When your transcription engine silently fails on accented speech, your downstream LLM pipeline does not just degrade, it hallucinates. The wrong name lands in a CRM record, a key entity drops from a coaching scorecard, a meeting summary omits the decision that mattered. By the time anyone catches it, the damage is already downstream.
Most engineering teams choose Rev.ai for its human-in-the-loop fallback. That option is genuinely valuable for accuracy-critical workflows, but at 10,000 hours of audio per month, per-hour billing combined with separate add-on fees for diarization and translation becomes difficult to forecast, and language coverage gaps become impossible to ignore when your product is expanding into European or Asian markets.
This guide gives you the exact technical blueprint to migrate from Rev.ai to Gladia: payload mappings, WebSocket transition logic, a TCO model at realistic scale, and the benchmark data to make a defensible decision.
Overcoming Rev.ai bottlenecks at scale
Multilingual accuracy gaps in production
Rev.ai's Reverb ASR model was built primarily for English. Its documented code-switching support is limited to a small number of language pairs in async mode only, which is a structural constraint for any product serving multilingual workforces or global customer bases. When your audio includes accented speakers, language mixing mid-call, or low-resource languages like Tagalog, Bengali, or Punjabi, accuracy degrades without the kind of obvious error that surfaces in staging. It surfaces in production, through support tickets and quietly churning non-English user segments.
Solaria-1 handles mid-conversation language changes natively across all 100+ supported languages, in both async and real-time modes. For European business audio specifically, Solaria-3 ranks #1 across English, French, German, Spanish, and Italian ahead of AssemblyAI, ElevenLabs, Deepgram, Mistral, and Speechmatics per our public benchmark methodology.
Real-time transcription limitations
Rev.ai's Streaming API imposes documented concurrency limits, with increases requiring a request to Rev.ai support, and enforces a hard session time limit that requires manual reconnection before expiry. Our real-time streaming on Solaria-1 delivers partial transcripts under 103ms, with final transcript latency around ~300ms. A fintech customer currently runs 800 concurrent sessions through our infrastructure with no pre-provisioning required. On Growth and Enterprise plans, concurrency limits scale significantly higher than free tier defaults, removing one class of capacity-planning overhead from your backlog.
Language coverage: Rev.ai vs Gladia
Gladia vs Rev.ai language reach
Rev.ai supports major South Asian languages including Hindi, Bengali, Tamil, Telugu, Marathi, Kannada, and Urdu, but coverage gaps remain for Punjabi, Javanese, Tagalog, and languages across the Caucasus and Central Asia. Solaria-1 covers languages that no other API-level STT provider supports, including Tagalog, Punjabi, Persian, Haitian Creole, Maori, and Javanese, alongside the full breadth of European and Asian high-resource languages.
For European business audio, our Solaria-3 model achieves 6.4% WER on Earnings22 financial call recordings, the only model under 7% on that benchmark, and 33.9% WER on Switchboard conversational speech, the only model under 35%.
Processing mixed language audio streams
Rev.ai requires specifying a single language per session and does not offer native code-switching detection across arbitrary language pairs. Our code-switching detection works automatically in both async and real-time modes without requiring you to pre-specify switch points or manage session state around language transitions. This is not a configuration flag, it is how Solaria-1 was built from the ground up.
For teams building meeting assistants serving international workforces where English and French, or Spanish and English, alternate mid-sentence, this distinction has direct downstream consequences for entity extraction, sentiment analysis, and CRM population accuracy.
Mitigating low-resource transcription errors
Low-resource language failures are silent. There is no HTTP error, no degraded confidence score in the response, just a transcript that is measurably wrong in ways your English-language QA process will not catch.
Three approaches surface these failures before your users do:
- Build a held-out multilingual test set using audio from your actual user distribution, not synthetic reads. Run it against both your current provider and a candidate replacement on the same files, then compute WER per language rather than in aggregate. Aggregate WER hides low-resource degradation behind strong English performance.
