Most product teams evaluating transcription for after-call work focus entirely on base API costs and ignore the add-on fees for diarization and entity extraction that destroy unit economics at scale. That blind spot costs engineering cycles that should be building core features, not debugging audio pipelines.
Reducing (ACW) is no longer a transcription problem: it is a workflow orchestration and CRM synchronization problem. This guide compares Tier 1 conversation intelligence platforms against Tier 2 ppeech-to-text APIs, breaking down total cost of ownership, integration complexity, and real-world accuracy needed to automate post-call data extraction at scale.
The cost of after-call work on team velocity
After-call work accumulates silently. Each minute an agent spends logging call details, updating CRM fields, applying disposition codes, and drafting follow-up emails compounds across thousands of daily interactions.
Manual after-call work limitations
ACW breaks into four categories:
- Documentation and notes: Agent-written call summaries and interaction records.
- CRM and ticket updates: Syncing call outcomes to customer records and support queues.
- Disposition coding: Tagging calls by type, outcome, and resolution status.
- Follow-up scheduling: Logging required callbacks, escalations, or next actions.
Industry research shows that 57% of service leaders expect call volumes to increase by as much as 20% over the next one to two years. If ACW scales linearly with volume and your team is still relying on manual documentation, that growth directly inflates agent headcount requirements and degrades the CRM data quality that downstream AI systems depend on.
Hidden engineering cost of building in-house
Teams running self-hosted transcription setups frequently encounter accuracy challenges in production, with word error rates that can make automated CRM population unreliable for anything more precise than call categorization. Beyond accuracy, self-hosting introduces infrastructure overhead, manual scaling, and ongoing DevOps capacity. The real cost is not the server bill: it is the engineers spending three months wrestling with audio pipeline bugs instead of shipping the AI coaching features your users are waiting for.
Impact on time-to-market for AI features
Infrastructure bottlenecks have a compounding effect on roadmap velocity. A three-month proof-of-concept for a foundational API capability delays roadmap items by a full quarter. Production teams integrating well-documented STT APIs with direct engineering support typically report integration times under 24 hours, freeing engineering to build search, agent coaching, and CRM webhooks on top of a reliable audio layer rather than maintaining one.
Optimizing ASR for velocity and cost
The build vs.buy decision for ACW reduction is primarily a question of scale and language. For teams shipping their first transcription feature in standard English with no custom LLM routing requirements, buying a packaged platform is the faster path. For teams processing high-volume, multilingual contact center audio with structured output requirements, building on a dedicated STT API delivers better unit economics at every volume milestone above a few thousand hours per month.
Packaged conversation intelligence
Packaged CI platforms handle recording, transcription, enrichment, and CRM integration as a bundled product. The tradeoff is rigidity: you ship quickly but you are constrained by the vendor's feature roadmap, their definition of a useful call summary, and per-seat pricing structures designed for sales team headcount rather than per-hour audio volume.
STT APIs: enhanced call intelligence
API-tier providers separate the audio infrastructure from the application layer. You own the pipeline: audio in, structured data out, LLM routing of your choice. Most teams report sub-24-hour integration times when working with a well-documented API and direct engineering support, and the flexibility is significant for product teams building proprietary contact center tooling or multilingual workflows that packaged platforms do not support.
Build or buy: which path is right?
The decision hinges on four variables: engineering capacity, multilingual requirements, call volume scale, and LLM routing flexibility. Teams with no engineering resources and standard English needs should buy. Teams processing high-volume multilingual audio with custom downstream workflows should build on an API.
| Criteria |
Buy (CI platform) |
Build (STT API) |
| Time to first value |
Days |
Hours to days |
| Pricing model |
Per seat, annual |
Per hour, usage-based |
| Multilingual depth |
Varies by platform |
Up to 100+ languages |
| Custom LLM routing |
Typically vendor-controlled |
Full control |
| Engineering overhead |
Low |
Low to moderate |
| TCO at 10,000 hrs/mo |
Varies by vendor |
Lower (per-hour, all-inclusive) |
| Data governance |
Varies by vendor |
Configurable per tier |
Tier 1: End-to-end conversation intelligence
Several tools occupy the broader conversation intelligence category before the named enterprise platforms: Fireflies.ai, Otter.ai, Fathom, Trint, Rev, Sonix, CallRail, Invoca, Five9, and SalesCloser.ai all offer varying degrees of ACW automation for smaller teams or specific verticals. The six platforms below represent the most widely deployed options for enterprise contact centers and revenue teams.
Gong: transcription accuracy for product insights
Gong captures calls, transcribes them, and pushes structured deal summaries and CRM syncs tied to deal stage. Gong is a premium-priced platform designed for enterprise sales teams, with pricing typically structured as a per-seat annual contract plus platform fees.
