Updated April 1, 2026
TL; DR: Building a production-grade AI note-taker means solving the audio pipeline before you touch the LLM layer. Self-hosting Whisper introduces GPU provisioning, maintenance overhead, and engineering costs that push monthly spend to $25,000-$50,000 at 10,000 hours of audio. Async transcription via a managed API handles the hard parts (diarization, code-switching, multilingual accuracy) in a single call, returning structured JSON you pass directly to your LLM. Gladia's async API processes one hour of audio in under 60 seconds, supports 100 languages including 42 unavailable on competing platforms, and includes all audio intelligence features at a per-second billing rate (typically expressed as cost per hour for planning purposes) with no add-on fees.
The secret to a great AI meeting assistant has nothing to do with the LLM you choose. It depends entirely on the word error rate of your transcription pipeline. Teams building self-hosted Whisper deployments burn significant engineering capacity on GPU provisioning and maintenance, only to discover their unit economics are harder to model than expected once they scale, or that WER degrades on Tamil and other South Asian language speakers as ArXiv research on ASR performance confirms, with Tamil reaching 93.3% WER on baseline Whisper models.
This guide covers the complete architecture for an async AI note-taker: the build-versus-buy decision for speech infrastructure, every pipeline component from audio ingestion to downstream CRM integration, cost models at realistic production scale, and how to structure your LLM prompts for summaries and action items.
The build vs. buy calculation for speech infrastructure
Self-hosting Whisper vs. managed APIs
Self-hosting looks cheap on a spreadsheet until you account for the full stack. A dedicated GPU instance starts at roughly $276/month minimum, but at 10,000 hours of audio per month you need multiple concurrent instances. AWS g5.xlarge GPU instances in a production region run approximately $3,600/month for three instances, and that covers compute only, before you add storage, networking, and the monitoring stack.
The number that breaks most TCO models is engineering cost. Maintaining a self-hosted Whisper deployment requires 0.25 to 0.5 FTE ongoing for GPU driver updates, CUDA version compatibility, model quantization, and autoscaling logic. At a fully loaded cost of $160,000 per year for a senior engineer, that 0.25 to 0.5 FTE translates to $3,333-$6,667 per month in engineering time alone, and that is before the team's attention returns to product work. Combined with infrastructure costs, self-hosting at scale often exceeds managed API pricing.
The other problem is utilization. Self-hosting is only cost-effective if your GPUs stay busy. For sporadic or bursty workloads, idle GPU time erases the per-hour savings immediately, and a managed API bills only for what you process.
For teams in air-gapped or heavily regulated environments where on-premises deployment is a hard requirement, self-hosting remains a valid path. Gladia also offers on-premises and air-gapped deployment for those specific cases, which removes the trade-off entirely.
Cost modeling at 10,000 hours
Here is what the numbers look like at 10,000 hours per month with the full audio intelligence suite enabled (diarization, sentiment analysis, named entity recognition, and summarization):
| Approach |
Monthly cost at 10,000 hrs |
Diarization |
Sentiment + NER |
Notes |
| Self-hosted Whisper |
$25,000-$50,000 |
DIY |
DIY |
Infrastructure + engineering overhead |
| AssemblyAI (with add-ons) |
$3,000-$4,500 |
Included (Universal-2) |
Billed separately, stacks per feature |
From $0.30-$0.45/hr effective with common add-ons |
| Gladia Scaling (async) |
$5,000 |
Included |
Included |
Flat $0.50/hr on Scaling plan, all features at base rate |
The add-on pricing model compounds like interest. Each feature metered separately makes the total bill harder to model as you scale, and the effective rate can reach multiples of the headline figure. Gladia's all-inclusive pricing means your cost at 10,000 hours is the hourly rate multiplied by the hours, no footnotes required. You can verify the current rate structure on the Gladia pricing page.
Core components of an AI note-taker pipeline
Audio ingestion and pre-processing
Your ingestion layer needs to handle format diversity from day one. Meeting recordings arrive as MP4, M4A, WAV, FLAC, and AAC depending on the recording platform. Gladia's async API accepts all of these formats natively, up to 1,000 MB per file and 135 minutes per request on standard plans (up to 255 minutes on Enterprise).
For files that exceed standard limits, the efficient approach is to upload via the upload endpoint documented in Gladia's API reference and receive a hosted audio URL, then pass that URL to the transcription request. This decouples file transfer from transcription job management and makes retry logic cleaner when upstream recording platforms send oversized files.
