Scope & boundaries
This guide covers replacing the IVR with an agent that holds a conversation — and reading a per-minute rate correctly, since the four meters inside it are bundled differently by every vendor.
It does not cover routing, recording, workforce management and the humans it escalates to (Contact Center as a Service (CCaaS)), text channels, where neither constraint applies in the same way (Conversational AI & Chatbot Platforms), internal employee support, priced per agent, conversation or outcome (AI Service Desk & Employee Support Agents), or analyzing conversations a human had, and what changes as a result (AI Conversation Intelligence & Analytics).
Executive Summary
A voice minute is assembled from four meters that different vendors bundle differently. The headline rate tells you which layers a vendor decided to include, not what a call will cost you.
This is the rare AI category with genuinely published pricing, and the transparency creates its own trap. Retell AI publishes a range of $0.07 to $0.31 per minute and, unusually, breaks the stack out: $0.04 per minute for the LLM, $0.055 for its own voice infrastructure, $0.015 for text-to-speech. Vapi prices calls at $0.05 per minute and passes model calls through at cost, or at $0 if you bring your own API key. Those two headline numbers are not measuring the same thing, and a shortlist built by sorting on them will be sorted wrongly.
Underneath the pricing question is a scope question that decides more. Some products here are infrastructure — you build the agent. Some are contact-center platforms that arrive with the workflows, the compliance posture and an implementation team. Both call themselves voice AI platforms, they differ by roughly an order of magnitude in cost and effort, and the demo does not distinguish them because a good demo is easy in both.
Why the IVR Is Finally Being Replaced
Voice automation has been promised for twenty years and mostly delivered phone trees people hate. What changed is not the ambition but three specific engineering problems getting solved close enough together to matter: speech recognition that handles real accents and interruptions, models that hold context across a conversation, and latency low enough that the pause before a reply stops feeling like a machine. ElevenLabs frames the shift directly, describing IVR menus, scripted chatbots and long hold times as what conversational agents replace.
Latency deserves more weight than buyers usually give it, because it is the variable that decides whether callers accept the thing at all. A response that arrives half a second late reads as a bad line; a second late reads as a machine, and callers start pressing zero. That single number is a function of the whole chain — transcription, model inference, speech synthesis, telephony — which is why vendors keep collapsing the stack. Deepgram unifies speech-to-text, text-to-speech and LLM orchestration into a single API, and Cartesia offers real-time speech and transcription models through one API purpose-built for voice agents. Fewer hops is the argument, and it is a real one.
The other thing worth deciding early is what the agent is allowed to do. An agent that answers questions is a containment play and the risk is a bad answer. An agent that reschedules an appointment, takes a payment or changes an address is doing consequential work over an unauthenticated channel, and the identity question that a web form answers with a login has no equivalent on a phone call. Most disappointing deployments in this category are not model failures; they are scope decisions made before anyone thought about authentication.
Which type of AI Voice Agents & IVR Replacement fits your organization?
Building is genuinely viable here and the components are all purchasable, which is unusual. LiveKit describes an open source framework and developer platform for building, testing, deploying, scaling and observing agents in production, and provides automatic turn detection and interruption handling — which is the hard part of voice, not an incidental feature. A competent team can assemble a working agent from a transcription API, a model and a speech synthesizer in days.
What a team cannot easily build is the last twenty percent: barge-in that feels natural, graceful recovery when the caller says something unexpected, telephony that survives a bad mobile connection, and the compliance apparatus. That is what the platforms sell, and it is the difference between a demo that works and a system that answers ten thousand calls without producing complaints. The sourcing question is therefore not really build versus buy — it is how much of that last twenty percent you intend to own.
| Layer | What you are buying | What it costs you |
|---|---|---|
| Speech infrastructure | Transcription and synthesis as APIs, priced per minute | Everything above it. You are buying components, not an agent. |
| Agent developer platform | Orchestration, telephony and monitoring; you write the logic | Engineering ownership, indefinitely. The platform will not design your conversation. |
| Contact-center platform | Workflows, integrations, compliance and an implementation team | Cost and cycle time. This is a procurement, not a signup. |
| Outcome-priced agent | Resolved conversations rather than access | Defining the outcome, which is the negotiation and happens once. |
| Open source framework | The turn-taking and media plumbing, self-hosted | Operations. You now run real-time media infrastructure. |
| Your CCaaS vendor's own agent | Whatever is bundled into the platform you already run | Ceiling. It is as good as their roadmap and you cannot shop separately. |
How do you evaluate AI Voice Agents & IVR Replacement?
