Vapi vs Retell AI: Voice Agent Comparison

Vapi and Retell AI are the two most popular developer-focused voice AI agent platforms in 2026. Both let you build real-time voice agents that make and receive phone calls. Both are LLM-agnostic. Both have Python and TypeScript SDKs.

If they are so similar, why does the choice matter? Because they make different tradeoffs in pricing structure, telephony setup, configuration depth, and specific features like batch calling and multi-agent routing. For most teams, one of these differences will be the deciding factor.

This comparison is written for developers evaluating both platforms. We cover the real differences rather than repeating the marketing surface.

Overview#

Vapi launched in late 2023 with a philosophy of maximum configurability. Every component of the voice pipeline — STT, LLM, TTS, telephony — is independently selectable and configurable. Vapi acts as the orchestration layer; you assemble the pipeline from parts.

Retell AI launched around the same time with a slightly different philosophy: give developers the important choices (LLM selection, voice selection) while making sensible defaults for the rest (bundled telephony, default STT/TTS). Get developers to a working product faster.

Both platforms have evolved since launch, adding features that blur these distinctions — but the philosophical difference still shows in how each platform is structured.

Pricing Comparison#

This is often the first decision factor, and it is more nuanced than the headline numbers suggest.

Vapi Pricing#

• $0.05/min platform fee
• Plus: STT provider cost (Deepgram ~$0.005/min, OpenAI Whisper ~$0.006/min)
• Plus: TTS provider cost (ElevenLabs ~$0.03-0.05/min, OpenAI TTS ~$0.015/min)
• Plus: LLM cost (GPT-4o mini ~$0.01-0.02/min, Claude 3.5 Haiku ~$0.01-0.02/min)
• Plus: Telephony cost (Twilio ~$0.008-0.015/min)

Typical all-in with standard providers: $0.09-0.13/min

Retell AI Pricing#

• $0.07/min covering: platform + telephony + STT + TTS
• Plus: LLM cost (same as Vapi — your API key, your provider, your bill)

Typical all-in with standard providers: $0.08-0.12/min

Cost Comparison at Scale#

The difference is marginal at most scales. The real cost advantage of Vapi appears at high volume when teams actively optimize provider selection — using self-hosted Llama for LLM, Cartesia for TTS (often cheaper than ElevenLabs at scale), and Deepgram's volume pricing.

Bottom line: Total call costs are similar at most scales. Retell AI's billing is simpler (one per-minute rate + LLM); Vapi's billing is more complex but more optimizable.

Telephony Setup#

This is where the practical onboarding experience differs most.

Vapi Telephony#

Vapi integrates with Twilio, Vonage, and SIP. Most teams use Twilio:

1. Create a Twilio account
2. Purchase a phone number in Twilio
3. Configure the Twilio number to forward calls to Vapi's webhook endpoint
4. Outbound calls are initiated via Vapi API, which uses your Twilio credentials

If you already have Twilio set up, this is straightforward. If you do not, setting up Twilio for the first time — verifying identity, understanding Twilio's pricing, configuring webhooks — adds 2-4 hours of setup time for a first-time user.

Vapi also offers its own telephony (direct phone number provisioning) without Twilio, though this is a newer feature and the Twilio integration remains more commonly documented.

Retell AI Telephony#

Retell AI includes telephony in the platform. You purchase a phone number directly in the Retell AI dashboard. There is no Twilio account required.

For outbound calls, you specify the from-number and to-number in the API call; Retell AI handles the telephony. The entire setup — from creating an account to making a phone call — can be done in under an hour with no third-party accounts.

Winner: Retell AI wins on telephony simplicity. Vapi wins on telephony flexibility (SIP support, bring-your-own-Twilio, multi-provider choice).

LLM Flexibility#

Both platforms are genuinely LLM-agnostic, but with slightly different implementation.

Vapi LLM Support#

Vapi supports native integrations with:

• OpenAI (GPT-4o, GPT-4o mini, o1 models)
• Anthropic (all Claude 3.x and Claude 4.x models)
• Google (Gemini Pro, Gemini Flash, Gemini Ultra)
• Groq (Llama models with fast inference)
• Together AI, Fireworks AI (open-source model hosting)
• Custom OpenAI-compatible endpoints

Vapi's LLM configuration supports advanced parameters including temperature, max tokens, and custom stop sequences. The "Squads" feature lets you route to different LLMs based on conversation context — useful for using a cheap model for simple queries and an expensive model for complex reasoning.