- Sample-and-listen on non-English segments specifically. Automated WER requires a reference transcript, which you may not have for every language. A structured listening test on 20–30 clips per language, scored by a native or near-native speaker, often catches systematic errors that aggregate metrics miss, dropped honorifics, misspelled named entities, wrong script rendering.
- Monitor entity extraction accuracy downstream rather than WER alone. If your pipeline extracts names, account numbers, or product terms from transcripts, track the extraction hit rate by language. A drop in entity recall on Tagalog or Punjabi segments is often the first production signal that transcription accuracy has degraded on those languages, well before a user files a support ticket.
Language comparison: Rev.ai vs Gladia Solaria-1
| Capability |
Rev.ai |
Gladia (Solaria-1) |
| Code-switching support |
Limited language pairs, async only |
Full language breadth, async and real-time |
| Translation included in base rate |
No (separate tier) |
Yes (Starter and Growth plans) |
| Foreign language surcharge |
$0.30/hr separate tier |
No surcharge, included at base rate |
Migrating your real-time pipeline
Migrating WebSocket logic
The transition from Rev.ai's WebSocket implementation to ours is a payload restructuring exercise, not a rewrite. The core steps are:
- Session initiation: POST to
/v2/live to create a session and receive your WebSocket URL with a session token. - Connection establishment: Connect to
wss://api.gladia.io/v2/live?token=<temporary-token> using the session token returned in step 1. - Configuration message: Send your initial JSON configuration object specifying language detection, code-switching, and any audio intelligence parameters:
{
"language_config":{"languages":[],"code_switching": true},
"sample_rate": 16000,
"encoding": "LINEAR16"
}
- Audio streaming: Send base64-encoded audio frames as JSON objects to the open WebSocket connection. Rev.ai requires selecting a single language upfront. Our configuration accepts a
language_config object. Set languages to an empty array for automatic detection and code_switching to true, removing the session management logic your team currently owns.
Handling high-volume stream concurrency
Three implementation decisions determine whether your real-time pipeline holds under load:
- Manage WebSocket lifecycle explicitly. Treat each session as ephemeral: open it, stream audio, close it on completion. Do not reuse a WebSocket connection across independent calls. Connection reuse creates subtle state leakage and makes error attribution harder when one session affects another.
- Implement exponential backoff on connection failure. When a WebSocket handshake fails, whether from a transient network event or a brief capacity constraint, back off with jitter rather than retrying immediately. A flat retry loop at scale converts a momentary spike into a sustained thundering-herd problem. A 100ms base delay with 2x multiplier and randomised jitter cap at 30 seconds is a reasonable starting point.
- Decouple your audio capture thread from your send thread. If your capture loop produces frames faster than the WebSocket can flush them, you need a bounded queue between the two with an overflow policy. Either drop oldest frames or apply backpressure to the capture source. Unbounded queues under sustained load exhaust memory before they exhaust the connection.
Growth and Enterprise plans remove concurrency ceiling planning from your backlog. Aircall processes over 1 million calls per week through our infrastructure with no pre-provisioning required.
API parity: mapping Rev.ai to Gladia
Gladia API authentication workflow
Authentication uses a single header, x-gladia-key, passed with every request. There is no OAuth flow, no token refresh cycle, and no session initialization separate from the API call itself. Your API key is issued immediately on account creation and works across both async and real-time endpoints without scope configuration.
Migrating API payloads: Rev.ai to Gladia
The side-by-side comparison below shows a standard async transcription request for both platforms. Rev.ai requires specifying a single language and making separate API calls for translation and sentiment analysis. Our endpoint accepts the full intelligence configuration in a single POST request, with all enrichment running in the same pipeline call. For model selection between Solaria-1 and Solaria-3, refer to the API reference for initiating a transcription.
Rev.ai async request (reference structure):
{
"media_url": "YOUR_AUDIO_URL",
"language": "en",
"skip_diarization": false,
"skip_punctuation": false
}
Translation and sentiment analysis require separate API calls in Rev.ai's model.