Chorus: transcription for ACW automation
Chorus (ZoomInfo) is positioned as a cost-effective alternative to Gong for mid-market sales teams, offering conversation intelligence features on a per-seat annual pricing model.
CallMiner: automated ACW reduction
CallMiner focuses on compliance automation and speech analytics at scale, designed for regulated-industry workflows (financial services, healthcare). Pricing is custom and enterprise-focused rather than the developer-accessible pipeline that product teams building custom tooling need.
Observe.AI: transcribe and analyze for ACW
Observe.AI targets real-time agent coaching and post-call quality assurance. Pricing is custom and enterprise-only, designed for teams buying a finished QA workflow rather than building one.
Balto: reduce after-call work effort
Balto focuses on real-time agent guidance during calls, particularly useful in high-volume inside sales environments where consistency and script adherence reduce the manual effort needed to document outcomes post-call. Pricing is custom based on user volume and integrations.
Avoma: automated notes and custom templates
Avoma operates at the lower end of CI platform pricing with per-seat annual plans, per Avoma's public pricing page. It offers automated note-taking with custom templates and AI-generated features.
When full CI platforms deliver ROI
Tier 1 platforms are the right choice when: the team has no available engineering resources for API integration, the use case is standard English sales or support calls, CRM sync to a supported platform (Salesforce, HubSpot) covers the full requirement, and the user base does not extend to non-English speakers requiring production-grade accuracy. If those four conditions hold, the time-to-value from a packaged platform outweighs the per-seat cost at typical team sizes.
Tier 2: Speech-to-text APIs with audio intelligence
API-tier providers give product teams direct access to the transcription and enrichment layer, with structured outputs they can route to any LLM or CRM connector. The critical evaluation criteria at this tier are: accuracy on real-world production audio, feature bundling versus add-on pricing, multilingual depth, and data governance defaults.
Gladia: multilingual accuracy at scale
Solaria-1 covers 100+ languages with native mid-conversation code-switching, evaluated against eight providers across seven datasets and 74+ hours of audio in the async benchmark, showing on average 29% lower WER on conversational speech and 3x lower DER versus alternatives.
Gladia also runs Solaria-3, its depth model for European real-world business audio across English, French, German, Spanish, and Italian. Solaria-3 ranks #1 against AssemblyAI, ElevenLabs, Deepgram, Mistral, and Speechmatics on real customer recordings: 6.4% WER on Earnings22 financial calls (only model under 7%), 33.9% on Switchboard conversational speech (only model under 35%). Solaria-3 is async only. Use Solaria-1 for breadth and code-switching; use Solaria-3 for European contact center and business audio.
The async pipeline processes audio efficiently and returns word-level timestamps, speaker labels, translation, named entities, text-based sentiment, and summaries in a single API call. All of this is included at the base rate on Starter and Growth plans with no add-on fees. For the CCaaS use case, the 42 languages we cover that no other API-level provider supports, including Tagalog, Bengali, Punjabi, Tamil, Urdu, Persian, and Marathi, directly serve BPO operations in Southeast Asia, South Asia, and Latin America.
AssemblyAI for actionable call insights
AssemblyAI's Universal model covers multiple languages with base transcription starting at $0.15 per hour per AssemblyAI's public pricing page. Speaker diarization is available for most supported languages. Add the full intelligence suite (speaker identification, sentiment, summarization, entity detection, and topic detection), and the effective rate increases significantly per AssemblyAI's public add-on pricing structure. Real-time streaming language support is more limited than batch processing. AssemblyAI's LeMUR layer also positions them as an application-layer product competing with the meeting assistants and contact center platforms built on their API.
Deepgram: predictable cost STT API
Deepgram's Nova-3 model is available at rates listed on Deepgram's public pricing page. The strategic consideration for product teams is that Deepgram now offers a Voice Agent API that positions them as a direct competitor to the CCaaS and voice agent products built on their speech layer. A technical migration reference from Deepgram to Gladia is available in the Gladia migration docs.
Integrating OpenAI's transcription API for custom ACW
OpenAI's transcription API (gpt-4o-transcribe model) prices audio at $0.006 per minute ($0.36 per hour). The limitations for production ACW are specific: the API enforces a 25MB file cap (requiring chunking for calls longer than roughly 20 minutes), has no native real-time endpoint, and does not bundle diarization or NER at any price point. OpenAI's transcription API generates plausible-sounding text even when there is nothing to transcribe, a documented hallucination artifact from its training data that pollutes transcripts in production.
APIs for custom transcription challenges
Tier 2 APIs are the correct architectural choice when call volume exceeds a few thousand hours per month, the product requires non-English language support, structured outputs need to route to a custom LLM pipeline, or data governance requirements rule out vendors that train models on customer audio by default.
