Voice agents have moved past simple speech recognition with rule-based responses. The current generation holds conversations, calls tools, and completes tasks while the caller stays on the line. But the market is crowded with similar-looking platforms, and the advertised price per minute rarely reflects what you actually pay to resolve a call.
The stack you’re really buying
An AI voice agent coordinates six moving parts: telephony or real-time transport, speech-to-text with turn detection, a language or speech-to-speech model, tools and business logic, text-to-speech, and state with guardrails and observability. A platform may bundle some of these or leave you to assemble them yourself.
Firecrawl’s comparison of ten platforms in 2026 shows the split clearly. Retell AI packages the full phone-agent experience with visual call flows and custom functions, priced at $0.07–$0.31 per minute. Vapi takes the opposite approach: $0.05 per minute for orchestration only, with speech, model, and phone costs billed separately or brought your own. ElevenLabs starts free with 15 minutes and paid plans from $6 per month, but the language model and telephony are extra.
Latency is a product decision, not a spec
Response latency measures the full wait from the end of the caller’s turn to the first agent audio. Firecrawl’s evaluation guidance suggests human turn transitions peak within 200ms, and sub-500ms agent responses feel responsive enough. But latency isn’t just a number—it interacts with barge-in behavior, false interruptions from background noise, and protected speech like legal disclaimers that must play without interruption.
When you choose a platform, you’re also choosing how much control you have over these behaviors. A composable platform like Vapi or LiveKit gives engineering control over models and telephony, but your team maintains more of the system. A packaged platform like Retell or Bland moves that work to the vendor, which suits teams that mainly need to change prompts and call flows.
Task completion beats feature lists
A voice agent’s value comes from what it can actually do behind the scenes. Firecrawl’s framework for task completion includes three checks: a successful write that confirms after the system responds, input coverage across names, addresses, and amounts in each supported language, and a failure path that doesn’t produce false confirmations or duplicate updates. Debugging requires one timeline connecting audio, transcript, model response, tool call, tool result, and transfer.
This is where the build vs. buy tradeoff gets concrete. If you’re already committed to a voice stack, ElevenLabs lets you build the workflow around it. If you need collaborative conversation design across chat and voice, Voiceflow is the visual builder option. For large contact centers with procurement and governance requirements, managed deployments like Synthflow or PolyAI make sense.
The real cost is per resolved call
Firecrawl’s cost breakdown is the most useful part of the comparison. The advertised price per minute may cover only one layer of the stack. When you combine voice stack, call outcomes, and operations, a $0.05 platform minute can become $0.13 after the rest of the stack is added—making a six-minute call cost $0.78. The decision metric should be total cost divided by resolved calls, compared with what the same task costs today.
This connects to a broader pattern in AI product building: the OpenAI flywheel shows how better models, cheaper compute, and broader reach compound. Voice agent platforms are following a similar path, but the compounding only works if you measure the full cost per resolved call, not the headline rate.
Where to start
If you need to ship quickly without maintaining real-time infrastructure, start with a packaged self-serve platform like Retell or Bland. If voice is part of your product and engineering needs control, look at Vapi or LiveKit. If you’re already on Twilio Voice, ConversationRelay is the natural fit. The supplied comparison does not cover every platform’s enterprise pricing or implementation details, so treat public rates as a starting point for your own cost modeling.
Sources
AI-assisted summary compiled from the sources above, reviewed by a human before publishing.