TL;DR: ElevenLabs built its reputation on text-to-speech, but its STT layer shows clear production gaps: no native code-switching, limited multi-channel support capped at five channels and one hour of audio, and extra charges for keyterm prompting and entity detection. For purpose-built speech-to-text, the decision breaks down by use case. Deepgram leads on sub-300ms real-time streaming for voice agents. AssemblyAI handles English-first enterprise diarization on async workflows. Gladia delivers the strongest async multilingual accuracy with native code-switching across 100+ languages, all-inclusive per-hour pricing on Starter and Growth plans, and a no-retraining data policy on paid tiers by default.
The biggest hidden cost in voice AI is not compute. It is the add-on fees that stack up once your audio pipeline hits scale and you discover that diarization, language detection, and entity extraction each carry their own line item at most vendors. Most engineering teams hit this ceiling after choosing ElevenLabs for its strong TTS capabilities, only to find the STT layer cannot handle production audio: accented speech, mid-conversation language switches, and high-concurrency batch workloads.
This guide compares Gladia, Deepgram, and AssemblyAI on the metrics that matter in production: WER on noisy and accented audio, real-time versus async trade-offs, diarization quality, and total cost of ownership (TCO) at 1,000 and 10,000 hours per month. ElevenLabs has an appropriate role in certain workflows, and we cover that too.
ElevenLabs STT limitations in production
ElevenLabs STT works for straightforward, low-complexity audio. The problems start when you push it into production environments with multi-speaker audio, non-English content, or high concurrency.
The ElevenLabs API documentation reveals several hard constraints worth understanding before committing to it as your STT infrastructure layer. The API transcribes files over 8 minutes by splitting audio into four concurrent segments, which introduces seam artifacts and inconsistent speaker attribution across chunk boundaries. Multi-channel mode caps at 5 channels and 1 hour of audio. Language detection happens at call start only, with no mid-conversation code-switching capability, which breaks entirely for bilingual speaker pairs. ElevenLabs prices keyterm prompting and entity detection as add-ons, not bundled in the base rate.
For teams building meeting assistants or CCaaS platforms, these constraints are not edge cases. They are the daily reality of production audio.
ElevenLabs STT: Ideal use cases
ElevenLabs STT fits one specific scenario: integrated TTS-STT loops where keeping both modalities under a single vendor simplifies development. Prototypes, simple internal voice bots, and low-volume applications where transcription accuracy is not a downstream dependency all fit this profile.
The moment transcription feeds into a CRM entry, a coaching scorecard, or an LLM pipeline, the accuracy ceiling of a bundled STT feature becomes a liability for everything downstream.
High-performance STT use cases
Engineering leads evaluating production STT infrastructure have a short list of non-negotiable requirements:
- WER on noisy, accented, and multilingual audio - clean benchmark data rarely survives contact with real production audio
- REST and WebSocket API support with published latency SLAs
- Speaker diarization with a disclosed DER - not just a checkbox feature
- SOC 2 Type II and GDPR compliance with a clear data residency policy
- Predictable TCO at 10x current volume - with all features enabled, not just the base transcription rate
- Async concurrency at scale - hundreds of parallel jobs without pre-provisioning
Benchmarking ElevenLabs STT alternatives
Vendor benchmarks run on studio-quality audio with a single native speaker tell you almost nothing about what will happen in production. Real engineering decisions require datasets with conversational speech, multiple speakers, background noise, and non-standard accents. Before committing to any provider, run your actual audio samples through the API and measure WER on your specific distribution: the accented speakers, the noisy call center recordings, the bilingual meeting that switches between English and French mid-conversation.
Noisy & accented speech tests
Our async benchmark evaluates Solaria-1 against 8 providers across 7 datasets and 74+ hours of audio, with open-sourced methodology you can reproduce on your own data. According to Gladia's published benchmark, Solaria-1 achieves up to 29% lower WER and up to 3x lower DER compared to alternatives on conversational speech, methodology is open and reproducible for independent verification.
Gladia's Solaria-1 model handles the conditions where bundled STT features fail: accented speech, overlapping speakers, and mid-conversation language switches across 100+ supported languages.