CI vs. STT: differentiating feature sets
| Feature |
Tier 1 (CI platforms) |
Tier 2 (STT APIs) |
| Pricing model |
Per seat, annual contract |
Per hour, usage-based |
| Diarization |
Typically bundled |
Varies (included with Gladia, add-on elsewhere) |
| NER and entities |
Platform-dependent |
Configurable, structured JSON output |
| Multilingual depth |
Varies by platform |
Up to 100+ languages (Gladia) |
| LLM routing |
Typically vendor-controlled |
BYO model or integrated |
| Data governance |
Varies by vendor |
Configurable per tier |
| Integration time |
Weeks to months |
Hours to 1 day (REST or WebSocket) |
| CRM sync |
Native connectors available |
Custom via structured output |
"Who spoke when": diarization accuracy
Diarization attributes each segment of a transcript to the specific speaker who produced it. For ACW automation, this transforms a raw transcript into a structured conversation: agent turn one, customer turn one, agent turn two. Without accurate speaker attribution, automated CRM population of agent-specific fields (resolution time, script adherence score, coaching trigger) is not possible. Gladia's speaker diarization delivers lower diarization error rate than alternatives for async workflows. This is an async-optimized capability: speaker attribution at this accuracy level benefits from full-context analysis of the complete recording, which is why the async pipeline is the right architecture for post-call ACW workflows.
Code-switching transcription quality
Code-switching, where a speaker shifts between languages mid-sentence or mid-call, is one of the most reliable ways to break legacy transcription models. The failure mode is silent: the model either produces garbled output or falls back to English, corrupting the transcript without triggering any error signal. Our guide on code-switching in contact centers documents exactly how this failure cascade damages ACW data quality. Solaria-1 handles code-switching natively across 100+ supported languages, detecting mid-conversation language changes automatically without a broken session or degraded transcript.
Automating post-call data extraction
Structured outputs are what separate a transcript from a post-call workflow. After processing a call, Gladia's audio-to-LLM pipeline returns speaker-attributed text with word-level timestamps, named entities (product names, account numbers, location references), text-based sentiment scores, a call summary, and action items in a single JSON response. That structure maps directly to CRM fields without a second API call to an LLM. Teams at Attention use Gladia as the core transcription layer powering CRM population, coaching scorecards, and conversation intelligence.
Integration cost in engineering time
Multiple Gladia customers independently report sub-24-hour integration to production. Lightweight Python and JavaScript SDKs and direct Slack access to Gladia engineers are the two most cited reasons.
Pricing models at scale
The most common mistake in STT pricing evaluation is comparing base rates without accounting for feature bundling. At 1,000 hours per month with diarization and entity extraction enabled, the effective hourly rate diverges significantly across providers.
Hidden costs and add-on fees
Tier 1 CI platform costs are seat-based, so they do not respond to audio volume increases. Large contact center teams on enterprise CI platforms can reach significant annual costs before platform fees, regardless of whether the team processes 1,000 or 100,000 hours of audio per month.
For Tier 2 APIs, the risk is stacked add-on fees. AssemblyAI's base rate is $0.15 per hour for Universal-2 ($0.21/hr for Universal-3 Pro), with intelligence add-ons such as entity detection and PII redaction stacking on top of the base rate per AssemblyAI's public add-on pricing structure. Deepgram's Nova-3 pricing is available on Deepgram's public pricing page; feature bundling and add-on structure should be verified directly before modeling production costs. OpenAI's transcription API costs $0.36 per hour with no native diarization or NER at any price point.
Total cost at 1K and 10K hours
| Provider |
1,000 hrs/mo (fully loaded) |
10,000 hrs/mo (fully loaded) |
Diarization included? |
NER included? |
| Gladia Growth |
~$200 |
~$2,000 |
Yes (async) |
Yes |
| AssemblyAI (full features) |
~$150/mo base + add-ons |
~$1,500/mo base + add-ons |
Varies by plan |
Add-on |
| Deepgram Nova-3 |
See Deepgram's public pricing page |
See Deepgram's public pricing page |
See public pricing page |
See public pricing page |
| OpenAI STT API |
$360 |
$3,600 |
Not available |
Not available |
All Gladia figures use the Growth plan rate of $0.20/hr for async, competitor figures use publicly listed base rates plus documented add-on structures.
How Gladia reduces after-call work with one API call
Gladia replaces the fragmented pipeline of separate recording, transcription, and enrichment vendors with a single API endpoint. One call handles capture, transcription, speaker attribution, entity extraction, sentiment scoring, and summarization, removing every data seam where information degrades between providers.
Integrated speaker identification
Our speaker diarization labels each transcript segment with a speaker identifier and confidence score with word-level timestamps attached. In a contact center context, this means agent turns and customer turns are separable in the output, enabling per-speaker sentiment scoring, script adherence checking, and agent-specific coaching triggers without any post-processing. Diarization is included in the base rate on Starter and Growth plans for async workflows.
