Retell AI Latency: Causes and Fixes

The difference between a voice agent that feels natural and one that feels obviously robotic usually comes down to a number most callers will never consciously notice: how many milliseconds pass between them finishing a sentence and the agent starting its reply. Get that number low enough and the conversation flows. Let it creep up and callers start talking over the agent, repeating themselves, or just hanging up.

I treat latency as a first-class concern on every Retell build, not something to check once a client complains the agent feels slow. This post covers where latency actually comes from in a voice AI pipeline, what tends to add delay that is fully within your control to fix, and where the ceiling genuinely sits outside the platform's hands.

What Retell AI Latency Means

Latency here means the gap between the caller finishing what they are saying and the agent's response beginning to play back. Retell's own architecture targets roughly 600 milliseconds end-to-end for this gap, and the general threshold where a conversation stops feeling human and starts feeling like talking to a machine sits somewhere around 700 milliseconds. Above that, callers notice. Below it, most people forget they are talking to AI at all.

Where Voice Latency Comes From

A real-time voice pipeline is really three sequential stages wrapped in two layers of orchestration: speech-to-text turns the caller's audio into words, the LLM decides what to say or which function to call, and text-to-speech turns that reply back into audio. Around all three sits turn-taking logic (deciding when the caller has actually finished speaking) and barge-in handling (what happens if the caller interrupts). Every one of these adds time, and the well-optimized platforms make their money by streaming each stage into the next rather than waiting for one to fully finish before starting the next.

Speech-to-text

This is usually not where the bulk of the delay actually sits, contrary to what people expect. Modern transcription models process audio close to real time. Where STT does add latency is in noisy environments or with accents the model handles less confidently, both of which can slow down how quickly it commits to a final transcription.

LLM

This is frequently the largest single contributor, specifically the time to first token, how long the model takes to start generating a response once it has the full context. A slower or more heavily loaded model adds directly to this number. This is also where a lot of the hidden latency lives: not in raw model speed but in how much context the model has to process before it can start responding, and how quickly a function call, if the model needs one, returns a result it can use.

Text-to-speech

Modern TTS engines start producing audio from the beginning of a reply before the entire reply is fully generated, which is what makes streaming pipelines fast. A slower TTS provider, or one configured to wait for a complete sentence before synthesizing, adds noticeably more delay than one built to stream from the first few words.

External APIs

This is the piece most within your control and most likely to be the actual culprit when a specific agent feels slower than it should. Every function call your agent makes mid-conversation, a calendar lookup, a CRM search, a database query, sits directly in the response path. A slow backend does not just delay that one exchange, it breaks the entire rhythm of the conversation for however long the caller is waiting on it.

How Interruptions Affect Perceived Latency

Latency is not only about raw milliseconds, it is also about how the conversation handles interruptions. A caller who starts speaking while the agent is still talking needs to be heard immediately, not after the agent finishes its current sentence. Poor barge-in handling makes even a genuinely fast pipeline feel sluggish and unresponsive, because the caller experiences a delay in being heard even if the underlying response time is fine.

Reducing Slow Tool Calls

If your agent uses custom functions, the pattern covered in Retell AI Function Calling, the speed of your own backend directly determines how the call feels during any moment the agent needs to check something real. Keep database queries fast, cache what can reasonably be cached, and set a hard timeout on any third-party API your function calls so a slow upstream service does not stall the entire conversation while the caller sits in silence.

Choosing Faster Models

If you have flexibility in which LLM your agent runs on, model choice has a real, measurable effect on time-to-first-token, and it is one of the more direct levers available if latency is a persistent problem rather than an occasional slow function call. This is a genuine trade-off against reasoning quality and cost, not a free upgrade, so treat it as one variable to test against your actual conversation requirements rather than defaulting to the fastest option blindly.

Testing Voice Agent Latency

Use Retell's live web call feature to listen to your agent directly rather than only reading transcripts after the fact. A transcript tells you what was said, not how the pauses actually felt in the moment. Pay particular attention to any point in the conversation where a function call happens, since that is where latency spikes are most likely to be hiding and least likely to show up in a casual read-through of the call log.

When Latency Is a Network Problem

Not every latency issue originates in the STT-LLM-TTS pipeline itself. Network instability, whether on the caller's end or somewhere in the audio transmission path, adds delay that no amount of model or backend optimization will fix. If latency is inconsistent, fine on some calls and noticeably bad on others, rather than a steady baseline issue, that pattern points toward network conditions rather than a configuration problem worth chasing inside the agent itself.

If your Retell AI voice agent feels slower than it should, whether that is a backend function call dragging on the response or a deeper pipeline configuration issue, diagnosing and fixing that is exactly the kind of work I take on for clients. Book a free 30-minute call and bring a recording of a call that felt slow, and we can work through where the delay is actually coming from.

Frequently Asked Questions

Why is my Retell AI agent slow?

Most often because a custom function call in the conversation path is taking too long, whether that is a slow database query or a third-party API without a reasonable timeout. Less commonly, it is a heavier LLM model or unstable network conditions on the call.

What causes voice AI latency?

The combined time across speech-to-text, LLM processing (particularly time-to-first-token), and text-to-speech synthesis, plus any external function calls the agent makes mid-conversation, and the turn-taking and barge-in logic wrapped around all three stages.

How can I reduce Retell AI latency?

Speed up any custom backend functions your agent calls, add timeouts so a slow API does not stall the whole conversation, and consider whether a faster LLM model meets your reasoning requirements if time-to-first-token is a persistent bottleneck.

Do external APIs make Retell slow?

Yes, often more than any other single factor within your control. A function call sits directly in the response path, so a slow calendar lookup or CRM query delays the agent's reply for exactly as long as that call takes to return.

Does the LLM affect voice latency?

Yes, significantly, primarily through time-to-first-token, how quickly the model begins generating a response once it has the conversation context. This varies meaningfully between models and is one of the more direct levers available if latency is a persistent issue.

What is good latency for a voice agent?

Roughly under 700 milliseconds end-to-end is the general threshold where a conversation still feels natural. Retell's own architecture targets around 600 milliseconds. Above that range, callers start to notice delays and the conversation feels less human.

If you would rather have this diagnosed than diagnose it yourself, I take on Retell AI voice agent work through Upwork.