Retell AI LLM Support#

Retell AI supports:

• OpenAI (GPT-4o, GPT-4o mini)
• Anthropic (Claude 3.5 Sonnet, Claude 3 Haiku)
• Google (Gemini Pro, Gemini Flash)
• Meta Llama (via compatible endpoints)
• Custom OpenAI-compatible endpoints

The supported model list is slightly smaller than Vapi's native integrations, but custom endpoint support means any compatible model works.

Winner: Vapi has a slight edge on LLM flexibility, particularly for exotic model providers and the multi-LLM routing via Squads. For standard LLM use cases, both are equivalent.

Latency Performance#

Retell AI publishes a sub-800ms latency claim and has invested in edge infrastructure to support it. Vapi's latency varies more based on provider selection — choosing ElevenLabs TTS (slower) vs. Cartesia TTS (faster) can change total latency by 200-400ms.

In practice, both platforms achieve conversation-quality latency with well-chosen providers. The difference is that Retell AI's default configuration is optimized for latency, while Vapi's configuration requires deliberate provider choice to achieve the same result.

Winner: Retell AI for default latency; Vapi can match or beat it with optimized provider configuration.

Batch Calling#

Vapi Batch Calling#

Vapi does not have a dedicated batch calling API. You initiate outbound calls one at a time via the POST /call endpoint. For batch campaigns, you build your own queuing system: iterate your contact list, fire API calls, handle rate limiting, implement retry logic.

This is adequate for engineering teams who build the infrastructure anyway, but it is additional work compared to a native batch API.

Retell AI Batch Calling#

Retell AI has a native batch calling API. A single API call submits a list of contacts with per-contact custom data. Retell AI handles:

• Concurrency management (no carrier-rate concerns)
• Retry logic for unanswered calls
• Progress tracking per batch
• Webhook delivery for each call completion

For outbound campaign use cases, Retell AI's batch API saves significant engineering effort.

Winner: Retell AI wins clearly on batch calling. This may be the deciding factor for teams with significant outbound calling requirements.

SDK Quality#

Both SDKs are functional and well-maintained. Vapi's SDK has a larger ecosystem of community examples, blog posts, and tutorials — a byproduct of its earlier launch and larger developer community. Retell AI's SDK is cleaner in some respects, reflecting lessons from Vapi's design.

Winner: Vapi for ecosystem depth; Retell AI for API ergonomics.

Multi-Agent Features#

Vapi: Supports "Squads" — multi-agent configurations where a primary agent can transfer a conversation to a secondary agent mid-call. The receiving agent gets full conversation context. This supports complex routing scenarios: a greeter agent, a qualification agent, a specialist agent, each with different LLMs and voices.

Retell AI: Does not have a native multi-agent feature equivalent to Squads. Complex routing requires building custom logic using function calls to determine when to transfer and managing the transfer.

Winner: Vapi for multi-agent and complex routing requirements.

Use Case Recommendations#

Choose Vapi When:#

• You need multi-agent routing or complex call flow logic
• You want to bring an existing Twilio number
• Long-term LLM cost optimization via provider switching matters
• Your team has engineering bandwidth for more complex configuration
• You need SIP trunking for enterprise telephony integration
• You want the largest community ecosystem for answers to edge case questions

Choose Retell AI When:#

• You want the fastest path from zero to a working phone agent
• You run high-volume outbound campaigns and need the native batch API
• You prefer all-inclusive telephony (no Twilio account management)
• Simpler per-minute pricing with predictable cost structure matters
• Sub-800ms latency is a published, documented SLA to point to

Both Are Good For:#

• Customer service voice agents with function calling
• Sales qualification calling at moderate volume
• Developer-built voice products
• LLM-agnostic deployments using any major provider

For use cases beyond just the platform choice, see Voice AI Agents for Customer Service and Voice AI Agents for Sales.

Verdict#

Vapi and Retell AI are both excellent platforms for developer-built voice agents. The choice comes down to a few specific requirements:

Pick Retell AI if you value simpler onboarding, built-in batch calling, and all-inclusive telephony. It is the better starting point for most projects.

Pick Vapi if you need multi-agent routing, maximum provider flexibility, SIP integration, or you have an existing Twilio relationship. It is the better choice for complex architectures.

If you are evaluating broader platform options including enterprise-focused tools, see Voice AI Agent Platforms Compared 2026.

For context on how voice AI fits into broader agentic workflows and human-in-the-loop patterns, see those glossary entries. For the economics of building voice AI products, see AI Agent Build vs Buy.

Related Resources#

• Vapi Platform Profile
• Retell AI Platform Profile
• Voice AI Agent Platforms Compared 2026
• Voice AI Agents for Customer Service
• What is a Voice AI Agent?
• Best Enterprise AI Agent Solutions