"Superior accuracy on accented speech compared to competitors... Clean API, easy to integrate and deploy to production." - Yassine R. on G2
STT vendor cost analysis
Base rates are rarely what you pay. The table below shows where add-on pricing compounds for diarization-enabled workloads specifically, using current published rates:
| Feature |
Gladia (Starter/Growth) |
Deepgram Nova-3 |
AssemblyAI |
| Base transcription |
$0.61–$0.20/hr |
~$0.55/hr |
$0.21/hr (async, Universal-3 Pro) |
| Diarization |
Included |
Add-on ($0.0020/min, pre-recorded and streaming) |
$0.02/hr extra |
| Translation |
Included |
Not native |
Separate |
| Sentiment analysis |
Included |
Not included |
Separate |
| Named entity recognition |
Included |
Not included |
Separate |
All pricing sourced from Gladia pricing, Deepgram published rates, and AssemblyAI published rates.
Best for low-latency live transcription: Deepgram
Deepgram built its product for real-time applications, and Nova-3 delivers consistent sub-300ms streaming performance for voice agents and live captioning workflows. If your primary constraint is raw streaming latency, Deepgram is the most focused option in this comparison.
Real-time STT performance benchmarks
Deepgram's Nova-3 achieves sub-300ms streaming latency through WebSocket connections, which makes it a strong choice for voice agent pipelines where the STT output feeds directly into an LLM response loop. Nova-3 supports 36+ languages and includes real-time code-switching across 10 languages (English, Spanish, French, German, Hindi, Russian, Portuguese, Japanese, Italian, and Dutch), making it a more capable multilingual option than its initial release suggested. For a migration from Deepgram to Gladia, the WebSocket and REST surface maps closely.
Streaming vs. batch STT accuracy trade-offs
Real-time transcription processes audio incrementally, producing output before the model has seen the full context of an utterance. Batch (async) transcription processes the complete recording, enabling better accuracy, more consistent speaker attribution, and stronger multilingual handling.
A contact center platform running post-call analysis does not need sub-300ms latency, but it does need accurate diarization, named entity recognition, and sentiment scores. Choosing Deepgram for that use case sacrifices accuracy for a latency budget you never needed to spend. For the meeting assistant and CCaaS use cases where most teams outgrow ElevenLabs STT, async processing is the correct architecture. The async architecture guide covers a complete implementation walkthrough.
Predictable costs for real-time STT
Deepgram's Nova-3 Multilingual base rate is $0.0092/min on pay-as-you-go, translating to approximately $0.55/hr. Diarization is an add-on at $0.0020/min on both pre-recorded and streaming audio. Deepgram's listed rates assume opt-in to their model improvement program; customers who opt out using the mip_opt_out=true parameter may encounter different pricing terms, which affects cost modeling for regulated use cases.
Gladia: Precision multilingual STT for global apps
Gladia is an async-first audio infrastructure provider. One API call covers the full pipeline: record, transcribe, and return structured, LLM-ready data including diarization, translation, sentiment analysis, named entity recognition, and summaries, all at the base per-hour rate on Starter and Growth plans.
Solaria-1, Gladia's current production model, achieves an average of 29% lower WER than all other providers and covers 100+ languages, 42 of which no other API-level STT provider supports. Multilingual support is the primary reason cited in sales wins and developer migrations.
Gladia's multilingual WER breakdown
Our async benchmark measures Solaria-1 against 8 providers on 7 datasets and 74+ hours of audio with open and reproducible methodology. According to Gladia's published benchmark, Solaria-1 achieves on average 29% lower WER and up to 3x lower DER than alternatives on conversational speech, methodology is open and reproducible for independent verification.
In production, Claap, a video messaging and meeting recording platform, reached 1-3% WER with Gladia and transcribes one hour of video in under 60 seconds. A fintech customer processing high-volume calls achieves 98.5% numerical accuracy across 800 concurrent sessions. These numbers come from production environments with real-world audio, not controlled test sets.
The 42 unique languages Solaria-1 covers and competitors do not include Bengali, Punjabi, Tamil, Urdu, Persian, Marathi, Hebrew, Pashto, Kazakh, Georgian, Mongolian, Haitian Creole, Maori, Javanese, and Malagasy, among others. For CCaaS platforms serving Southeast Asia, South Asia, or Latin America, this coverage difference is a direct product capability gap against competitors.