Voice quality is what buyers notice in demos and it is close to solved — the synthesis in every serious product here is good enough that callers do not object. The variables that decide whether a deployment survives are less audible: how fast it replies, what it does when interrupted, and what happens at the boundary of what it can handle.
Four vectors separate these products once the demos end. Latency is first and is the whole experience; measure it end to end, on your own telephony, not in a browser demo. Interruption handling is second, and it is a genuinely hard engineering problem — LiveKit provides automatic turn detection and interruption handling, and the products that treat this as a first-class concern behave visibly better with real callers. Escalation quality is third: what the human receives when the agent gives up, and whether the caller has to repeat themselves. Fourth is integration depth, because an agent that cannot read the account record is a phone tree with better grammar.
| Capability | What it does | Buyer translation |
|---|---|---|
| End-to-end latency | Time from caller finishing to agent replying | The whole experience. Measure on your telephony, at your peak, not in a browser. |
| Barge-in and turn detection | Handles being interrupted mid-sentence | LiveKit provides automatic turn detection and interruption handling — the difference between conversation and monologue. |
| Stack consolidation | Whether transcription, model and synthesis share one API | Deepgram unifies speech-to-text, text-to-speech and LLM orchestration into a single API. Fewer hops, less latency, less choice. |
| Escalation to human | What happens when the agent cannot help | Where deployments fail in production. Ask what context transfers, and whether the caller repeats themselves. |
| Channel breadth | Whether one agent covers voice and text | Sierra deploys one agent across chat, SMS, WhatsApp, email, voice and ChatGPT — one definition instead of several. |
| Compliance posture | HIPAA, PCI, data retention controls | Priced separately and rarely budgeted. Establish the number before the pilot, not after. |
| Concurrency headroom | How many simultaneous calls you can take | Metered on some platforms. Vapi lists call concurrency as 10 included plus $10 per line per month. |
Which vendors lead in AI Voice Agents & IVR Replacement?
The camps below are organized by what you have to build, because that predicts both the cost and the calendar far better than any feature list. The gap between the first and last camp is roughly an order of magnitude in both.
One caution about reading these pages. Every vendor now describes natural, human-like conversation at enterprise scale, and the copy has converged to near-interchangeability. The distinctions that survive production are not in the copy: they are measured latency on your telephony, behavior when interrupted, what the human receives on escalation, and what compliance costs as a line item. Ask each vendor which call types they would advise you not to automate. A vendor with a real answer is describing a product; a vendor with none is describing a demo.
| Vendor | Approach | Where it fits |
|---|---|---|
| Retell AI | Agent developer platforms | Teams wanting per-minute transparency and a published cost breakdown |
| PolyAI | Conversational specialists | Brands where the quality of the conversation is the point |
| Cresta | Enterprise CX platforms | Established contact centers automating alongside human agents |
| LiveKit | Open source frameworks | Teams self-hosting for data residency or unusual telephony requirements |
| Sierra | Outcome-priced agents | Buyers who prefer paying per resolution to forecasting minutes |
| Deepgram | Speech infrastructure | Builders who want the speech layer consolidated behind one API |
One representative of each approach is named here; the category runs to several dozen vendors and several occupy more than one camp. The camps were written before the vendors were chosen, and no placement here is for sale. Any vendor in this category can speak for themselves in the Spotlight below.
How much should you budget for AI Voice Agents & IVR Replacement?
The per-minute rate is the headline everywhere and it is assembled from separate meters that vendors bundle differently. Retell AI is unusually explicit: it publishes a range of $0.07 to $0.31 per minute and breaks out $0.04 per minute for the LLM, $0.055 for its own voice infrastructure and $0.015 for text-to-speech. Vapi takes the opposite approach, pricing its own layer at $0.05 per minute and listing calls at cost, or at $0 if you bring your own API key. Deepgram, selling the speech layer alone, lists a current price of $0.0065 per minute against a regular price of $0.0077.
Read those three together and the comparison problem is obvious. Retell's number covers the model, the infrastructure and the voice. Vapi's covers the platform and leaves the model to you. Deepgram's covers transcription only. All three are honest and all three are quoted per minute. Before comparing any two rates in this category, write down which of the four layers — transcription, model, synthesis, telephony — each one includes, and add the missing ones at their own rates.
