Retell AI vs Vapi vs Shunya: Which Voice Agent Platform Is Best?

Building an AI voice agent is no longer just about connecting a speech-to-text model to an LLM and adding a synthetic voice.

A production voice agent needs to listen, understand, respond, interrupt naturally, remember context, call APIs, handle errors, and complete tasks in real time.

That makes the choice of voice AI platform important.

Three platforms worth comparing are Retell AI, Vapi, and Shunya. All three can be used to build conversational voice applications, but they approach the problem differently.

Retell AI focuses on providing a managed infrastructure for building and deploying conversational voice agents. Vapi takes a developer-first approach, giving teams control over the components used in the voice pipeline. Shunya combines voice-agent infrastructure with its own speech AI stack, including speech recognition, text-to-speech, multilingual capabilities, and deployment options designed for enterprise environments.

So which is best?

Let’s compare them.

Retell AI vs Vapi vs Shunya: Quick Comparison

Feature	Retell AI	Vapi	Shunya
AI voice agents	✓	✓	✓
Inbound & outbound calling	✓	✓	✓
Streaming speech recognition	✓	✓	✓
Text-to-speech	✓	✓	✓
LLM integration	✓	✓	✓
Custom workflows	✓	✓	✓
API access	✓	✓	✓
Multilingual voice agents	✓	✓	✓
Indic language focus	Limited	Depends on model/provider	Strong
Code-switching	Depends on ASR	Depends on ASR	Native code-switching models
Custom speech models	Limited	Provider-dependent	✓
On-premise deployment	Limited	Deployment-dependent	Enterprise deployment options
Enterprise customization	✓	✓	✓
Voice AI + speech model stack	Platform	Orchestration layer	Integrated speech AI stack

The most important difference isn’t simply the number of features.

It’s how much of the voice stack you control.

What Is a Voice Agent Platform?

A voice agent platform provides the infrastructure needed to build an AI system that can have a spoken conversation with a user.

A typical architecture looks like this:

User speaks → STT → LLM → TTS → User hears response

A production platform adds several other components:

• Voice activity detection
• Turn detection
• Interruption handling
• Conversation memory
• Tool and API calling
• Call routing
• Telephony integration
• Monitoring
• Analytics
• Authentication
• Human handoff

The platform you choose determines how much of this infrastructure you build yourself.

1. Retell AI

Retell AI positions itself as an infrastructure platform for building production-ready AI phone agents.

Its focus is largely on making voice agents easier to deploy without requiring teams to build the entire real-time conversational pipeline themselves.

Retell provides components for creating agents, handling conversations, integrating telephony and connecting external systems.

Where Retell stands out

Retell is particularly attractive when the goal is to get a voice agent into production quickly.

Its managed approach reduces the amount of infrastructure a development team needs to build and maintain.

Retell AI	Assessment
Ease of development	Strong
Voice agent infrastructure	Strong
Telephony	Strong
Developer experience	Strong
Customization	Strong
Multilingual support	Strong
Indic specialization	Limited compared with Indic-first platforms
Enterprise deployment flexibility	Depends on requirements

For companies primarily looking for a managed voice-agent layer, Retell is a strong option.

2. Vapi

Vapi takes a more developer-oriented approach.

Instead of tightly coupling the entire voice stack to one provider, Vapi allows developers to configure different components of their voice pipeline.

That flexibility can be useful when a company already has preferred models or wants to experiment with different providers.

A typical Vapi implementation can combine:

Speech recognition + LLM + voice + tools + telephony

This makes it more of an orchestration layer than a single-model speech platform.

Vapi	Assessment
Developer flexibility	Very strong
API access	Strong
Model choice	Very strong
Voice agent infrastructure	Strong
Integrations	Strong
Multilingual support	Provider-dependent
Indic specialization	Provider-dependent
Custom speech models	Depends on integration
Enterprise deployment	Depends on architecture

The advantage is flexibility.

The trade-off is that your team’s overall experience can depend on the individual STT, LLM and TTS providers you connect to Vapi.

3. Shunya

Shunya takes a different approach.

Instead of treating speech recognition and voice generation as interchangeable components underneath a voice-agent platform, Shunya develops the speech models themselves alongside its voice-agent infrastructure.

This matters when speech quality is a core part of the application.

Shunya’s platform includes speech-to-text, text-to-speech, voice intelligence and voice-agent capabilities, with support for 216+ languages across its broader speech AI platform.

For Indian enterprise applications, Shunya also has specialized Indic and code-switching models.

For example, its Zero STT Codeswitch model is designed for mixed-language speech such as:

Mujhe payment ka status check karna hai, can you help?

That makes the platform particularly relevant for multilingual Indian voice agents.

Shunya	Assessment
Voice agents	Strong
Speech-to-text	Strong
Text-to-speech	Strong
Indic languages	Strong
Code-switching	Strong
Custom speech models	Strong
Multilingual deployment	Strong
Enterprise deployment	Strong
On-premise options	Available for enterprise requirements
Speech AI control	High

The biggest distinction is that Shunya isn’t only an agent orchestration layer.

It also controls the underlying speech technology.

Retell AI vs Vapi vs Shunya: Speech Capabilities

For voice agents, the speech layer can have a bigger impact on the user experience than the LLM itself.

If the system mishears the customer, the LLM receives the wrong information.

If the voice sounds unnatural or takes too long to respond, the conversation feels artificial.