Gladia async request (POST /v2/pre-recorded):
{
"audio_url": "YOUR_AUDIO_URL",
"language_config": {
"languages": [],
"code_switching": true
},
"diarization": true,
"diarization_config": {
"number_of_speakers": 3,
"min_speakers": 1,
"max_speakers": 5
},
"translation": true,
"translation_config": {
"model": "base",
"target_languages": ["fr", "en"]
},
"subtitles": true,
"subtitles_config": {
"formats": ["srt", "vtt"]
}
}
Our API returns an immediate response with an id and a result_url. You poll that URL or configure a webhook to receive the completed transcript. The full API reference for initiating a transcription documents every available parameter, and our SDK overview demonstrates the Python and JavaScript SDKs for teams who prefer a higher-level abstraction.
Feature gaps in diarization workflows
Speaker diarization is powered by pyannoteAI's Precision-2 model and is available in async workflows only. Gladia's diarization must be explicitly enabled per request (diarization: true), Rev.ai's async API enables it by default unless you pass skip_diarization. Its streaming API offers a more limited speaker-switch feature rather than performing full diarization. For real-time pipelines where speaker attribution matters, speaker labeling can be handled in post-processing for higher accuracy.
Webhook delivery: Rev.ai vs Gladia
Our webhooks use Svix for reliable delivery. Once transcription completes, we POST to your registered endpoint with a JSON body containing a job identifier, which you use to fetch the full result. Webhook events cover the key lifecycle states: job created, completed successfully, and failed. Both platforms follow the same notify-then-fetch pattern: a webhook fires on job completion, and a separate call retrieves the actual transcript.
The implementation differs in shape. Rev.ai accepts a callback_url in the job submission payload and delivers status metadata on completion, after which a separate call retrieves the transcript itself. We use Svix-delivered events (transcription.created, transcription.success, transcription.error) carrying a transcription_id you use to fetch the full result separately.
Rev.ai vs Gladia: unit economics evaluated
Avoiding hidden costs in STT billing
Rev.ai's base Reverb ASR rate is $0.20/hr for English and $0.30/hr for foreign language audio, per their public pricing. Diarization, translation, and sentiment analysis are not included at these rates and require separate purchases. On our Starter and Growth plans, diarization, translation, named entity recognition, sentiment analysis, summarization, and custom vocabulary are all included in the base per-hour rate. The full pricing breakdown is public and requires no sales conversation to access.
TCO comparison: illustrative estimates at scale (based on public rates, all features included)
| Volume (hours/month) |
Rev.ai (foreign tier base + add-ons) |
Gladia (Growth, all-inclusive) |
Estimated monthly difference |
| 1,000 hours |
~$300+ ($0.30/hr base + add-ons) |
~$200 (as low as $0.20/hr) |
$100+ |
| 10,000 hours |
~$3,000+ |
~$2,000 |
$1,000+ |
| 50,000 hours |
~$15,000+ |
~$10,000 |
$5,000+ |
These figures are illustrative estimates based on Rev.ai's publicly listed foreign language tier rate and the assumption that diarization and translation add-ons apply. Actual Rev.ai costs will vary depending on which add-ons your pipeline requires. Our Growth pricing floors at $0.20/hr with all audio intelligence included. Rev.ai's human transcription option at $1.99/min adds another cost dimension entirely for teams requiring manual verification, and it is a genuine differentiator worth modeling if your compliance workflow mandates human sign-off.
Decision matrix: when to stay vs when to migrate
| Requirement |
Stay with Rev.ai |
Migrate to Gladia |
| Human-in-the-loop fallback |
Yes ($1.99/min) |
No (AI-only infrastructure) |
| Multilingual code-switching |
Limited language pairs, async only |
Yes (100+ languages, Solaria-1) |
| Predictable all-inclusive pricing |
No (add-ons billed separately) |
Yes (diarization, translation, named entity recognition included) |
| EU data residency by default |
Check DPA |
Yes (EU-west or US-west, specified per API call) |
| Real-time concurrency at scale |
Documented concurrency limits (increase requires contacting Rev.ai support), hard session time limit enforced |
Higher limits on Growth/Enterprise |
Executing your switch from Rev.ai to Gladia
1. Benchmark Gladia against Rev.ai output
Run the same audio samples through both APIs before you migrate any production traffic. Use your actual audio distribution: your hardest accent sets, your noisiest call recordings, and the language pairs your users generate most frequently. Our blind STT comparison tool lets you submit audio and compare providers without knowing which model produced which output, removing confirmation bias from the evaluation.
