TL;DR

Gnani AI and ElevenLabs both train their own speech models, which already separates them from most of the voice AI market. The difference sits in what each stack was optimised for and how far the deployment envelope stretches for a regulated enterprise in India.

ElevenLabs is a voice model company that has extended into agents. Its synthesis quality is the reference point most buyers measure against, Scribe v2 covers 90 or more languages, it publishes a per-minute rate card, and its telephony integrations reach the CCaaS platforms large contact centres run.

Gnani AI is a full-stack enterprise voice AI platform where recognition, synthesis, native speech-to-speech and reasoning are all first-party and trained on Indian telephony audio, and where voice biometrics, live agent assist and conversation analytics sit inside the same contract as the agents. Gnani Artha extends that into a sovereign posture, with open-weight Indic reasoning models running inside the customer's own data centre.

If your evaluation is weighted toward global language coverage and published pricing, ElevenLabs is the more direct route. If it is weighted toward Indic accuracy on noisy telephony, code-mixed speech and on-premise inference of the reasoning layer, Gnani AI carries more of it.

Want to see how these platforms compare on enterprise grade performance more broadly? Refer to our master comparison page.

Our Methodology for Comparing Gnani AI and ElevenLabs

We scored each platform on five criteria that map to what an enterprise buyer has to de-risk before signing. The weighting favours model ownership and deployment flexibility, which most evaluations underweight until the platform carries production traffic and a security review lands on the desk.

Gnani AI vs ElevenLabs: What Are You Actually Buying?

ElevenLabs sells voice models and, on top of them, an agents platform documented as ElevenAgents. Each agent is assembled from a fine-tuned recognition model, a language model of your choosing, text-to-speech and a proprietary turn-taking model. Synthesis runs from Eleven v3 across 70 or more languages to Eleven Flash v2.5 at approximately 75 ms, with 5,000 or more voices across 31 languages in the agents documentation, and Scribe v2 covers recognition across 90 or more languages. Telephony reaches SIP trunks, Twilio, Vonage, Telnyx, Plivo, Exotel, Genesys and Five9, alongside web, mobile, WhatsApp and Teams.

Gnani AI is a full-stack voice AI platform built on first-party models: Gnani Prisma v2.5 for recognition, Gnani Timbre v2.5 for synthesis, Gnani Warp v2.0 as a 5 billion parameter native speech-to-speech model, and Gnani Evon for reasoning and tool calling. Above them sit four products: Gnani Agents for voice, WhatsApp, chat and SMS; Analytics for conversation intelligence and automated QA; Biometrics for caller authentication; and Assist for real-time guidance on live calls. Gnani AI publishes 30 million or more daily voice interactions across 200 or more enterprise deployments.

We also launched Gnani Artha, the sovereign layer of that stack, packaging Evon v3.3, a 30 billion parameter Indic reasoning model with 3.5 billion active parameters per token under Apache 2.0, with Evon v2.0, Prisma v2.5, Timbre v2.5 and an orchestration platform called Plexus, designed to run inside the customer's own data centre or VPC.

At a Glance

Feature-by-Feature Comparison

Who trains the models

Gnani AI trains all of them. Prisma v2.5 learned from more than 14 million hours of real phone calls and expects 8 kHz audio with clipping and street noise. Timbre v2.5 speaks 10 Indian languages, several accents within each. Warp v2.0 goes straight from speech to speech. Evon reasons, tuned for banking, insurance, healthcare and telecom, and its open-weight sibling Evon v3.3 sits inside Gnani Artha.

ElevenLabs trains the two layers it is known for, Scribe v2 for listening and the Eleven v3 and Flash families for speaking. For reasoning it hands you a menu: Qwen models it hosts itself, or Google, OpenAI and Anthropic, or your own endpoint. Pick an outside provider and that provider decides when the model beneath you changes.

Verdict: Gnani AI owns more of the chain, and Evon v3.3 comes with open weights under Apache 2.0. ElevenLabs owns its speech models outright and lets you choose or bring the reasoning model, which many architects prefer.

Indian languages and mixed speech

Gnani AI lists 10+ Indian languages for listening and 40 or more at the model level. It reports under 4% word error rate on Indian speech and 96% accuracy when a speaker mixes languages, and it places first in 8 of 9 Indian languages on Kathbath Noisy 8 kHz, the closest public test to a real Indian phone call. It also lifts out PAN numbers, Aadhaar IDs, policy numbers and account numbers while it listens, so nothing downstream has to guess them.

ElevenLabs reads far more of the world and grades each language in public. Kannada and Malayalam sit at 5% word error rate or better. Hindi, Tamil, Telugu, Bengali, Marathi, Gujarati and Odia fall between 5% and 10%. Punjabi falls between 10% and 20%. You set one main language per agent, add the rest in the settings, and a detection tool moves the agent between them when it hears a change. That serves a caller who speaks Tamil throughout. It does less for a borrower who begins a sentence in Hindi and ends it in English, which is the case Gnani AI trains for.

Verdict: ElevenLabs reads more languages and shows its marks, which makes checking easy. Gnani AI goes deeper on Indian phone audio and on speech that switches language inside one sentence.