Pre-processing decisions you need to make upfront:
- Sample rate normalization: Gladia handles 8kHz Twilio audio natively without conversion, which matters if your note-taker captures phone meetings
- File splitting for long meetings: Standard plan limits cap at 135 minutes, so sessions over that threshold need splitting logic before submission
- Metadata attachment: Pass meeting metadata (participant names, scheduled time, calendar source) as part of your job payload so you can reference it when constructing LLM prompts downstream
Async vs. real-time transcription
For a note-taker, async transcription is the right default, but the optimal choice depends on your use case: live support, accessibility services, and legal transcription each weigh these trade-offs differently:
| Dimension |
Async |
Real-time |
| Latency |
1 hour of audio in under 60 seconds |
Sub-300ms final transcript |
| Accuracy |
Higher (full audio context available) |
Lower (single-pass, limited context window) |
| Diarization |
Full speaker attribution via pyannote.ai Precision-2 |
Post-transcription processing |
| Primary use case |
Meeting summaries, action items, compliance records |
Live agent assist, voice interfaces |
| Gladia pricing |
From $0.50/hr (Scaling) |
From $0.55/hr (Scaling) |
The 60-second processing time for a one-hour recording, confirmed in Claap's production deployment, is fast enough for immediate post-meeting delivery while giving the model full audio context for higher accuracy. Teams building hybrid architectures can use real-time to drive a live transcript UI during the meeting, then trigger an async job at meeting end to generate the authoritative record with full diarization.
Watch the Gladia real-time webinar replay if you're evaluating the hybrid approach, and the high-speed Whisper transcription tutorial for async processing deep-dives.
Speaker diarization and code-switching
Diarization is technically harder than transcription. Attributing overlapping speech to individual speakers, handling cross-talk, and maintaining consistent speaker IDs across a 90-minute recording requires a dedicated diarization model working with full audio context, not a post-processing step applied to an already-completed transcript. Gladia's diarization implementation uses pyannote.ai's Precision-2 model and returns speaker-labeled utterances with word-level timestamps in the JSON response.
Code-switching is where most production pipelines fail silently. When a bilingual speaker in a Southeast Asia BPO switches mid-sentence between English and Tagalog, most APIs either garble the output or assign the wrong language to entire utterance blocks. Gladia handles code-switching across 100+ supported languages natively in both async and real-time modes.
"Excellent multilingual real-time transcription with smooth language switching. Superior accuracy on accented speech compared to competitors. Clean API, easy to integrate and deploy to production." - Verified user review of Gladia
The pyannoteAI diarization webinar covers the technical decisions behind the Precision-2 integration if you want to understand how diarization accuracy holds up on overlapping speech.
Designing the LLM integration and multi-agent architecture
Structuring prompts for summaries and action items
The structured JSON output from your async transcription call is your LLM's input. Each utterance carries a speaker ID, start/end timestamps, and word-level confidence scores. The Audio to LLM documentation covers how to pass this structure into downstream models.
Constraining the model with explicit output format requirements and limiting it to the JSON schema reduces hallucination and makes downstream parsing deterministic. For summary generation, a system prompt structured around the JSON schema produces consistent output:
You are a meeting analyst. Given the diarized JSON transcript below, where each
utterance has a speaker ID (integer), text, start_time, and end_time, produce:
1. A 3-bullet executive summary (1-2 sentences per bullet)
2. A list of all action items in this format:
{"assignee": "Speaker X or name if mentioned", "task": "description", "timestamp": float}
Only extract explicit commitments with clear ownership. Do not infer implicit tasks.
Transcript:
{transcript_json}
For action item extraction specifically, constrain the model tightly. Asking for "all tasks mentioned" returns noise. Asking for "explicit commitments where a speaker assigns a task to a named person or themselves, with a timestamp" returns usable data.
Advanced LLM use cases: sentiment and entity extraction
For teams building on top of the transcription, CrewAI's multi-agent framework lets you route specialized tasks to dedicated agents running in parallel. The architecture maps directly to Gladia's JSON output structure:
- Transcript analyzer agent: Maps speaker IDs to participant metadata from your calendar system and identifies the conversation structure
- Sentiment agent: Evaluates emotional tone per speaker and per meeting phase
- Action item agent: Extracts commitments with assignee attribution
- Entity recognition agent: Pulls company names, dates, dollar figures, and product references
- Summary agent: Combines all agent outputs into an executive summary
- Report generator: Compiles the final structured payload for downstream delivery
A multi-agent architecture like this lets each agent specialize in a single task against the same transcript JSON from Gladia's API, so you don't need to coordinate multiple audio processing vendors. IBM's multi-agent CrewAI tutorial for call analysis demonstrates how agents pass results between each other and how the aggregator step produces the final report. The Gladia Audio Intelligence documentation also covers how to enable sentiment analysis and NER directly through the API for teams that want these features without managing a separate agent framework.
