Deepgram vs ElevenLabs 2026: Which Fits Your Use Case?

Deepgram is the closer fit for developer teams building custom voice products with bring-your-own telephony; ElevenLabs is the closer fit for teams wanting bundled telephony and a conversational agent platform with minimal code. The main dividing line is build model: Deepgram provides speech-to-text, text-to-speech and voice agent APIs that integrate into your stack, while ElevenLabs offers a conversational AI platform with hosted agents and managed phone numbers, according to Deepgram's documentation and ElevenLabs' platform overview.

This comparison covers build model, telephony, model and voice choice, integrations, compliance and pricing. Prices were checked November 2026 from each vendor's published pages.

What is the difference between Deepgram and ElevenLabs?

Deepgram is a speech-to-text, text-to-speech and voice agent API provider; ElevenLabs is a voice AI platform offering conversational agents, speech models and voice cloning. The core difference is architectural: Deepgram provides components you integrate into your application with your own telephony and orchestration, while ElevenLabs provides a hosted conversational agent platform with bundled phone numbers and turn-taking logic.

What is Deepgram?

Deepgram is a speech AI platform offering automatic speech recognition (ASR), text-to-speech (TTS) and voice agent APIs. The platform is designed for developers building voice features into applications, with models optimized for accuracy, speed and cost. Deepgram's Nova-2 speech-to-text model supports 36 languages and delivers transcription via API, according to Deepgram's documentation.

What is ElevenLabs?

ElevenLabs is a voice AI platform offering text-to-speech, voice cloning and conversational AI agents. The platform provides a hosted agent builder with managed telephony, language model integration and real-time voice synthesis. ElevenLabs' conversational AI product handles inbound and outbound calls with turn-taking, interruption handling and function calling, according to ElevenLabs' conversational AI documentation.

Deepgram vs ElevenLabs side by side

Pricing and features checked November 2026. Sources linked in the sections below.

Which is easier to build with?

ElevenLabs is easier for teams without engineering resources because its conversational AI builder is a hosted platform with a visual interface, managed telephony and built-in turn-taking logic. Deepgram requires API integration, your own telephony provider and custom orchestration code to handle call flow, interruptions and language model coordination.

Deepgram provides REST and WebSocket APIs for speech-to-text and text-to-speech, with SDKs for Python, JavaScript, Go and other languages, according to Deepgram's API documentation. You send audio to the speech-to-text endpoint, receive a transcript, pass it to your language model, and send the response to the text-to-speech endpoint. Call control, turn-taking and telephony integration are your responsibility. This model fits teams with developers who want full control over the voice experience and already have or plan to build telephony infrastructure.

ElevenLabs' conversational AI platform handles orchestration for you. You configure the agent's prompt, choose a language model (GPT-4o, Claude, Gemini), select a voice, and deploy to a phone number ElevenLabs provisions, according to ElevenLabs' conversational AI documentation. The platform manages turn-taking, interruption detection and function calling. No code is required for basic agents, though custom logic can be added via webhooks. This model fits operations teams, agencies and small businesses that want a working agent quickly without building telephony or orchestration layers.

The trade-off is flexibility. Deepgram's API approach lets you swap models, customize latency-accuracy trade-offs and integrate voice into any application. ElevenLabs' hosted platform is faster to deploy but locks you into its agent runtime and telephony stack.

Which handles telephony and call quality controls better?

Deepgram offers more telephony flexibility because it works with any SIP provider or carrier you choose, while ElevenLabs bundles phone numbers in 30+ countries with its conversational AI platform. Neither vendor operates a carrier network; Deepgram expects you to bring telephony, and ElevenLabs provides managed numbers through third-party providers, according to ElevenLabs' pricing page.

Deepgram's speech-to-text API accepts audio from any source: SIP trunks, WebRTC streams, Twilio, Vonage or your own carrier. You control codec selection, sampling rate and audio preprocessing. The Nova-2 model supports telephony-grade audio (8 kHz) and broadcast-quality audio (16 kHz and higher), according to Deepgram's audio format documentation. Deepgram states Nova-2 achieves a word error rate of 7.8% on earnings calls and 10.3% on general conversation benchmarks, according to Deepgram's model card, though these are vendor-published figures.

ElevenLabs provides phone numbers in over 30 countries through its conversational AI platform, according to ElevenLabs' pricing page. The platform handles SIP connectivity, audio encoding and number provisioning. Call quality controls are not detailed in public documentation; the platform manages codec and latency optimization internally. ElevenLabs states its conversational AI achieves sub-1-second response times, according to ElevenLabs' conversational AI overview, though this is a vendor claim and measurement conditions are not specified.

For teams with existing telephony contracts or regulatory requirements to use specific carriers, Deepgram is the only option. For teams that want phone numbers provisioned and managed as part of the platform, ElevenLabs is simpler.