Speed and scale

ElevenLabs prints its timings: about 75 ms for Flash v2.5, 280 ms for Eleven v3 Conversational, 150 ms for Scribe v2 Realtime. None of those count your network or your own code. Its enterprise page claims 5,000,000 hours of conversation a month, which is a real production figure.

Gnani AI prints timings too. Prisma v2.5 streams under 200 ms. Timbre v2.5 starts speaking within 100 ms. Warp v2.0 answers within 200 ms at P95, and by skipping the text step it avoids paying three delays in a row, which Gnani AI says cuts GPU cost by 60%. Gnani Agents answers within 800 ms on live calls, and Prisma v2.5 carries 35,000 or more streams at once. ElevenLabs sets calls at once by plan, reaching 40 on Business, with more negotiated at Enterprise.

Verdict: ElevenLabs speaks faster. Gnani AI holds far more calls at once and skips a step that costs both time and GPUs. Neither figure includes your carrier, so measure both on your own lines.

Deployment and compliance

ElevenLabs holds SOC 2 Type II, ISO 27001, HIPAA, PCI DSS L1 and GDPR. Its zero retention mode, in the words of its HIPAA page, keeps patient data out of every log and store, and it strips personal fields before writing anything down. It signs Business Associate Agreements on Enterprise only. It runs walled-off storage in the EU, India and Singapore for Enterprise customers, though its own docs warn that the models on offer differ by region. Its private deployment docs say ElevenAgents runs whole agent workflows in your own cloud account or on your own hardware, with speaking and listening also available as separate endpoints on SageMaker or Vertex AI. It announced on-premise and on-device in April 2026 and still calls both early access.

Gnani AI holds SOC 2 Type 2, ISO 27001, HIPAA, GDPR and PCI-DSS, and ships cloud, private cloud, on-premise and air-gapped today, reasoning model included. Artha exists for this case. Evon v3.3 fires 3.5 billion parameters per token, so it fits on one server rather than a cluster, and where that server sits answers DPDP, RBI and IRDAI. Send call audio to an outside model API and that traffic joins your data inventory, and your auditor will want to discuss it.

Verdict: Gnani AI ships more ways to run it today, air-gapped reasoning above all. ElevenLabs holds more certificates, PCI DSS Level 1 among them, and already runs full agent workflows inside a customer's own cloud account.

What else comes in the box

Gnani AI ships four products on one platform. Agents speaks over voice, WhatsApp, chat and SMS. Biometrics names the caller in under five seconds from more than 300 features of the voice, catches cloned and synthetic voices, and can enrol someone in Hindi then check them in Tamil. Assist feeds a human agent answers while the call runs. Analytics reads every call rather than a sample. Plexus fires agents on triggers and keeps the audit trail.

ElevenLabs spreads wider in another direction: a large voice library, voice cloning, dubbing, a music model, Studio, SDKs for web, mobile and server, and links into Genesys and Five9. It publishes no voice biometrics and no live agent assist, so a bank that reads balances aloud must buy authentication elsewhere and hand its call audio across one more boundary.

Verdict: Gnani AI covers more of a contact centre. ElevenLabs covers more of a studio, and drops into the CCaaS platforms you may already run.

Price and time to the first call

ElevenLabs prints its prices. Agents run at $0.080 a minute, $0.160 in burst, and plans climb to Business at $990 a month for 12,375 minutes and 40 calls at once. Two bills sit outside that. ElevenLabs prices the language model "based on usage and varies by model" and passes telephony through "at cost". Add all three before you compare anything.

Gnani AI quotes each deployment through sales. That hurts a buyer who wants to model unit economics in week one, and it is worth saying plainly. Because the products arrive together, ask for the price of the whole job, authentication and quality checks included, rather than the minute rate alone.

Verdict: ElevenLabs wins on a price you can read today. Gnani AI wins on how much one contract covers.

Gnani AI vs ElevenLabs: Overall Score Breakdown

Where Both Get Tested: Real-World Enterprise Scenarios

Scenario 1: A private bank chasing overdue accounts

The job: 500,000 overdue accounts a month. Borrowers speak Hindi, Marathi, Tamil and Bengali and drop into English mid-sentence. The audio is customer PII under DPDP, the RBI is watching, and the security team wants the thinking done inside the bank.

ElevenLabs suits you if: the India storage region, zero retention and an approved model satisfy your reviewers, and one language per call is enough.

Gnani AI suits you if: four or more languages mix heavily, the reasoning model must sit in the bank's own racks, and a caller must prove who they are before hearing a balance. Artha runs on one node for exactly this.

Scenario 2: An insurer calling about renewals

The job: 700,000 welcome and renewal calls a month in eight or more Indian languages. Policyholders outside the metros expect an agent who sounds local, and the IRDAI governs what you say and how you record consent.

ElevenLabs suits you if: the voice itself carries the brand, the calls are routine, and consent arrives by SMS link.

Gnani AI suits you if: you need regional accents, want consent captured by voice on the same call, and need quality checks on every call for the audit file.