API design and security considerations
Webhooks, rate limiting, and error handling
For async transcription jobs that take 30-60 seconds to process, polling is the wrong pattern. A webhook-based architecture returns control to your application immediately and notifies you when processing completes.
The workflow for a production async note-taker:
- Submit the transcription request (POST to
/v2/transcription/) and receive a job ID and result URL immediately - Store the job ID linked to the meeting record in your database
- Receive the webhook callback when processing completes, with the full result payload
- Trigger your LLM pipeline using the webhook payload
Here is the Python implementation for submitting an async job with diarization and code-switching enabled, based on Gladia's API documentation:
````python
import requests
import time
```python
GLADIA_API_KEY = "your_gladia_api_key"
TRANSCRIPTION_URL = "https://api.gladia.io/v2/transcription/"
````
headers = {
"x-gladia-key": GLADIA\_API\_KEY,
"Content-Type": "application/json"
}
def submit\_transcription(audio\_url, webhook\_url=None):
payload = {
"audio\_url": audio\_url,
"diarization": True,
"diarization\_config": {
"number\_of\_speakers": None, # Auto-detect
"min\_speakers": None,
"max\_speakers": None
},
"language\_behaviour": "automatic", # Enables code-switching
"detect\_language": True
}
```
if webhook_url:
payload["webhook_url"] = webhook_url
response = requests.post(
TRANSCRIPTION_URL,
headers=headers,
json=payload
)
return response.json()
```
Submit with webhook for production
```python
job = submit_transcription(
audio_url="https://your-storage.com/meeting.mp4",
webhook_url="https://example.com/api/transcription-complete"
)
print(f"Job ID: {job['id']}")
print(f"Result URL: {job['result_url']}")
When the job completes, Gladia calls your webhook_url with the full result. Your handler validates the webhook signature, extracts the transcript, and fires the LLM pipeline. Check the status field for "error" cases and implement retry logic for failed downstream processing.
For resilience, implement idempotent webhook handlers so duplicate deliveries do not create duplicate notes.
For teams that need polling as a fallback, the pattern is to GET the result_url at regular intervals until status equals "done" or "error".
Data privacy and SOC 2 compliance
The compliance question every enterprise customer’s legal team asks first is simple: does the vendor retrain on customer audio?
Gladia’s privacy policy states that on paid plans, customer audio is never used for model retraining by default. On the free plan, audio is automatically processed for service improvement, with no opt-out option available. This is a plan-level default, not an enterprise-only contract clause.
For teams handling regulated data, Gladia’s compliance hub documents SOC 2 Type 2, HIPAA, GDPR compliance, and data residency across EU-west and US-west regions.
For PII handling in meeting notes, configure your LLM prompts to redact names and contact details in the summary output. Gladia also encrypts data at rest and in transit, and EU and US workloads run on separate regional infrastructure.
Integrating the output with downstream tools
Your LLM pipeline produces structured JSON: a summary array, an action items array, speaker-attributed sentiment scores, and named entities.
The integration layer’s job is to route this output to the right destinations without coupling your note-taker to any single downstream tool.
A clean architecture separates delivery targets into three categories:
- Immediate notification: Post a Slack message with the summary and action items to the meeting channel, triggered by the webhook handler after the LLM step completes.
- CRM enrichment: Write action items as tasks in Salesforce or HubSpot, matched to the deal or contact record by participant email.
- Knowledge base: Create a Notion or Confluence page with the full transcript, summary, and action items, organized by meeting date and attendees.
The integration layer should be event-driven. Publish a meeting.processed event when the LLM step completes, and let individual integrations subscribe to that event. This decouples your pipeline from downstream tool availability and makes adding new integrations a one-subscriber change.
For teams building custom integrations, the Gladia library documentation covers SDK options across Python, TypeScript, and REST.
How Gladia simplifies the async transcription pipeline
The async pipeline described in this guide requires exactly one API call to handle transcription, diarization, code-switching, sentiment analysis, named entity recognition, and summarization.
Gladia includes all of these features in the base async rate with no per-feature billing.
For a meeting where participants switch between English and French, the response structure includes language detection alongside speaker labels and word-level timestamps:
{
"id": "45463597-20b7-4af7-b3b3-f5fb778203ab",
"status": "done",
"result": {
"transcription": {
"full_transcript": "Hello, thank you for joining today.",
"languages": ["en", "fr"],
"utterances": [
{
"speaker": 0,
"text": "Hello, thank you for joining today.",
"start": 0.5,
"end": 2.8,
"confidence": 0.98,
"words": [
{
"word": "Hello",
"start": 0.5,
"end": 0.9,
"confidence": 0.99,
"speaker": 0
},
{
"word": "thank",
"start": 1.1,
"end": 1.3,
"confidence": 0.97,
"speaker": 0
}
]
}
]
}
}
}
The languages array reflects code-switching detection across the full recording. Speaker IDs remain consistent across the full transcript, which means your LLM prompt can reference Speaker 0 throughout and produce coherent speaker-attributed summaries.