2. Map endpoints for Gladia migration
Replace your Rev.ai base URL with https://api.gladia.io and update the authentication header from Rev.ai's key format to x-gladia-key. The payload restructuring follows the template in the API parity section above. For teams migrating real-time WebSocket connections, our migration guides from other providers document the exact connection flow transition patterns.
3. Configure data residency and compliance
Set your region parameter to eu-west for EU data residency or us-west for North American infrastructure. On Growth and Enterprise plans, regional residency configuration does not introduce a per-hour cost premium. Our compliance hub documents the full data handling posture including DPA terms, certifications, and regional infrastructure boundaries.
4. Stress test the migration pipeline
Before cutting over production traffic, run concurrency tests at 2x your current peak load. Test error handling for webhook failure events and verify your retry logic against our status page. For Contact Center as a Service (CCaaS) platforms processing high-volume contact center audio, test with your actual call length distribution rather than synthetic samples, since call length variance affects async processing time estimates.
5. Monitor WER and latency in production
Set up WER tracking against a held-out set of manually verified transcripts from your own audio. Our key data extraction guide covers how to measure entity extraction accuracy on production call data, which is often a more meaningful signal than aggregate WER for CCaaS use cases. Track p50 and p99 latency separately for async and real-time workflows, since tail latency matters more than the average for real-time voice agent pipelines.
Self-serve POC checklist:
- Run 50-100 representative audio files through both APIs and compare entity extraction accuracy on names, account numbers, and domain-specific terms
- Measure end-to-end latency from submission to transcript delivery under your typical concurrent load
- Verify webhook delivery reliability under simulated network interruption
- Confirm EU-west or US-west region routing matches your data residency obligations
- Model all-in cost at current volume using our public pricing
Protecting sensitive audio workflows
Data residency and DPA requirements
Our infrastructure runs on dedicated EU and US clusters. You specify the target region in the API call, and audio from intake through transcript delivery stays on that infrastructure. For EU-headquartered teams, this means GDPR compliance is structural rather than procedural, and it is a standard API parameter, not an enterprise-only contract clause.
The DPA is a standard agreement available without an enterprise sales conversation, which means your legal team can complete their review before you finalize the vendor decision.
Meeting GDPR and SOC 2 requirements
We hold SOC 2 Type II, ISO 27001, HIPAA, GDPR, and PCI certifications.
Model training and data usage by plan
On our Growth and Enterprise plans, customer data is never used for model training, and no opt-out action is required. No contract clause negotiation is needed. On the Starter plan, audio can be used for model training by default, so teams handling sensitive or regulated audio should move directly to Growth or request an Enterprise agreement to get this protection as a default.
Compliance comparison: Rev.ai vs Gladia
| Compliance standard |
Rev.ai posture |
Gladia posture |
| GDPR |
Not independently verified against current documentation |
Certified, EU-west and US-west regions available |
| SOC 2 Type II |
Available |
Certified |
| HIPAA |
Available |
Certified |
| ISO 27001 |
Not independently verified against current documentation |
Certified |
| Data retraining policy |
Not independently verified against current documentation |
Never on Growth/Enterprise (default) |
| On-premises deployment |
Available (Docker container) |
Available |
"Rev.ai's compliance posture in the rows marked 'Not independently verified' could not be confirmed against live documentation at time of publication. Verify directly with Rev.ai before making vendor decisions based on this comparison."
