Why Shunya is a stronger choice than Smallest.ai for production text-to-speech (TTS)

Smallest.ai has built its TTS platform around one clear idea: make high-quality speech fast enough for real-time applications.

Its current Lightning TTS stack supports streaming over HTTP, SSE, and WebSocket, 44.1 kHz audio, voice cloning, pronunciation dictionaries, speed control, and multilingual generation. Its latest Lightning v3.1 Pro model is positioned for premium conversational TTS with a curated voice catalogue and ~200ms time to first byte.

That makes Smallest.ai particularly relevant for voice agents, conversational AI, IVR, and other latency-sensitive applications.

Shunya approaches the problem from a broader production perspective.

Shunya Zero TTS is designed around global, Indian, and Asian voice applications, with 46 documented voices across global, asian and 23 Indic languages, 11 expression styles, cross-lingual synthesis, voice cloning, streaming, and enterprise deployment options.

The difference is therefore not simply about which platform is faster.

It is about the complete system:

How natural does the voice sound?

Does it pronounce real business text correctly?

Can the same voice work across languages?

How well does it perform inside a voice agent?

Can it support telephony and enterprise deployment?

And perhaps most importantly:

Can it scale from TTS into a broader voice-AI stack?

Shunya and Smallest.ai at a glance

Capability	Shunya Zero TTS	Smallest.ai Lightning
Language focus	Global, Indian and Asian languages	Global multilingual
Indic specialization	Dedicated Indic TTS	Indic + global support
Documented voice catalogue	46 voices	217 voices on standard pool
Indic voice catalogue	23 Indic languages + English	Multiple Indic voices
Expression	11 styles	Natural, expressive synthesis
Voice cloning	Yes	Yes
Cross-lingual voice identity	Yes	Yes / cross-language routing
Pronunciation controls	Yes	Pronunciation dictionaries + number controls
Streaming	HTTP / WebSocket	HTTP / SSE / WebSocket
Real-time focus	Yes	Core strength
Telephony formats	μ-law, A-law and more	μ-law, A-law and more
On-device options	Yes	Available models/options
Enterprise deployment	Cloud, private, on-premise, edge	Cloud, on-premise, on-device options
Broader voice stack	STT + TTS + voice agents + SLMs	STT + TTS + managed agent stack

Smallest.ai’s current documentation distinguishes its standard Lightning v3.1 catalogue from Lightning v3.1 Pro, so voice count and language availability depend on the selected model.

Voice quality is more than a low-latency demo

Smallest.ai’s Lightning models are designed for natural prosody, intonation, conversational rhythm, and real-time generation. Its current Pro model is positioned as a premium 44.1 kHz voice pool for conversational applications.

That is exactly what a modern TTS system needs.

But production voice quality has another dimension.

The voice must remain natural when the text becomes difficult.

Consider:

“Your payment of ₹1,25,499.50 has been received.”

Or:

“Your reference number is AB-2047-X.”

Or:

“Your appointment is scheduled for 12 October at 10:30 AM.”

A system can sound excellent on ordinary sentences and still struggle with numbers, acronyms, names, codes, currencies, and technical terminology.

For enterprise TTS, correctness and naturalness have to be evaluated together.

Pronunciation is one of the most important production features

This is an area where Smallest.ai has invested heavily.

Lightning supports custom pronunciation dictionaries for brand names, technical terms, and acronyms. Its newer releases also introduced a dedicated number_pronunciation_language control so developers can determine how numbers, currency, times, and numeric date components are spoken independently of the synthesis voice.

That’s useful for enterprise applications.

Shunya also supports pronunciation controls and language-aware TTS, with its API exposing pronunciation dictionaries, speed controls, multiple output formats, and explicit language selection.

The practical test is simple.

Give both systems:

₹25,499.50

AB-2047-X

12 October 2026

500 mg

support@example.com

Then listen.

The system that sounds more human in a generic demo isn’t necessarily the one that produces the better production voice.

Shunya’s Indic voice specialization is a different proposition

Smallest.ai has significantly expanded its multilingual TTS offering.

Its Lightning v3.1 Pro documentation currently lists 10 Indic languages, 8 Asian and Middle Eastern languages, and 13 European languages in the Pro voice catalogue.

The standard Lightning catalogue also contains a growing set of Indian voices.

Shunya’s approach is more explicitly Indic-first.

The Zero TTS Indic platform is built around Indian languages, dialects, regional pronunciation, and native phonetics, while the main TTS product extends into broader global and Asian voice applications.

Its documented voice catalogue provides dedicated male and female voices across 23 Indic languages plus English.

That makes the distinction less about:

“Who supports Indian languages?”

and more about:

“How deeply is Indian speech integrated into the product?”