Which offers more model and voice choice?

ElevenLabs offers more voice choice with over 3,000 voices in its library and custom voice cloning, while Deepgram offers more language model flexibility because you bring your own LLM. For text-to-speech, ElevenLabs provides a larger voice catalog. For conversational agents, Deepgram lets you use any language model, while ElevenLabs supports GPT-4o, GPT-4, Claude and Gemini.

Deepgram's Aura text-to-speech model offers 12 voices across multiple languages, according to Deepgram's TTS documentation. Custom voice cloning is available but requires contacting Deepgram's team. For language models in voice agent workflows, Deepgram does not provide an LLM; you integrate OpenAI, Anthropic, Google or any other provider via API. This gives you access to the latest models (GPT-4o, Claude 3.5 Sonnet, Gemini 1.5 Pro) and lets you swap models without changing your voice infrastructure.

ElevenLabs provides access to over 3,000 voices in its voice library, including pre-made voices and community-contributed options, according to ElevenLabs' voice library page. Custom voice cloning is available on paid plans. For conversational agents, ElevenLabs supports GPT-4o, GPT-4, Claude and Gemini as language model options, according to ElevenLabs' conversational AI documentation. You select the model in the agent builder; ElevenLabs manages the API connection and token usage.

Deepgram supports 36 languages for speech-to-text, according to Deepgram's language documentation. ElevenLabs supports 32 languages for conversational AI, according to ElevenLabs' language support page.

If voice variety is the priority, ElevenLabs is the stronger fit. If language model choice and the ability to use models outside the platform's supported list matter, Deepgram is the stronger fit.

Which integrates better with your CRM and tools?

Deepgram integrates via API, so CRM and calendar connections depend on your implementation; ElevenLabs does not document native CRM integrations in its public materials, so similar custom work is likely required for both platforms. Neither vendor offers pre-built Salesforce, HubSpot or Google Calendar connectors in the way no-code platforms do.

Deepgram provides REST and WebSocket APIs that return transcripts and accept text for synthesis. Integrating with a CRM means writing code to send call transcripts to Salesforce, log outcomes in HubSpot or update records in your database. Deepgram does not provide middleware or Zapier-style connectors. This model fits teams with developers who already integrate APIs into their stack.

ElevenLabs' conversational AI platform supports function calling, which lets the agent trigger external actions during a call (booking an appointment, looking up a record, creating a ticket), according to ElevenLabs' conversational AI documentation. You define functions as webhooks; the agent calls them when the conversation requires it. This is not a pre-built integration but a mechanism for custom logic. Public documentation does not list native CRM or calendar integrations.

For teams that need out-of-the-box CRM sync, neither platform is the right choice; no-code builders like Synthflow or Vapi's workflow tools are stronger fits. For teams comfortable building API integrations, Deepgram and ElevenLabs are comparable, with ElevenLabs offering function calling as a built-in orchestration feature.

Which is stronger on security and compliance?

Deepgram is stronger on healthcare compliance because it offers HIPAA Business Associate Agreement (BAA) availability, while ElevenLabs lists SOC 2 Type II certification but does not document HIPAA BAA availability in public materials. Both vendors publish SOC 2 Type II reports.

Deepgram lists SOC 2 Type II certification and HIPAA BAA availability on its security page. The HIPAA BAA is available to customers who require it for handling protected health information (PHI). Deepgram also states it is GDPR-compliant and offers data residency options, according to the same page. These are vendor-published statements; compliance with TCPA, recording-consent laws and other regulations depends on how you deploy the platform. Legal review is recommended.

ElevenLabs lists SOC 2 Type II certification on its security documentation page. HIPAA BAA availability is not mentioned in public materials. ElevenLabs states it is GDPR-compliant and offers data processing agreements, according to the same page. As with Deepgram, compliance with call-recording and telemarketing laws depends on your implementation and jurisdiction.

For healthcare, legal services and other industries requiring HIPAA coverage, Deepgram is the documented fit. For general business use where SOC 2 Type II is sufficient, both platforms are comparable.

Which costs less?

Which platform costs less depends on call volume, average call length and whether you already have telephony infrastructure. Deepgram charges per minute for speech-to-text and per character for text-to-speech, with no platform fee. ElevenLabs charges $0.11 per minute for conversational AI usage, which includes the language model, speech recognition and synthesis in one rate.

Deepgram pricing

Deepgram charges $0.0043 per minute for speech-to-text using the Nova-2 model and $0.015 per character for text-to-speech using the Aura model, as of November 2026, according to Deepgram's pricing page. There is no monthly platform fee, no minimum spend and no charge for phone numbers because Deepgram does not provide telephony. You pay your carrier or SIP provider separately.