Scenario 3: A hospital group calling patients

The job: 400,000 reminders and follow-ups a month. Before anyone hears a lab result, the caller must prove who they are. HIPAA and DPDP both apply, and the system must sit inside the hospital.

ElevenLabs suits you if: the calls are reminders and non-clinical follow-ups, with an OTP doing the identity check. It signs BAAs on Enterprise and offers zero retention with redaction.

Gnani AI suits you if: calls read out results or prescriptions, where voice biometrics decide whether compliance signs off, and where air-gapped means this quarter.

Scenario 4: A D2C brand in festive season

The job: two million or more contacts in a peak month about tracking, returns and refunds. Traffic runs eight times normal, margins are thin, and callers ring in from noisy streets in smaller cities.

ElevenLabs suits you if: a predictable minute rate is the thing keeping you awake, or you want one vendor for support calls and marketing audio.

Gnani AI suits you if: peak volume is the thing keeping you awake, or accuracy falls apart on poor lines.

Scenario 5: A telecom operator opening self-service

The job: billing, plan changes and outage calls, tens of millions of them. TRAI DLT rules govern outbound messages, and the sums only work at rupee ARPU.

ElevenLabs suits you if: you scope it to a few call types where the voice wins the call, with volume and data location written into an Enterprise contract.

Gnani AI suits you if: you are replacing the whole IVR estate, need speech to speech to hold down both delay and GPU spend, or the network team insists on on-premise.

How to Choose Between Gnani AI and ElevenLabs

Final Verdict

ElevenLabs sets the mark for how a machine should sound. It trains its own listening and speaking models, reads 90 or more languages, shows its accuracy scores, prints a price you can plan against on day one, and holds SOC 2 Type II, ISO 27001, HIPAA, PCI DSS L1 and GDPR with an India storage region. If you sell in many countries, or the voice itself wins the deal, buy it.

On our marks, which give ownership and deployment half the total, Gnani AI takes 86 to ElevenLabs at 79. It earns that by training every model in the chain, by learning from real phone lines rather than clean studio audio, by handling mixed speech inside one model, by shipping on-premise and air-gapped today, and by folding biometrics, agent assist and analytics into one contract. Artha pushes it further, putting open-weight Indic models inside your own walls so data stays home by design.

The fair counter is that Gnani AI hides its price, which forces you into a sales call sooner than you may want, and that ElevenLabs reads more languages than any India-first vendor. Either way, run both against your own recordings, in your own languages, before you sign.

Frequently Asked Questions

What are ElevenLabs Agents, and what are they built from?

ElevenAgents is the agent layer above the ElevenLabs voice models. The docs give each agent four parts: a tuned speech to text model, a language model you choose, a fast text to speech model, and a turn-taking model that decides when to speak. They count 5,000 or more voices in 31 languages. For reasoning you pick from Qwen models ElevenLabs hosts, from Google, OpenAI or Anthropic, or from your own endpoint.

What are the best ElevenLabs alternatives for enterprise voice agents in India?

It depends on what is stopping you. For an Indian contact centre it is usually accuracy on 8 kHz phone audio, what happens when a caller mixes languages inside a sentence, and whether the reasoning model can sit on your own hardware. Gnani AI answers all three, with Prisma v2.5 trained on more than 14 million hours of real phone calls and Artha running open-weight Indic models on a single node.

How much does ElevenLabs cost per minute for voice agents?

The published card reads $0.080 a minute, $0.160 in burst, with plans up to Business at $990 a month for 12,375 minutes and 40 calls at once. Two costs sit outside it. ElevenLabs prices the language model "based on usage and varies by model" and bills telephony "at cost". Your real minute is the sum of all three.

How secure is voice data uploaded to ElevenLabs?

ElevenLabs holds SOC 2 Type II, ISO 27001, HIPAA, PCI DSS L1 and GDPR. Its zero retention mode, in the words of its HIPAA page, keeps patient data out of every log and store, and it strips personal fields before writing anything down. It signs Business Associate Agreements on Enterprise only. The separate question for an Indian buyer is who else touches the audio on its way through.

Where does my call data sit if I run ElevenLabs from India?

ElevenLabs offers walled-off storage in the EU, India and Singapore, which its docs call an Enterprise feature, so an India region is yours on the right contract. One warning appears in the same docs: the models on offer differ by region, so the model you test may not be the model you get. Artha answers this another way, running the inference on your own hardware so DPDP, RBI and IRDAI follow the machine.

Which languages can I use with ElevenLabs agents?

You set one main language per agent, add the rest in the agent settings, and a detection tool moves between them during the call. Scribe v2 reads 90 or more languages, with published marks putting Kannada and Malayalam at 5% word error rate or better and Punjabi between 10% and 20%. What that design does not chase is a caller who begins in Hindi and finishes in English inside one sentence.

Can ElevenLabs replace a contact centre IVR?

It can carry the agent layer, with SIP trunk, Twilio, Exotel, Genesys and Five9 links, and private running where whole agent workflows sit in your own cloud account or hardware. Replacing an IVR usually also means proving who the caller is during the call and checking quality on all of it, which Gnani AI ships itself.