For an Indian customer-service application, that can matter when the text contains:

Local names

Indian places

Regional terms

English words inside Indian-language sentences

Indian currency

Regional pronunciation

This is one of the areas where Shunya has a clear specialization.

One brand voice across multiple languages

A multilingual enterprise often wants more than translation.

It wants identity.

Imagine the same assistant speaking:

English

then Hindi

then Tamil

then Japanese.

If the voice changes dramatically each time, the customer experiences what feels like a different assistant.

Shunya’s TTS architecture separates voice identity from language, allowing a voice to speak across supported languages. Its documentation explicitly states that the voice parameter controls vocal character while the text determines the language.

That is useful for:

Global brands

Multilingual customer support

Banking assistants

Telecom

Healthcare

Government services

Smallest.ai also supports cross-language routing and says an English or Hindi voice can be used across supported languages when using its cross-language behavior.

So both support multilingual voice identity.

The important benchmark is how consistent the same voice sounds across languages, especially between English and regional languages.

Voice libraries: Smallest.ai offers more catalogue breadth

Smallest.ai currently exposes a 217-voice standard Lightning catalogue, with a separate curated Pro pool.

That gives developers a substantial amount of choice.

Shunya’s documented catalogue is smaller, at 46 voices across 23 Indic languages plus English.

But this is another example of different product priorities.

Smallest.ai gives developers a broader voice catalogue within its model pools.

Shunya puts more emphasis on purpose-built production voices across Indian and multilingual enterprise use cases.

For applications where you need dozens of voices with different personalities, Smallest.ai’s catalogue can be useful.

For a brand looking for a small set of consistent voices across languages, the catalogue size may matter less than voice quality and cross-lingual consistency.

Voice cloning is becoming standard

Both platforms support voice cloning.

Smallest.ai’s current Lightning documentation says its voice cloning workflow can clone a voice from 5–15 seconds of audio on the standard pool, and its latest updates also added cloning support to the Pro pool.

Shunya also supports cloning from a short reference recording and can use cloned voices across its supported language stack.

So the useful question isn’t:

“Does it clone voices?”

Both do.

Test:

Clone similarity

Voice consistency

Cross-lingual consistency

Long-form stability

Latency

Performance after telephony compression

For enterprise applications, the ability to use the same cloned identity across multiple languages can be more valuable than cloning alone.

Voice cloning should always be used with the appropriate consent and rights from the voice owner.

Latency is one of Smallest.ai’s strongest advantages

Smallest.ai makes latency a central part of the Lightning architecture.

Its current Lightning v3.1 Pro model publishes approximately 200ms TTFB, with 3.3× real-time factor, and supports HTTP, SSE, and WebSocket streaming.

Its WebSocket API is particularly useful when text arrives incrementally from an LLM.

The platform can buffer fragments through a context_id, join them at natural sentence boundaries, and preserve prosody across chunks rather than treating every LLM fragment as a new TTS request.

That is an important real-time capability.

Shunya also provides WebSocket streaming TTS, designed for conversational and voice-agent workloads.

The correct production benchmark is therefore not:

“Which vendor has the lowest published latency?”

It is:

LLM generates text → TTS starts audio → user hears the response

Measure time to first audible speech under the same network, chunking, and model conditions.

Streaming architecture matters when the LLM streams tokens

A voice agent often doesn’t wait for the LLM to produce an entire paragraph.

It receives text incrementally.

For example:

“Sure, I can help you with…”

followed a moment later by:

“…your account.”

The TTS engine needs to handle those fragments naturally.

Smallest.ai’s latest WebSocket implementation specifically supports this pattern through context_id, buffering fragments and carrying prosody across successive chunks.

That’s a strong developer-focused feature.

Shunya’s WebSocket streaming architecture also supports real-time audio generation for conversational pipelines.

This is an important benchmark for any LLM-powered voice agent.

Don’t test a 500-word paragraph.

Test streaming LLM output token by token.

Telephony is a separate TTS challenge

Enterprise voice often runs through:

IVRs

Contact centers

Outbound calling

Phone-based assistants

Studio-quality audio is only part of the problem.

You need to consider:

8 kHz audio

μ-law

A-law

Buffering

Streaming

Speech clarity after compression

Smallest.ai supports PCM, MP3, WAV, μ-law, and A-law, with sample rates including 8 kHz through 44.1 kHz.

Shunya also supports μ-law and A-law, along with PCM, WAV, MP3, OGG Opus, and FLAC.

So both are suitable for telephony workflows.

The meaningful test is how the voices behave after they enter an actual phone call.

TTS becomes more useful when it connects to the rest of the voice stack

Smallest.ai has expanded beyond TTS as well.