To estimate total cost, add Deepgram's per-minute and per-character charges, your language model API cost (e.g., OpenAI charges $0.005 per 1,000 input tokens and $0.015 per 1,000 output tokens for GPT-4o as of November 2026, according to OpenAI's pricing page), and your telephony provider's per-minute rate. A 5-minute call generating 500 characters of TTS output would cost approximately $0.0215 for Deepgram's speech services ($0.0043 × 5 minutes for STT + $0.015 × 500 characters ÷ 1,000 for TTS), plus LLM and carrier costs.

ElevenLabs pricing

ElevenLabs charges $0.11 per minute for conversational AI agent usage as of November 2026, according to ElevenLabs' pricing page. This rate includes the language model, speech-to-text, text-to-speech and turn-taking orchestration, based on the platform's all-in agent model. Phone numbers are included in the platform; the pricing page does not specify a separate per-number fee.

A 5-minute call would cost $0.55 in agent usage ($0.11 × 5 minutes). ElevenLabs does not break out the cost of the language model, STT or TTS separately, so you cannot optimize by choosing a cheaper LLM or reducing synthesis usage.

Cost comparison

For low-volume use (under 1,000 minutes per month), ElevenLabs is simpler to budget because the per-minute rate is all-in. For high-volume use or teams with existing telephony contracts, Deepgram is likely cheaper because you can optimize each component and avoid paying for bundled telephony you do not need.

An illustrative scenario: 10,000 minutes per month, 3-minute average call length, 400 characters of TTS per call.

These are illustrative assumptions, not quotes. Actual cost depends on your carrier rate, LLM token usage and TTS character count. The formula is:

Deepgram monthly cost ≈ (minutes × STT rate) + (characters × TTS rate) + LLM API cost + carrier cost

ElevenLabs monthly cost ≈ minutes × $0.11

For teams with engineers and existing telephony, Deepgram offers more control and potentially lower cost. For teams that want predictable per-minute pricing with no separate carrier bill, ElevenLabs is simpler.

What do users say about Deepgram and ElevenLabs?

Deepgram user feedback

Public review volume for Deepgram is limited; the recurring themes below are drawn from the available G2 coverage. Reviewers highlight transcription accuracy and speed as strengths, particularly for telephony-grade audio. One G2 reviewer notes the platform's performance on low-quality audio, though specific accuracy figures are not provided in the review.

Integration complexity is a documented theme. Deepgram requires API integration and custom orchestration, which fits teams with developers but adds setup time compared to hosted platforms. Pricing transparency is cited as a positive; the per-minute and per-character model is straightforward to estimate.

ElevenLabs user feedback

ElevenLabs is rated 4.6 out of 5 on G2 from over 100 reviews as of November 2026, according to ElevenLabs' G2 profile. Reviewers frequently mention voice quality and the size of the voice library as strengths. Ease of use is a recurring theme, particularly for the text-to-speech and voice cloning features. Reviewers note the platform is accessible to non-developers.

Conversational AI is newer and has less review coverage than the TTS product; early feedback highlights the speed of deployment and the quality of turn-taking, though some reviewers report occasional latency or interruption-handling issues. Pricing is mentioned as a consideration for high-volume use. The per-minute rate for conversational AI is higher than component-based platforms, which some reviewers note as a trade-off for the bundled, managed experience.

Which use cases fit each?

When should you choose Deepgram?

You have engineering resources and want full control over the voice stack, including language model choice, telephony provider and call orchestration. You already have a SIP trunk, carrier contract or telephony infrastructure and want to add speech-to-text and text-to-speech without changing providers. You need HIPAA BAA coverage for handling protected health information in voice workflows. You want to optimize cost by selecting cheaper language models, reducing TTS usage or negotiating carrier rates independently.

When should you choose ElevenLabs?

You want a hosted conversational agent platform with managed phone numbers, turn-taking and function calling, and you do not have engineering resources to build orchestration. You need a large voice library (3,000+ voices) or custom voice cloning for brand consistency across agents. You prefer predictable per-minute pricing that includes the language model, speech recognition and synthesis in one rate. You are deploying standalone agents (reception, booking, qualification) and want to go live quickly without writing telephony integration code.

How do you decide between Deepgram and ElevenLabs?

What is your team's technical capacity?

If you have developers who can integrate APIs, manage telephony and orchestrate call flow, Deepgram offers more flexibility and potentially lower cost. If you do not have engineering resources or want to deploy agents without writing code, ElevenLabs' hosted platform is the faster path to production.

Do you need bundled telephony or bring-your-own carrier flexibility?

If you already have a carrier contract, regulatory requirements to use a specific provider, or enterprise telephony infrastructure, Deepgram is the only fit because it works with any SIP provider. If you want phone numbers provisioned and managed as part of the platform, ElevenLabs is simpler.

