (TTFT): Time until the first token is generated. Critical for the user experience during streaming.
  • Total Latency: Total time until a complete response is received. Important for non-interactive processes.
  • Throughput: The number of requests per second. Relevant for scalability.
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Frequently Asked Questions About Latency

Optimize the Latency of Your AI Applications

In a free initial consultation, we’ll analyze latency bottlenecks in your AI applications and outline an optimization plan—with clear target metrics.

As an AI partner for small and medium-sized businesses, we build fast AI applications with clean streaming architecture, appropriate models, and active monitoring.

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