Migration logic and implementation details
Integration roadmap and duration
Most engineering teams complete the payload mapping, endpoint transition, and initial production validation in under 24 hours. Scoreplay reported: "In less than a day of dev work we were able to release a state-of-the-art speech-to-text engine." The standard REST and WebSocket protocols mean your existing HTTP client and WebSocket handling code requires no changes, only updated endpoints and authentication headers. We offer direct Slack access to our engineering team during integration, not a ticket queue.
Validating Gladia against production data
Do not validate against synthetic benchmarks alone. Run your actual production audio against our API during the POC phase, particularly the audio segments where Rev.ai currently produces the most errors. For meeting assistant teams, Claap achieved 1-3% WER in production and transcribes one hour of video in under 60 seconds after migrating to our API. Spoke transcribes 27,000+ meeting hours weekly across European markets on our infrastructure.
Choosing between Solaria-3 and Solaria-1
Solaria-3 is optimized for European business audio quality across English, French, German, Spanish, and Italian. Solaria-1 provides maximum language breadth, real-time streaming, and full code-switching support across all supported languages. Our open benchmark methodology compares Solaria-1 against eight providers across seven datasets and 74+ hours of audio with reproducible test conditions you can verify against your own audio.
Handling your Rev.ai data exports
Historical transcripts from Rev.ai export as JSON or plain text. If you need to run enrichment workflows such as summarization or entity extraction on historical audio, re-processing the original files through our API is the recommended path. The pipeline transforms audio into structured outputs in a single call.
Start with €50 in free credit and have your integration in production in less than a day. Test Gladia on your own multilingual audio to see how it handles language detection, accent-heavy speech, and code-switching.
FAQs
How long does it take to migrate from Rev.ai to Gladia?
Most engineering teams complete the API payload mapping and transition to production in less than 24 hours. The migration requires updating your base URL, replacing your authentication header, and restructuring the request payload to our unified format, with no changes to your existing HTTP client or WebSocket handling code.
Does Gladia support human-in-the-loop transcription like Rev.ai?
No, we are a pure-play AI audio infrastructure provider with no human review option. Solaria-3 is our most accurate model for real-world European business audio, and its benchmark results on financial call recordings reduce the practical need for manual review on most production audio workflows.
Where is my audio data stored during processing?
We support multi-region data residency on all paid plans, allowing you to route and store your audio entirely within EU-west or US-west regions. You specify the region in the API call, and all processing and storage stays on that regional infrastructure by default.
Is my data used to train Gladia models?
On our Growth and Enterprise plans, your data is never used for model training, and no opt-out action is required. On the Starter plan, audio may be used for training by default, so teams handling sensitive or regulated audio should migrate directly to a Growth or Enterprise plan.
Can Gladia handle code-switching in real-time as well as async?
Yes. Code-switching detection is supported in both real-time and async modes on Solaria-1. Enable it by passing {"languages": [], "code_switching": true} inside the language_config object in your API payload. For real-time multilingual pipelines, Solaria-1 is the correct model choice, as Solaria-3 is async-only and has more limited code-switching support.
What happens to diarization in real-time workflows after migrating?
Diarization powered by pyannoteAI's Precision-2 model is available in async workflows only. For real-time pipelines, speaker attribution can be handled in post-processing for higher accuracy. Async diarization has full audio context available, which is why it produces more reliable speaker labels than any real-time approach.
Key terms glossary
Word Error Rate (WER): The standard metric for measuring speech-to-text accuracy, calculated by dividing the sum of insertions, deletions, and substitutions by the total number of reference words. Lower is better.
Diarization Error Rate (DER): The metric used to evaluate speaker attribution accuracy, measuring the percentage of time that speaker labels are incorrectly assigned across a recording.
Code-switching: The practice of alternating between two or more languages or dialects within a single conversation. Solaria-1 handles this natively across all supported languages in both async and real-time modes.
Data Processing Agreement (DPA): A legally binding contract that establishes the rights and obligations of data controllers and processors under GDPR, governing how audio data is stored, processed, and retained by a vendor.
Diarization: The process of partitioning an audio stream into segments according to the identity of each speaker, commonly called "who spoke when." On our platform, this is powered by pyannoteAI's Precision-2 model and is available in async workflows only.