What volume and call patterns do you expect?

For low to moderate volume (under 5,000 minutes per month), ElevenLabs' all-in per-minute rate is easier to budget and may be cost-competitive. For high volume or long calls, Deepgram's component pricing lets you optimize by choosing cheaper language models and reducing synthesis usage, which can lower total cost significantly.

What compliance certifications does your use case require?

If you need HIPAA BAA coverage, Deepgram is the documented option. If SOC 2 Type II is sufficient, both platforms are comparable. For TCPA, recording-consent laws and other telemarketing regulations, compliance depends on how you deploy the platform; legal review is recommended for both.

If you have developers, existing telephony and need HIPAA or cost optimization, choose Deepgram. If you want a hosted agent platform with managed infrastructure and no code, choose ElevenLabs. If you need both flexibility and ease of use, evaluate whether your team can handle Deepgram's integration work or whether ElevenLabs' trade-offs (higher per-minute cost, less LLM choice) are acceptable.

Frequently asked questions

Is Deepgram cheaper than ElevenLabs?

Deepgram is typically cheaper for high-volume use or teams with existing telephony because its per-minute speech-to-text rate ($0.0043) and per-character text-to-speech rate ($0.015) are lower than ElevenLabs' all-in conversational AI rate ($0.11 per minute), according to each vendor's pricing page as of November 2026. Deepgram requires you to pay separately for a language model and telephony provider, so total cost depends on your carrier rate and LLM usage. For low-volume use or teams that want bundled infrastructure, ElevenLabs' predictable per-minute pricing may be simpler to budget.

Which is easier for non-developers?

ElevenLabs is easier for non-developers because its conversational AI platform is a hosted builder with a visual interface, managed phone numbers and built-in turn-taking, according to ElevenLabs' conversational AI documentation. Deepgram requires API integration, custom orchestration code and your own telephony provider, which fits teams with engineering resources but is not accessible to operations teams without developers.

Can I bring my own phone numbers to Deepgram or ElevenLabs?

Deepgram requires you to bring your own phone numbers because it does not provide telephony; you connect Deepgram's speech-to-text and text-to-speech APIs to your SIP provider or carrier. ElevenLabs provides managed phone numbers in 30+ countries as part of its conversational AI platform, according to ElevenLabs' pricing page; bring-your-own-carrier options are not documented in public materials.

Are Deepgram and ElevenLabs HIPAA compliant?

Deepgram offers HIPAA Business Associate Agreement (BAA) availability, according to Deepgram's security page, which is required for handling protected health information in the United States. ElevenLabs lists SOC 2 Type II certification but does not document HIPAA BAA availability in public materials, according to ElevenLabs' security documentation. Compliance with HIPAA, TCPA and recording-consent laws depends on how you deploy the platform; legal review is recommended.

Which handles outbound calling campaigns?

ElevenLabs handles outbound calling campaigns through its conversational AI platform, which manages dialing, call pacing and agent orchestration, according to ElevenLabs' conversational AI documentation. Deepgram supports outbound calling via your telephony provider; you manage dialing and campaign logic through your carrier or SIP platform. If you have existing outbound infrastructure, Deepgram integrates into it; if you want managed outbound campaigns, ElevenLabs is simpler.

Can I migrate from Deepgram to ElevenLabs?

Migrating from Deepgram to ElevenLabs requires rebuilding your voice workflow because the platforms have different architectures. Deepgram is an API you integrate into your application; ElevenLabs is a hosted agent platform. You would need to move your call logic from your custom orchestration code into ElevenLabs' agent builder, reconfigure your language model prompts and switch from your telephony provider to ElevenLabs' managed phone numbers. Transcripts, call logs and custom integrations do not transfer automatically.

Which offers better voice quality?

Voice quality depends on the text-to-speech model and use case. ElevenLabs is known for natural-sounding voices and offers over 3,000 voices with custom cloning, according to ElevenLabs' voice library page, which reviewers frequently cite as a strength. Deepgram's Aura text-to-speech model offers 12 voices optimized for speed and cost, according to Deepgram's TTS documentation. Both vendors publish voice samples on their websites; testing with your specific use case is recommended.

Do Deepgram and ElevenLabs support real-time interruptions?

ElevenLabs supports real-time interruptions and turn-taking as a built-in feature of its conversational AI platform, according to ElevenLabs' conversational AI documentation. Deepgram provides the speech-to-text and text-to-speech components, but interruption handling and turn-taking logic must be implemented by your team using Deepgram's streaming APIs and your orchestration code. For teams without developers, ElevenLabs is the simpler option; for teams that want custom interruption behavior, Deepgram offers more control.

Last updated: November 2026.