Handling diverse accents & dialects
Solaria-1 handles true mid-conversation code-switching, meaning when a speaker moves from English to French or from Spanish to English mid-sentence, the model stays with them without breaking the transcript or requiring a new session. This works in both async and real-time modes across all 100+ supported languages.
No hidden fees or tier surprises
Gladia's pricing model bundles diarization, translation, sentiment analysis (text-based), named entity recognition, summarization, and custom vocabulary into the base per-hour rate on Starter and Growth plans.
- Starter: $0.61/hr async, $0.75/hr real-time, 10 hours free monthly. Customer data is used for model training by default on this tier.
- Growth: As low as $0.20/hr async, as low as $0.25/hr real-time. Customer data is never used for model training and no opt-out action is required.
- Enterprise: Custom, with debundled pricing, custom models, and zero data retention options.
AssemblyAI: Handling multi-speaker audio
AssemblyAI's Universal-3 Pro model handles 99 languages for async transcription at $0.21/hr, with strong English diarization and a built-in LLM integration layer (LeMUR) for post-processing workflows. For enterprise teams who need English-first accuracy in async mode, it is a solid option.
AssemblyAI built LeMUR as an application-layer product that competes with the meeting assistants and conversation intelligence platforms that also use AssemblyAI's API. If you are building a product in that category, you are evaluating infrastructure from a provider who now competes at the application layer. For a migration from AssemblyAI to Gladia, the WebSocket and REST surface maps closely.
Production diarization: Real-world metrics
AssemblyAI provides speaker diarization in async mode with competitive quality for clean audio with well-separated speakers. Diarization costs an additional $0.02/hr on top of the $0.21/hr base rate, putting the effective async rate at $0.23/hr for diarized workloads. Real-time streaming supports multilingual transcription with Universal-Streaming Multilingual at $0.15/hr covering 6 languages (English, Spanish, German, French, Portuguese, Italian), and Whisper-Streaming at $0.30/hr covering 99+ languages.
Gladia's diarization uses pyannoteAI's Precision-2 model, available in async workflows. The Gladia x pyannoteAI webinar covers the technical implementation and what Precision-2 achieves on overlapping speech and noisy recordings. The Gladia benchmark shows up to 3x lower DER compared to alternatives.
PII redaction & compliance
AssemblyAI offers SOC 2, GDPR, and HIPAA compliance, with EU data residency available to all customers via self-serve API parameters through their Dublin endpoint. An opt-out process is available for their model improvement program.
Gladia's compliance hub covers SOC 2 Type II, ISO 27001, HIPAA, and GDPR certifications. PII redaction requires explicit configuration; it is not active by default. Multi-region deployment covers EU-west and US-west clusters, with on-premises and air-gapped hosting available for strict data residency requirements.
Side-by-side comparison: Gladia vs. Deepgram vs. AssemblyAI
Feature comparison
| Criterion |
Gladia |
Deepgram |
AssemblyAI |
| Key differentiator |
Async multilingual (100+ languages) + all-inclusive pricing + up to 29% lower WER |
Sub-300ms real-time streaming |
US-English diarization + LeMUR |
| Async pricing |
$0.20–$0.61/hr, all-in |
~$0.55/hr + add-ons |
$0.21/hr + add-ons (Universal-3 Pro) |
| Real-time pricing |
$0.25–$0.75/hr, all-in |
~$0.55/hr |
$0.15–$0.30/hr |
| Languages (async) |
100+ (42 unique) |
36+ (Nova-3) |
99 |
| Languages (real-time) |
100+ |
36+ |
99+ languages (Whisper-Streaming); 6 languages (Universal-Streaming) |
| Code-switching |
Native, all 100+ languages |
Supported (Nova-3, 10 languages) |
Supported (async, 6 languages) |
| Diarization |
Included (async, pyannoteAI Precision-2) |
Add-on ($0.0020/min, pre-recorded and streaming) |
Add-on ($0.02/hr) |
| Translation |
Included |
Not native |
Separate |
| Target use case |
Meeting assistants, CCaaS, global audio |
Voice agents, live captions |
Async transcription, US-focused |
| Data privacy (paid) |
No retraining, no opt-out needed (Growth/Enterprise) |
Opt-out available (affects pricing) |
Opt-out available |
| EU data residency |
Built-in (EU-west + US-west) |
Available |
Available (self-serve) |
| Compliance |
SOC 2 II, ISO 27001, HIPAA, GDPR |
SOC 2, HIPAA, GDPR |
SOC 2, GDPR, HIPAA |
Production performance: Latency & concurrency
For real-time streaming, Deepgram's Nova-3 delivers the lowest latency of the three at sub-300ms, making it the strongest option for voice agent inference loops. Gladia supports ~270ms final transcript latency in real-time mode as a secondary capability, covered in the real-time webinar.