Its current platform includes Lightning TTS, Pulse STT, and Hydra speech-to-speech, while its product messaging targets real-time voice agents and conversational applications.

This gives developers a path toward building more of the voice stack from one provider.

Shunya has taken a similar broader-platform direction.

The Shunya Platform combines:

STT

TTS

Voice agents

Custom SLMs

Edge speech understanding

Knowledge-grounded AI

That means TTS can be integrated directly into a larger speech-to-intelligence-to-voice workflow.

For an enterprise building a complete conversational system, that broader architecture can be more important than standalone TTS performance.

On-device and private deployment

Smallest.ai’s documentation and current product materials describe on-device and on-premise options, depending on the model and deployment.

Shunya also provides private, on-premise, and edge-oriented deployment, including an on-device model family.

This matters for applications where audio cannot always be sent to a public cloud:

Healthcare

Financial services

Government

Industrial systems

Retail devices

Edge assistants

The relevant evaluation should include:

Data residency

Network dependency

Hardware requirements

Inference location

Latency

Offline behavior

A TTS provider may look identical on a public API and completely different once the requirement becomes local inference.

Pricing: compare cost at your actual volume

Smallest.ai’s current model pricing page lists Lightning TTS at $0.175 per 10,000 characters, with its current model selection centered on Lightning v3.1 and v3.1 Pro.

The current Pro model documentation lists approximately $0.195 per 10,000 characters on its standard plan.

This difference illustrates why buyers should check the exact model and pricing tier being used rather than relying on a single headline number.

Shunya provides model and enterprise pricing through its pricing page.

For a production TTS evaluation, calculate:

Cost per million characters

Cost per generated minute

Cost at peak concurrency

Cost of cloned voices

Cost of required model tier

Cost of downstream voice infrastructure

The cheapest characters are not necessarily the cheapest voice system.

Where Smallest.ai is the better choice

Smallest.ai is particularly compelling when you prioritize:

Real-time TTS

Lightning is built specifically around low-latency streaming.

Streaming LLM output

Its context_id functionality is designed to preserve prosody across incremental text fragments.

Pronunciation control

Custom dictionaries and dedicated number-pronunciation controls are strong developer features.

Voice catalogue breadth

The standard Lightning catalogue currently contains 217 voices.

Voice-agent infrastructure

Smallest.ai’s broader platform now includes STT, TTS, and speech-to-speech capabilities.

For teams primarily building real-time conversational voice products, Smallest.ai is a strong option.

Where Shunya makes a stronger case

Shunya is particularly compelling when:

Indian voice applications are central

Zero TTS Indic is specifically designed around Indian speech, pronunciation, regional variation, and Indic voices.

You need a consistent voice across languages

Shunya supports cross-lingual voice synthesis, separating voice identity from the language being spoken.

Global, Indian, and Asian coverage are all important

Shunya’s TTS positioning spans these markets rather than focusing only on one language family.

You need enterprise-oriented expression

Shunya provides 11 explicit voice styles, including Conversational, News, Narrative, Happy, Sad, and Enthusiastic.

Telephony is important

Shunya supports μ-law and A-law, making it suitable for phone-based applications.

TTS is part of a larger voice-AI architecture

The Shunya Platform connects STT, TTS, voice agents, SLMs, edge AI, and knowledge-grounded systems.

You need edge-oriented inference

Shunya On-Device Models provide a path for local voice processing.

Shunya vs Smallest.ai by use case

Use case	Stronger fit
Real-time voice agents	Both
Ultra-low-latency streaming	Smallest.ai
Streaming LLM output	Smallest.ai
Pronunciation dictionaries	Both
Number pronunciation controls	Smallest.ai
Creative narration	Both
Indian-language TTS	Shunya
Indian multilingual voice agents	Shunya
Cross-lingual enterprise voice identity	Shunya
Global multilingual voice agents	Both
Telephony TTS	Both
Voice cloning	Both
Large voice catalogue	Smallest.ai
Enterprise voice applications	Both
Edge voice applications	Shunya / Both
Broader STT + TTS + voice-AI stack	Both

How to benchmark Shunya and Smallest.ai

The best TTS comparison uses the same text, comparable voices, the same language, and the same environment.

Build a test set containing:

Conversation

Questions

Numbers

Currency

Dates

Names

Acronyms

URLs

Product codes

Indian-language text

Code-switched text

Then measure:

Naturalness

Does the voice sound human over short and long responses?

Pronunciation

Does it pronounce names, numbers, brands, and terminology correctly?

Prosody

Does emphasis and pacing sound natural?

Expression

Can the voice communicate the required tone?

Voice consistency

Does the voice maintain the same identity across repeated generations and languages?

