Best platforms for enterprise voice agents

Every voice agent demo looks good. A founder opens a laptop, speaks a few sentences, and the agent responds with something reasonable in under a second. The room nods. But here's where it gets interesting: that same agent needs to handle 10,000 concurrent calls on a Tuesday morning when half your customer base decides to check their account balance at once. It needs to correctly capture email addresses, prescription numbers, and policy IDs—not approximate them. And it needs to do all of this while meeting the compliance requirements your legal team won't budge on.

The gap between a working demo and a production voice agent that enterprises actually depend on is enormous. Choosing the wrong platform means months of integration work followed by accuracy problems that surface only after you've committed.

So what should enterprise teams actually look for? And which platforms deliver? Let's break it down.

What enterprise voice agents actually need

Most platform comparison guides focus on surface-level features: language count, voice selection, basic latency numbers. That's table stakes. Enterprise voice agents have a different set of requirements that only become obvious once you're building for production.

Speech accuracy on entities—not just words

Overall word accuracy matters, but it's not the whole picture. Voice agents act on specific pieces of information: account numbers, email addresses, phone numbers, medication names, confirmation codes. If the speech-to-text layer gets "RX-7704132" wrong, the LLM downstream acts on bad data. The agent doesn't just mishear—it takes the wrong action. Missed entity rate is the metric that actually predicts whether your agent completes tasks or frustrates customers.

Sub-second latency that holds under load

A one-second response time in a controlled demo is easy. Maintaining that latency at scale—with thousands of concurrent sessions, tool calls mid-conversation, and noisy telephony audio—is a different engineering problem entirely. Ask any platform what their P95 latency looks like at peak concurrency, not just their best-case P50.

Turn detection that works in real conversations

This is the thing most teams underestimate. Basic voice activity detection (VAD) uses silence thresholds to decide when someone's done talking. But people pause mid-sentence. They say "um" while thinking. They say "uh-huh" to acknowledge without wanting to interrupt. If your voice agent architecture can't distinguish between a thinking pause and a completed turn, every conversation will feel awkward—and your users will notice immediately.

Compliance that's actually enforceable

For enterprise deployments, SOC 2 Type 2 certification is the baseline. If you're in healthcare, you need a Business Associate Addendum (BAA) in place before any patient data touches the platform. If you're in finance, you need audit trails and data retention controls. Don't settle for "we're working on it"—ask for the documentation.

Scalability without concurrency ceilings

Some platforms cap concurrent sessions or require concurrency commitments before you can scale. That's fine for a pilot. It's a problem when your contact center traffic spikes 3x on the first of the month and you're hitting rate limits. True enterprise platforms autoscale without renegotiation.

Mid-conversation flexibility

Production voice agents aren't static. You need to update the system prompt when a customer provides their account type. You need to swap tools when the conversation shifts from billing to technical support. You need to adjust VAD sensitivity when a caller is in a noisy environment. The ability to change configuration mid-session—without dropping the connection—separates infrastructure built for real use cases from platforms built for demos.

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The major platforms compared

There are several serious options for building enterprise voice agents in 2026. Here's an honest look at how they stack up—what each does well, where they fall short, and which use cases they're best suited for.

AssemblyAI Voice Agent API

AssemblyAI's Voice Agent API takes a cascaded architecture approach—dedicated best-in-class models for STT, LLM, and TTS, exposed through a single WebSocket. You stream audio in, you get audio back. One connection, one bill.

The speech understanding layer is built on Universal-3 Pro Streaming, which ranks #1 on the Hugging Face Open ASR Leaderboard. In benchmarks, it achieves 94.07% word accuracy with a 16.7% missed entity rate on names, emails, phone numbers, and credit card numbers. That entity accuracy gap is significant—we'll get into why shortly.

The developer experience is notably simple. It's a standard JSON API over WebSocket with no proprietary SDK required. Most developers get a working agent running the same afternoon. But simple doesn't mean limited: you get speech-aware turn detection (semantic + neural network + VAD), tool calling via JSON Schema, mid-session updates to prompt, voice, tools, and VAD settings, and 30-second session resumption if the WebSocket drops.

Pricing is a flat $4.50/hr covering the entire pipeline. No token math, no separate input/output charges. Six languages currently supported: English, Spanish, French, German, Italian, and Portuguese. On compliance, AssemblyAI holds SOC 2 Type 2 and ISO 27001 certifications, with a BAA available for healthcare use cases. Medical Mode is also available for improved accuracy on clinical terminology.

AssemblyAI Universal-3 Pro Streaming (bring-your-own-stack)

Not every team wants a managed pipeline. If you've already built an orchestration layer with LiveKit, Pipecat, or Vapi, you might want the best possible streaming speech-to-text without replacing the rest of your stack.