For async concurrency, Gladia's infrastructure handles hundreds of parallel sessions spinning up instantly without pre-provisioning. A fintech customer runs 800 concurrent sessions through Gladia. This matters most for CCaaS platforms with unpredictable call volume spikes.
Forecasting API spend
Gladia's per-hour pricing on Starter and Growth includes all audio intelligence features, so the cost model is: (hours per month) x $0.20 = monthly spend on Growth. No feature multiplier, no add-on matrix.
Deepgram and AssemblyAI require modeling each feature separately, then applying volume discounts that often require a sales conversation to confirm. For an engineering lead building a cost model at 10x current volume, the single-variable Gladia equation is a significant operational advantage.
Predicting STT costs at production scale
1,000 hours/month: Vendor pricing breakdown
At 1,000 hours per month with diarization enabled, the cost breakdown based on published pricing:
| Provider |
Calculation |
Monthly cost |
| Gladia Growth (all-inclusive) |
1,000 hrs × $0.20/hr |
$200 |
| Gladia Starter (all-inclusive) |
1,000 hrs × $0.61/hr |
$610 |
| Deepgram Nova-3 (base + diarization add-on) |
1,000 hrs × ~$0.67/hr |
~$670 |
| AssemblyAI (base + diarization) |
1,000 hrs × $0.23/hr |
$230 |
The AssemblyAI figure covers only base transcription and diarization. Sentiment analysis, entity extraction, and translation each add separate per-hour charges.
10,000 hours: Predicting STT pricing at scale
At 10,000 hours per month with a full feature set, the cost differences compound:
- Gladia Growth: ~$2,000-2,500/month (flat per-hour rate, all features included)
- Deepgram: ~$6,700/month (base + diarization) (translation not native, costs higher with add-ons)
- AssemblyAI: ~$2,300/month for base + diarization, before sentiment, NER, or translation
Gladia's all-inclusive model produces a flat cost line that scales predictably with audio volume. Competitor models produce curves that steepen as features are activated.
Uncovering hidden STT costs
The features most likely to trigger unexpected costs when moving from a base rate to a fully featured production deployment:
- Translation: Not native on Deepgram, separate on AssemblyAI, included on Gladia Starter/Growth
- Sentiment analysis: Not included on Deepgram, separate on AssemblyAI
- Named entity recognition: Separate on Deepgram and AssemblyAI
- Keyterm prompting and entity detection: Add-on cost on ElevenLabs
- Model improvement opt-out: Changes effective pricing on Deepgram
Gladia includes all of these on Starter and Growth. The audio intelligence documentation covers what is bundled and how to configure each feature.
Which ElevenLabs alternative is right for your use case?
Choose Deepgram if your product is a voice agent where sub-300ms streaming latency is a hard constraint and multilingual depth beyond 10 languages is not a requirement. Live captioning for events and real-time voice interfaces for US-focused products also fit this profile. Expect the model improvement pricing structure and the streaming diarization add-on as trade-offs.
Choose Gladia if your product serves speakers across multiple languages, including code-switching bilingual users, and you need accurate diarization with predictable per-hour billing. CCaaS platforms serving Southeast Asia, South Asia, or Latin America, where Tagalog, Bengali, Punjabi, Tamil, and Urdu speakers make up your user base, fit this profile exactly. Gladia also handles async meeting assistants and post-call analysis platforms where the full pipeline, from recording through transcription to structured LLM-ready output, runs in a single API call.
Choose AssemblyAI for English-first podcast and video transcription with strong diarization in async mode, if you are comfortable with add-on pricing and do not need deep multilingual coverage.
ElevenLabs STT: Common issues & solutions
Production WER: ElevenLabs vs. alternatives
ElevenLabs STT was designed as a convenience feature for TTS-STT loops, not as a standalone transcription engine. The chunked processing of files over 8 minutes, language detection only at call start, and the absence of published WER methodology on noisy audio all point to a system that was not built for the production conditions that meeting assistants and CCaaS platforms encounter daily. The ElevenLabs vs. Gladia comparison covers the technical differences in detail for teams doing a focused evaluation.