Time to first audio

How quickly does the user actually hear the response?

Streaming stability

Does the voice remain natural when text arrives in small LLM-generated fragments?

Telephony quality

How does it sound after 8 kHz audio encoding?

Cost

What is the actual cost at your production volume?

For a voice agent, add one more metric:

Time from user finishing speech to first audible response.

That’s the number users actually experience.

Final words

Smallest.ai has built a strong TTS platform around real-time performance.

Its Lightning models provide streaming, voice cloning, pronunciation dictionaries, number-pronunciation controls, expressive speech, 44.1 kHz audio, and a growing voice catalogue, with Lightning v3.1 Pro currently positioned around ~200ms TTFB.

For teams building real-time voice agents and conversational applications, those are meaningful capabilities.

Shunya’s approach is broader.

Its TTS stack is designed around global, Indian, and Asian voice applications, with Indic-focused voices, cross-lingual synthesis, voice cloning, expressive styles, streaming, telephony formats, and enterprise deployment.

The strongest Shunya differentiator is therefore not simply speed.

It is the combination of:

Indian voice specialization

Cross-lingual voice identity

Production-oriented expression

Telephony support

Voice cloning

and integration with a broader STT + TTS + voice-agent + SLM stack.

Smallest.ai may be the stronger choice when the priority is extremely fast conversational TTS and streaming-first voice-agent development.

Shunya becomes particularly compelling when the application needs Indian and Asian voices, multilingual brand identity, enterprise telephony, and a broader voice-AI architecture.

The best way to decide is simple:

Take the text your application actually generates and make both systems speak it.

Test the names.

Test the numbers.

Test the currency.

Test the Indian languages.

Test the code-switching.

Test streaming LLM output.

Then measure first-audio latency, pronunciation, naturalness, voice consistency, telephony quality, and cost.

Because the best TTS system isn’t the one with the most impressive demo.

It’s the one that continues to sound right when your application goes into production.

Contact us to know more.

Frequently asked questions

Is Shunya better than Smallest.ai?

It depends on the workload. Smallest.ai is particularly strong for low-latency conversational TTS and streaming voice agents. Shunya is particularly strong for Indian-language TTS, cross-lingual voice identity, enterprise voice applications, telephony, and broader voice-AI infrastructure.

Which has more voices?

Smallest.ai’s current standard Lightning catalogue contains 217 voices, while Shunya’s documentation lists 46 voices across 23 Indic languages plus English.

Does Smallest.ai support Indian languages?

Yes. Its current Lightning models support multiple Indic languages, including Hindi, Tamil, Kannada, Marathi, Telugu, Malayalam, Gujarati, Punjabi, Bengali, and Odia.

Does Shunya support Indian languages?

Yes. Shunya’s Zero TTS Indic offering is specifically designed around Indian languages, regional pronunciation, and Indic speech patterns.

Does Smallest.ai support voice cloning?

Yes. Lightning supports voice cloning from approximately 5–15 seconds of audio, and current documentation also confirms cloning support on the Pro pool.

Does Shunya support voice cloning?

Yes. Shunya supports voice cloning and cross-lingual synthesis through its TTS platform.

Which has lower latency?

Smallest.ai currently publishes approximately 200ms TTFB for Lightning v3.1 Pro. Shunya also provides low-latency WebSocket streaming. Because vendors measure latency differently, production buyers should compare time to first audible response under identical conditions.

Does Smallest.ai support pronunciation dictionaries?

Yes. Lightning provides custom pronunciation dictionaries for specialized vocabulary, brand names, and domain terminology.

Does Smallest.ai support number pronunciation?

Yes. Lightning v3.1 introduced a dedicated control for numbers, currency amounts, times, and numeric date components.

Does Shunya support telephony?

Yes. Shunya supports μ-law and A-law alongside other common audio formats.

Which is better for real-time voice agents?

Both are strong options. Smallest.ai has a particularly strong streaming-first architecture, while Shunya combines TTS with STT, speech intelligence, voice agents, and other voice-AI components.

Which is better for Indian enterprise voice applications?

Shunya has a particularly strong proposition where the application depends on Indic voices, regional pronunciation, cross-lingual voice identity, telephony, and enterprise voice agents.

Which is cheaper?

Smallest.ai currently lists Lightning TTS from $0.175 per 10,000 characters, while its Pro model documentation lists approximately $0.195 per 10,000 characters. Shunya uses its own model and enterprise pricing structure.

The fairest comparison depends on your actual volume, model, concurrency, and required features.

How should I compare Shunya and Smallest.ai?

Use the same production text and compare naturalness, pronunciation, expression, voice consistency, first-audio latency, streaming behavior, telephony quality, and cost at your expected scale.