That's where Universal-3 Pro Streaming as a standalone STT API comes in. Same speech model, same accuracy, $0.45/hr for transcription only. Unlimited concurrent streams with autoscaling—no concurrency commitments required. It drops into existing pipelines as the STT layer, giving you full control over your LLM and TTS choices.

This is the best option for teams that already have an orchestrator and want to upgrade their speech understanding without rearchitecting everything.

OpenAI Realtime API

OpenAI takes a fundamentally different approach. Their Realtime API uses GPT-4o as a native multimodal model that handles audio as one of many modalities—text, images, video, and voice. It's not a pipeline designed specifically for speech conversations.

The upside is broad language support (99+ languages) and the ability to handle multimodal inputs. For prototyping conversational experiences, it's fast to get started.

The downsides become clear at enterprise scale. Pricing runs approximately $18/hr with per-token billing across 30+ event types—roughly 4x more expensive than cascaded alternatives. Word accuracy sits at 93.13% with a 23.3% missed entity rate, which reflects the trade-off of using a generalist model for speech-specific tasks. Turn detection relies on basic VAD rather than semantic understanding, which means more awkward interruptions in real conversations. And the developer surface area is complex—30+ event types to handle compared to a handful with purpose-built APIs.

OpenAI Realtime is a strong choice for demos, browser-first apps, and multilingual prototyping. But at enterprise scale, the cost and accuracy trade-offs add up quickly.

Deepgram Voice Agent API

Deepgram also uses a cascaded architecture, similar to AssemblyAI. Their voice agent offering runs approximately $4.50/hr but uses concurrency-metered billing—you'll need to commit to concurrency tiers as you scale.

Nova-3 achieves 92.10% word accuracy with a 25.5% missed entity rate—a meaningful gap compared to AssemblyAI's 16.7%. Turn detection relies on traditional VAD without the semantic layer that distinguishes thinking pauses from completed turns. Mid-session updates are limited to prompt and voice only. For enterprise deployments where accuracy on real-world data directly impacts outcomes, those gaps add up.

ElevenLabs Conversational AI

ElevenLabs built its reputation on voice synthesis, and their TTS remains excellent. Their Conversational AI product extends that focus into the voice agent space.

The enterprise limitation is the 30-agent concurrency cap. For contact centers or any high-volume deployment, that ceiling makes scaling impossible. ElevenLabs started as a TTS company—their speech understanding trails purpose-built STT providers, with a missed entity rate above 25.2%. For applications where voice output quality is the primary differentiator and concurrency demands are modest, it's a solid fit. For enterprise voice agents that need to scale, the constraints are significant.

Test voice agent accuracy on your own audio

See how Universal-3 Pro Streaming captures entities, handles accents, and detects turns. Compare the results to what you're getting from your current provider.

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Why speech accuracy is the deciding factor for enterprise

Here's the thing most teams don't realize until they're deep into production: transcription errors don't just reduce transcript quality. They cascade through the entire voice agent pipeline.

When your STT layer gets an email address wrong, the LLM doesn't know it's wrong. It processes the incorrect email as if it's fact, then takes action on it—sending a confirmation to the wrong address, looking up the wrong account, or storing incorrect contact information in your CRM. The agent didn't "make a mistake"—it did exactly what it was told with bad input.

The STT model you choose determines your agent's effective intelligence. A brilliant LLM fed wrong data will confidently do the wrong thing.

Consider a pharmacy refill scenario. A caller provides their prescription number, date of birth, medication name, dosage, address, and phone number in a single conversation. AssemblyAI's Voice Agent API correctly transcribes "RX-7704132" and "Metoprolol 80mg" while formatting the date of birth, address, and phone number accurately. In the same scenario, Deepgram's transcription produces "dash seven seven zero four one three two" without the RX prefix, drops date formatting, and garbles the medication dosage format.

That's not a cherry-picked example—it reflects the systematic accuracy advantage that comes from building an entire pipeline around a purpose-built speech model. When you're processing thousands of these conversations daily, the difference between a 16.7% missed entity rate and a 25.5% missed entity rate translates directly into failed tasks, repeat calls, and frustrated customers.

In our Voice Agent Report, 76% of respondents rated speech-to-text accuracy as the single most important factor when building voice agents—above latency, cost, and integration capabilities. The data backs up what builders already know intuitively: if the agent can't hear correctly, nothing else matters.

The two-path approach

One thing that sets AssemblyAI apart in the voice agent solutions space is that it offers two distinct paths to the same speech accuracy foundation.

The Voice Agent API is the fastest path to production. One WebSocket, one bill, working agent in an afternoon. It's purpose-built for teams that want to focus on their product logic rather than managing voice infrastructure. You write the system prompt, register your tools, and ship.