API access for PoC evaluation?
Gladia provides 10 free hours per month on the Starter plan with no sales call required. You can get an API key, run your own audio samples through the API, and measure WER on your actual language distribution before committing. The Gladia documentation covers enough to complete a proof-of-concept without speaking to anyone.
STT API setup & integration effort
Gladia supports REST for async and WebSocket for real-time, with official SDKs in Python and JavaScript plus code examples in multiple additional languages. Native integrations cover LiveKit, Twilio, Recall, Pipecat, Vapi, and MeetingBaaS. The audio-to-LLM pipeline documentation covers how to route structured transcript outputs to any downstream model.
Teams migrating from Deepgram or AssemblyAI can follow the Deepgram migration guide or AssemblyAI migration guide for a mapped comparison of API parameters and WebSocket event structures.
Evaluating STT privacy policies
Before finalizing a vendor decision, check three things:
- Is customer audio used for model training by default? On Deepgram, the model improvement program participation affects pricing, meaning the privacy default on standard plans involves data usage unless you opt out. On AssemblyAI, an opt-out process is required. On Gladia, Growth and Enterprise plans default to no retraining with no action required from your side.
- Where does audio data reside? Gladia offers EU-west and US-west clusters as standard, with on-premises and air-gapped deployment for regulated customers.
- What certifications cover the deployment? Gladia holds SOC 2 Type II, ISO 27001, HIPAA, and GDPR certifications, detailed at the Gladia compliance hub.
For teams handling regulated audio in healthcare, financial services, or legal, the Starter plan's data-for-training default means Growth is the minimum tier that provides the no-retraining guarantee. This distinction is stated directly in the pricing documentation, not buried in terms of service.
Test Gladia on your own multilingual audio with 10 free hours. No sales call required. See how Solaria-1 handles language detection, accent-heavy speech, and code-switching in production. Most teams are live in under a day.
FAQs
Does Gladia charge extra for diarization?
No. Diarization powered by pyannoteAI Precision-2, along with translation, sentiment analysis, named entity recognition, and summarization, is included in the base per-hour rate on Starter and Growth plans. Enterprise pricing can be debundled on request.
How many languages does ElevenLabs STT support compared to Gladia?
ElevenLabs STT supports 90+ languages with language detection only at call start and no native code-switching. Gladia's Solaria-1 supports 100+ languages, 42 of which are not covered by any other API-level STT provider, with automatic mid-conversation code-switching in both async and real-time modes, documented in the full supported languages list.
What is the all-in cost for 10,000 hours per month with diarization and translation on Gladia?
On the Growth plan at as low as $0.20/hr with all features included, 10,000 hours runs approximately $2,000-2,500/month depending on commitment level. For context, AssemblyAI at the same volume with diarization alone runs approximately $2,300/month, before sentiment, NER, or translation are added.
Does Gladia use customer audio to train its models?
On Growth and Enterprise plans, customer data is never used for model training and no opt-out action is required. On the Starter plan, data can be used for training by default. For regulated or sensitive audio workloads, Growth is the minimum tier that provides this guarantee by default. Full compliance details are at the Gladia compliance hub.
Key terms glossary
Word error rate (WER): The percentage of words in a transcript that differ from the ground truth, calculated as (substitutions + deletions + insertions) / total reference words. Always pair WER claims with the audio condition and dataset: 3% WER on clean single-speaker English differs significantly from 3% WER on noisy multilingual conversational speech.
Diarization error rate (DER): The percentage of audio incorrectly attributed to the wrong speaker or missed entirely. Gladia's async diarization via pyannoteAI Precision-2 achieves up to 3x lower DER than alternatives per the Gladia benchmark.
Code-switching: When a speaker changes languages mid-conversation, for example moving from English to French within the same utterance. Gladia detects code-switching natively across all 100+ supported languages without requiring a session restart, which is the default behavior for both async and real-time modes.
Async (batch) transcription: Transcription of a complete pre-recorded audio file where the model processes full context before returning output. Async workflows produce higher accuracy, better diarization, and more consistent multilingual handling than real-time streaming, making them the correct architecture for meeting assistants, note-takers, and post-call analysis platforms.