Two costs sit outside the per-minute discussion entirely. Compliance is the first and it is explicit rather than assumed: Vapi prices a HIPAA add-on at $2000 per month and zero data retention at $1000 per month. Those are fixed monthly costs independent of volume, which means they dominate the economics of a small deployment and disappear into the noise of a large one — and they are almost never in the business case a pilot produces. The second is the human path. Voice agents do not eliminate a contact center; they change its mix toward harder calls, which are handled by more experienced people and take longer. A deflection model that assumes the remaining calls cost what the average call cost today will overstate the saving.
| Basis | You are charged for | Grows with | Where it goes wrong |
|---|---|---|---|
| Per minute, full stack | Model, infrastructure and voice in one rate | Talk time | Nothing much — but it is not comparable to a platform-only rate. |
| Per minute, platform only | The orchestration layer; models passed through at cost | Talk time, plus your own model bill | Comparison. The headline looks cheaper because it covers less. |
| Per minute, speech only | Transcription or synthesis as a component | Audio processed | Being read as an agent price. It is one layer of four. |
| Per concurrent line | Capacity to take simultaneous calls | Peak, not volume | Seasonal peaks, which set the bill for the whole year. |
| Per outcome | Resolved conversations | Resolution volume | Defining resolution. It is negotiated once and lived with. |
| Compliance add-on | HIPAA, zero data retention, and similar | Nothing — it is fixed monthly | Small deployments, where a fixed surcharge dwarfs the usage. |
| Telephony passthrough | Carrier minutes underneath everything else | Talk time again | Being forgotten. It is a separate bill from a separate vendor. |
Rates quoted here are the figures each vendor publishes on its own pricing page, with the publisher named. Where a page prints a figure whose unit lives in a separate table cell — ElevenLabs, Twilio and Google all do this — the evidence ledger records the page and carries no claim, because the unit cannot be quoted alongside the number.
How long does implementation take for AI Voice Agents & IVR Replacement?
The sequencing that works here is narrow-then-wide, and the discipline that matters is resisting the temptation to start with the call type that has the most volume. Start with the one that has the least ambiguity, prove the escalation path on it, and widen from a working system rather than from a hopeful one.
Order status, appointment confirmation, balance inquiry — something with a clear success condition and a low cost of failure. High-volume call types are tempting and are the wrong place to learn what your telephony does under load.
Not in the vendor's browser demo. Through your carrier, at your peak, on a mobile connection. This is the number that determines whether callers accept the agent, and it is routinely two or three times what the demo suggested.
Run live with a low containment target and instrument every handoff. What the human receives, and whether the caller has to repeat themselves, decides whether contact-center staff support this deployment or quietly work around it.
Each new call type is a new conversation design and a new failure mode. Watch what happens to average handle time on the calls that still reach humans — it will rise, and the business case needs to have said so.
An agent confirming an appointment carries little regulatory weight. The same agent taking a payment, discussing a medical result or making an eligibility decision sits inside a regulated process, and the obligations attach there. Two things are specific to voice and routinely missed. Disclosure: many jurisdictions require callers to be told they are speaking to an automated system, and where the voice is convincing the expectation is stronger rather than weaker. And identity: a phone call has no login, so any agent authorized to change an account is authenticating a caller by knowledge alone, which is the weakest form there is.
Classified under the EU AI Act's transparency obligations for systems interacting with people, together with sector rules attaching to the process the call sits inside
What should you ask vendors about AI Voice Agents & IVR Replacement?
The first question resolves most of this category, and it is worth answering internally before any vendor is contacted.
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Do you have engineers to own the conversation design?Yes A developer platform gives you control and transparent per-minute pricing. Budget the engineering, not just the minutes.No An enterprise CX platform or a conversational specialist. The services cost is real and it is buying you the thing you lack.
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Will the agent take consequential action, or only answer?Yes Authentication is now the hard problem, not the model. Solve it before scoping anything else.No Containment is the metric and the risk is a wrong answer. Both are manageable, and the project is much simpler.
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Does your data carry HIPAA, PCI or residency obligations?Yes Price the compliance add-ons into the pilot business case. They are fixed monthly and they dominate small deployments.No Compare on latency and escalation quality. Those decide whether callers accept it.