Universal-3 Pro Streaming as a standalone STT API is for teams that want full architectural control. If you've already invested in an orchestration framework, a specific LLM, or custom TTS, you can drop in the same speech model that powers the Voice Agent API without changing anything else in your stack.

Both paths share the same Universal-3 Pro speech recognition foundation. Transcription quality stays consistent regardless of which approach you choose. And because both use WebSocket connections and standard JSON, developers often prototype with the Voice Agent API for speed, then graduate to the bring-your-own-stack approach as their architecture matures and they want more control over LLM routing or TTS selection. Switching requires updating your connection endpoint and message handling—not rebuilding from scratch.

This two-path approach is a big part of why developers consistently rank AssemblyAI as the best voice agent API for developers—you get the simplicity of a managed pipeline when you want speed, and the flexibility of a raw STT API when you need control. You're not locked into a single integration pattern, and you're not forced to compromise on speech accuracy to get the architecture you want.

Choosing the right platform for your team

For enterprise voice agents, the decision comes down to three factors: accuracy, compliance, and scalability. Get any of them wrong and you'll feel it in production—either through failed tasks, blocked deployments, or infrastructure that can't keep up with demand.

AssemblyAI covers all three. The highest entity accuracy in the market means your agents complete tasks on the first attempt. SOC 2 Type 2, ISO 27001, and a BAA for healthcare mean your compliance team can sign off. Unlimited concurrency with flat-rate pricing means you scale without surprises.

But beyond the specs, there's a practical question worth asking: what does it feel like to build on this platform? AssemblyAI's developer experience is deliberately simple—standard JSON over WebSocket, no proprietary SDKs, documentation you can read in 10 minutes. That simplicity compounds over time. Fewer moving parts mean fewer failure surfaces, faster debugging, and less operational overhead.

The best way to evaluate any voice agent platform is to have a conversation with one. Try the live demo—no signup required—and judge the accuracy, turn detection, and conversation flow for yourself.

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Frequently asked questions

What is the best API for building voice agents?

AssemblyAI's Voice Agent API is the best API for building voice agents in 2026, combining industry-leading speech accuracy (94.07% word accuracy, 16.7% missed entity rate) with a flat $4.50/hr rate that covers the entire STT, LLM, and TTS pipeline. It's a single WebSocket connection with no proprietary SDK required, and most developers ship a working agent the same day. For teams that want to bring their own LLM and TTS, Universal-3 Pro Streaming provides the same speech foundation at $0.45/hr as a standalone STT layer.

How much does it cost to build an enterprise voice agent?

Costs vary significantly by platform. AssemblyAI's Voice Agent API charges a flat $4.50/hr covering speech understanding, LLM reasoning, and voice generation—no token math or separate invoices. OpenAI's Realtime API runs approximately $18/hr with per-token billing. Deepgram's voice agent offering is roughly $4.50/hr but requires concurrency commitments as you scale. For high-volume enterprise deployments, the billing model matters as much as the per-unit cost.

What compliance certifications should a voice agent platform have?

At minimum, enterprise voice agent platforms should hold SOC 2 Type 2 certification, which verifies ongoing security controls. Healthcare organizations need a Business Associate Addendum (BAA) in place before processing any patient data. AssemblyAI holds SOC 2 Type 2 and ISO 27001 certifications, with a BAA available for healthcare use cases and Medical Mode for improved clinical terminology accuracy.

Can I use my own LLM with AssemblyAI's voice agent infrastructure?

Yes. AssemblyAI offers two paths. The Voice Agent API includes a managed LLM as part of its all-in-one pipeline, but if you want full control, Universal-3 Pro Streaming gives you AssemblyAI's industry-leading STT as a standalone API that drops into your existing orchestration stack—whether that's LiveKit, Pipecat, or a custom pipeline. You bring your own LLM and TTS while getting the same speech accuracy foundation.

What is the best one-API solution for voice agents?

AssemblyAI's Voice Agent API is the leading one-API solution for voice agents. It replaces three separate providers (STT, LLM, TTS) with a single WebSocket connection, one invoice measured in hours, and one set of logs. At $4.50/hr flat, it eliminates the token math and multi-vendor complexity that slows down development and creates fragile production systems.

How does turn detection work in voice agents?

Turn detection determines when a user has finished speaking and when they're trying to interrupt. Basic approaches use silence thresholds (VAD), which often cut users off mid-thought or create awkward pauses. AssemblyAI's Voice Agent API uses semantic turn detection that considers what the user actually said—not just silence—to decide if they're done. It distinguishes back-channel responses like "uh-huh" from real interruptions like "wait, stop," and adapts its timing to each user's speaking pace throughout the conversation.