Speech capability	Retell AI	Vapi	Shunya
Speech-to-text	✓	✓	✓
Text-to-speech	✓	✓	✓
Streaming ASR	✓	✓	✓
Multilingual ASR	✓	✓	216+ languages across platform
Indic ASR specialization	Limited	Provider-dependent	✓
Code-switching	Provider/model dependent	Provider/model dependent	✓
Custom ASR models	Limited	Provider-dependent	✓
Custom TTS	Limited	Provider-dependent	✓

This is one of the biggest reasons to evaluate the underlying speech models, not just the agent builder.

Retell vs Vapi vs Shunya for Indian Languages

This is where the comparison becomes particularly interesting.

India isn’t a single-language voice market.

A customer might speak Hindi, English and regional vocabulary within the same call.

For example:

Sir mera loan application submit ho gaya hai but document verification abhi pending hai.

A voice agent needs to recognize all of that accurately.

Requirement	Retell AI	Vapi	Shunya
Hindi	✓	✓	✓
English	✓	✓	✓
Indian languages	✓	Provider-dependent	55+ Indic languages
Hinglish	Model-dependent	Model-dependent	Dedicated code-switching support
Regional accents	Provider-dependent	Provider-dependent	Indic-focused models
Mixed-language speech	Provider-dependent	Provider-dependent	Native code-switching
Indian enterprise focus	General	General	Strong

For a global voice agent that happens to support Hindi, the first two platforms may be sufficient.

For an agent designed specifically around Indian multilingual conversations, speech model specialization becomes much more important.

Latency: Why It Matters for Voice Agents

Voice conversations are different from chat.

A user expects a response almost immediately after they stop speaking.

If the system takes two or three seconds to respond after every sentence, the conversation feels broken.

Latency comes from multiple stages:

Audio → STT → LLM → TTS → audio playback

Reducing just one component doesn’t necessarily solve the problem.

Latency factor	Why it matters
STT time-to-first-token	Determines how quickly the system understands speech
LLM response time	Determines how quickly the agent decides what to say
TTS time-to-first-audio	Determines how quickly the response starts
Network latency	Adds delay between components
Turn detection	Determines when the system decides the user has finished
Interrupt handling	Determines how naturally users can interrupt

Shunya’s speech infrastructure is designed for real-time applications, with its ASR benchmarks reporting below 500 ms streaming partials.

Which Platform Gives You More Control?

This is one of the clearest differences.

Control area	Retell AI	Vapi	Shunya
Choose LLM	✓	✓	✓
Choose voice	✓	✓	✓
Configure workflow	✓	✓	✓
Change speech provider	Limited/provider dependent	✓	✓
Custom speech model	Limited	Provider-dependent	✓
Fine-tune speech for domain	Limited	Provider-dependent	✓
Deployment customization	Moderate	Moderate/High	High
Enterprise-specific model customization	Limited	Depends on provider	✓

Vapi is particularly attractive when component-level flexibility is your priority.

Shunya becomes more attractive when you want control over the speech models themselves, rather than simply choosing between third-party providers.

Which Platform Is Best for Enterprises?

Enterprise voice AI introduces requirements that aren’t always obvious in a demo.

A production deployment may need:

• Data security
• Compliance
• Private deployment
• Custom integrations
• Domain-specific vocabulary
• Multiple languages
• Monitoring
• Human escalation
• High call volumes
• Custom SLAs
• Model customization

Enterprise requirement	Retell AI	Vapi	Shunya
Production voice agents	✓	✓	✓
Enterprise integrations	✓	✓	✓
Multilingual support	✓	✓	✓
Indic language specialization	Limited	Provider-dependent	✓
Custom models	Limited	Provider-dependent	✓
Private deployment requirements	Depends	Depends	✓
On-premise use cases	Limited/requirement dependent	Architecture dependent	Supported for enterprise deployments
Domain-specific speech	Limited	Provider-dependent	✓
Enterprise customization	✓	✓	✓

For a startup building a simple outbound calling agent, all three can be viable.

For a large BFSI, healthcare, telecom or contact-centre deployment, deployment architecture and speech model control become much more important.

Retell AI vs Vapi vs Shunya: Which Should You Choose?

The answer depends on what you’re optimizing for.

Choose Retell AI if:

You want a managed voice-agent platform and your priority is getting conversational phone agents into production without building the entire infrastructure layer yourself.

Choose Vapi if:

You are a developer-heavy team that wants flexibility over the underlying STT, LLM and TTS components and wants to experiment with different providers.

Choose Shunya if:

You need a full speech AI stack with voice agents, especially for multilingual and Indian-language applications where code-switching, speech accuracy, deployment control and custom models matter.

Final Comparison

	Retell AI	Vapi	Shunya
Best for	Managed voice agents	Developer flexibility	Enterprise speech + voice agents
Core strength	Agent infrastructure	Orchestration & customization	Speech AI + agents
Developer control	High	Very high	High
Indian languages	Good	Provider-dependent	Strong
Code-switching	Provider-dependent	Provider-dependent	Native support
Custom speech models	Limited	Provider-dependent	Yes
Enterprise deployment	Strong	Strong	Strong
Best fit for Indian enterprise	Good	Good	Excellent

The Bottom Line

Retell AI, Vapi and Shunya are not identical products.

Retell AI is a strong choice when you want managed voice-agent infrastructure.

Vapi is a strong choice when you want to assemble and control your own voice stack.

Shunya is particularly differentiated when speech itself is a strategic requirement, especially for multilingual and Indian enterprise applications.

The choice ultimately comes down to one question:

Do you just need to build a voice agent, or do you need to control the speech intelligence behind it?

For simple voice-agent deployments, an orchestration platform may be enough.

For applications where language accuracy, code-switching, latency, custom speech models, and enterprise deployment determine whether the agent actually works, the underlying speech stack becomes just as important as the agent framework.

And that’s where the platform choice starts to matter.