Claap, a video meeting platform, reached 1-3% WER in production and transcribes one hour of video in under 60 seconds using this pipeline. That processing speed means post-meeting notes are ready before the next calendar block starts.
"We have tested it across many many languages (we work with commentators in pro sports around the world) and have found great accuracy even with custom fields such as team names, player names, etc. We have never come across any sort of hallucination." - Verified user review of Gladia
Gladia covers 100+ supported languages, including 42 unavailable across alternative vendorson competing platforms, which matters when your meeting participants include speakers of Tagalog, Bengali, Tamil, or Punjabi. Those are not low-quality additions: they are trained on production data targeting BPO and outsourcing markets where those languages dominate.
"Gladia delivers precise speech-to-text transcriptions with reliable timestamps, making it perfect for downstream tasks. It saves time and ensures smooth integration into our workflows." - Verified user review of Gladia
You can review Gladia's WER benchmark methodology and dataset coverage on the real-time API benchmarks page, which includes but is not limited to covers Mozilla Common Voice and Google FLEURS test conditions.
"Gladia provides a highly accurate real-time speech-to-text solution for high volumes of support and service calls. Latency is low and accuracy high, even for numericals. We've appreciated the quality of support across pre-processing, post-processing, and model optimization." - Verified user review of Gladia
These production outcomes reflect what you can validate in your own environment before committing to a paid plan. The free tier gives you 10 hours per month with all features enabled and no credit card required. Given that multiple customers report sub-24-hour integration to production, you can have a working pipeline before your next sprint planning session. Start with 10 free hours and test the async API on your own multilingual meeting audio at gladia.io.
FAQs
What is the latency difference between async and real-time transcription?
Gladia's async API processes one hour of audio in under 60 seconds on average, making it suitable for post-meeting delivery. Real-time transcription returns final transcripts in roughly 700ms for a 3-second utterance, with partial results under 103ms.
How do I handle audio files longer than 135 minutes?
Standard plans support up to 135 minutes per request with a 1,000 MB file size limit. Enterprise plans extend this to 255 minutes and include custom pricing, on-premises and air-gapped deployment options, and dedicated support, making them the right fit for high-volume teams or regulated industries with strict data-residency requirements. For recordings beyond the standard limit, split the file before submission or contact Gladia to discuss an Enterprise plan.
What is the all-inclusive cost per hour for full audio intelligence?
Gladia's Scaling async plan is $0.50/hr and includes diarization, sentiment analysis, named entity recognition, summarization, code-switching, and translation. The Self-Serve async rate is $0.61/hr with the same feature set; Self-Serve is the entry-level, pay-as-you-go tier and is the right choice for lower-volume workloads where you want to get started without a volume commitment. The Scaling plan is designed for higher-volume use and delivers a lower per-hour rate as a direct trade-off for that increased throughput. There are no per-feature add-on charges at any tier.
Does Gladia retrain models on customer audio?
On paid plans, customer audio is never used for model retraining. On the free plan, audio is automatically processed for service improvement with no opt-out option available.
Which compliance certifications does Gladia hold?
Gladia holds SOC 2 Type 2 and HIPAA certification and is GDPR-compliant. EU and US workloads are processed on separate regional infrastructure.
What file formats does the async API accept?
Gladia's async API accepts WAV, M4A, FLAC, AAC, and MP4, along with direct audio URLs. YouTube direct links are supported up to 120 minutes.
Key terms glossary
Word Error Rate (WER): The percentage of words in a transcript that differ from the ground-truth reference. A 1-3% WER in production, as Claap achieved with Gladia, means fewer than 3 words per 100 are incorrect.
Diarization: The process of segmenting a transcript by speaker, attributing each utterance to a specific individual. Production diarization requires a dedicated model (Gladia uses pyannoteAI Precision-2) working with the full audio file.
Code-switching: The phenomenon where a speaker switches languages within a single conversation or sentence. Most ASR APIs fail on code-switching because they assume a fixed input language per audio file.
Async (batch) transcription: A processing model where a complete audio file is submitted and the result is returned via webhook when processing completes, eliminating the need for persistent WebSocket connections or polling loops.
Multi-agent architecture: An LLM orchestration pattern where specialized agents (summarizer, sentiment analyzer, entity extractor) each process the same input independently and a coordinator agent combines their outputs into a final result.
Webhook: An HTTP callback that sends a result to a specified URL when a long-running job completes. Webhooks eliminate polling loops and reduce infrastructure cost for async pipelines.
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