Vapi runs the call.
Speak AI analyzes
every conversation.

It depends on the job. If you are a developer building real-time voice agents, Vapi is purpose-built infrastructure with sub-500ms latency and strong tooling. If you need to capture, transcribe, and analyze conversations, and give a team one searchable archive with audio analysis, video analysis, and AI chat, Speak AI is the stronger fit. Many teams use both: Vapi runs the live call, and Speak AI analyzes the recordings.

For building low-latency voice agents, Vapi is one of the strongest options, with Retell AI, Bland, and open-source stacks like LiveKit Agents and Pipecat as the common alternatives. For understanding conversations after they happen, transcription, tone of voice, what was on screen, and analytics across a whole library, Speak AI is the better platform, and it includes no-code voice agents of its own.

As of August 2026, Vapi’s Build plan charges a $0.05 per minute platform fee, with speech-to-text, LLM, and text-to-speech provider costs passed through at cost (waived if you bring your own API keys). Independent breakdowns put typical all-in costs around $0.10 to $0.30 per minute once providers and telephony are included, and concurrency beyond 10 lines costs $10 per line per month.

Paid. New accounts get about $10 in trial credit to test calls, but there is no ongoing free tier: usage is billed per minute plus provider costs. Speak AI offers a trial with credits, then transparent subscription plans.

It can be economical at small scale, and costs grow with usage. The platform fee is $0.05 per minute, but real bills stack LLM, speech-to-text, text-to-speech, and telephony charges, and add-ons like HIPAA compliance ($2,000/month) and Zero Data Retention ($1,000/month) are priced for enterprises. Budget from the all-in number, around $0.10 to $0.30 per minute as of August 2026, rather than the base fee.

Pipecat, LiveKit Agents, and Vocode are the most cited open-source frameworks for building voice agents, trading Vapi’s managed infrastructure for full control and self-hosting. None of them handle the analysis side: transcribing recorded files, reading tone and screens, and keeping an archive your team can query. That is what Speak AI is built for.

They are close competitors for developer voice agents. Vapi is known for configurability, a model-agnostic stack, and Squads multi-agent handoffs; Retell AI is often praised for a smoother start and contact-center features. Either can run excellent live calls. If your real need is analyzing conversations rather than conducting them, compare both against Speak AI instead.

Vapi transcribes live calls in real time and runs per-call analysis: a summary, structured data, and a success evaluation attached to each call. It does not accept uploaded audio or video files, and it has no cross-call analytics, so you cannot transcribe existing recordings, track sentiment across a library, or chat with your whole archive. Speak AI does all of that as its core job.

The dashboard has improved, but Vapi is designed for developers: assistants, tools, and integrations are configured through APIs, prompts, and webhooks, and reviewers consistently describe a steep learning curve for non-technical users. Speak AI is no-code end to end, used by researchers, consultants, marketers, and operations teams without engineering help.

Yes. Speak AI offers AI voice agents with no-code setup. Vapi goes deeper on developer control, latency tuning, and multi-agent orchestration. The difference is what happens next: with Speak AI, the agent’s conversations flow into the same archive as your meetings and uploads, analyzed with the same tone, screen, and NLP pipeline.

No to both. Vapi is infrastructure for live voice agents, with no embeddable recorder for collecting async audio or video responses and no white-label layer for presenting results under your own brand. Speak AI offers both: embeddable audio and video recorders for websites and apps, plus white-label deployment for agencies and platforms.