Advanced audio intelligence features, no hidden fees
Diarization, translation, summarization, named entity recognition, text-based sentiment analysis, chapterization, and custom vocabulary are all included at the base rate on Starter and Growth plans. PII redaction is available but must be explicitly configured: it is not automatic or enabled by default.
On Growth and Enterprise plans, customer audio is never used to retrain models and no opt-out action is required. On the Starter plan, customer data may be used for model improvement purposes by default, which means teams handling sensitive customer conversations in regulated industries should select Growth or Enterprise plans. Our SOC 2 Type II, ISO 27001, HIPAA, and GDPR compliance covers EU and US region deployments with configurable data residency.
How Claap reduced API integration time
Claap, which builds video collaboration tools with multilingual meeting transcription, reports 1 to 3% WER in production and transcribes one hour of video in under 60 seconds across 99+ languages on Gladia's infrastructure. The integration was fast enough that the accuracy improvement was visible to end users within the same sprint cycle, and improved prospect conversion followed after switching from US-centric incumbents that underperformed on non-English audio.
Automate lead scoring: Gladia + Claude in action
The pattern below shows how our structured JSON output routes to Claude (Anthropic) for automated lead scoring and CRM sync. The audio-to-LLM pipeline documentation covers full integration options, including bring-your-own-model configuration.
import anthropic
import requests
# Step 1: Submit audio for async transcription with full intelligence suite
gladia_response = requests.post(
"https://api.gladia.io/v2/transcription",
headers={"x-gladia-key": "YOUR_API_KEY"},
json={
"audio_url": "https://your-storage.com/call-recording.wav",
"diarization": True,
"named_entity_recognition": True,
"sentiment_analysis": True,
"summarization": True,
"custom_vocabulary": ["your-product-name", "competitor-name"]
}
)
result_id = gladia_response.json()["id"]
# Step 2: Poll for completed transcript (or use webhook callback)
transcript_data = requests.get(
f"https://api.gladia.io/v2/transcription/{result_id}",
headers={"x-gladia-key": "YOUR_API_KEY"}
).json()
# transcript_data structure:
# {
# "utterances": [
# {"speaker": "agent", "text": "How can I help you today?", "start": 0.2, "end": 2.1, "sentiment": "positive"},
# {"speaker": "customer", "text": "I need to upgrade my plan.", "start": 2.5, "end": 5.3, "sentiment": "neutral"}
# ],
# "entities": [{"type": "PRODUCT", "value": "Enterprise Plan"}, {"type": "INTENT", "value": "upgrade"}],
# "summary": "Customer called to inquire about plan upgrade options.",
# "language": "en"
# }
# Step 3: Route structured output to Claude for lead scoring
claude_client = anthropic.Anthropic(api_key="YOUR_CLAUDE_KEY")
scoring_prompt = f"""
Given this call transcript and extracted entities, score the lead 1-10 on purchase intent
and identify the primary pain point for CRM logging.
Summary: {transcript_data['summary']}
Entities: {transcript_data['entities']}
Sentiment by turn: {[u['sentiment'] for u in transcript_data['utterances']]}
Return JSON: {{"lead_score": int, "primary_pain_point": str, "recommended_action": str}}
"""
scoring_result = claude_client.messages.create(
model="claude-opus-4-20250514"8",
max_tokens=256,
messages=[{"role": "user", "content": scoring_prompt}]
)
# Step 4: Push structured result to CRM
crm_payload = {
"call_summary": transcript_data["summary"],
"lead_score": scoring_result.content[0].text,
"entities_extracted": transcript_data["entities"],
"agent_id": "agent_turn_speaker_label"
}
# POST to CRM webhook endpoint
requests.post(
"https://your-crm.com/api/webhook",
headers={"Authorization": "Bearer YOUR_CRM_TOKEN"},
json=crm_payload
)
This pipeline delivers speaker-attributed transcripts, entity extraction, sentiment scoring, and an LLM-generated lead score in a single async workflow. The structured output maps directly to CRM fields without manual agent input, which is the point of ACW automation. Teams that want to validate the flow first can run it in the Gladia playground before writing any integration code.
Start with 10 free hours and test diarization, entity extraction, and multilingual handling on your own call center audio. Run your own cost model at 10,000 hours with all features enabled before committing to a plan.
FAQs
How long does STT API integration actually take for a production contact center pipeline?
Multiple Gladia customers report sub-24-hour integration to production using the Python or JavaScript SDK and standard REST endpoints. Teams migrating from Deepgram can use provider-specific migration guides that reduce the switch to configuration changes rather than architectural rewrites.
What does after-call work automation actually cost at 10,000 hours per month?
Our Growth plan processes 10,000 async hours for approximately $2,000 per month with diarization, NER, sentiment, and summarization all included. Tier 2 STT API competitors with similar feature sets typically cost more when fully loaded with intelligence features, while Tier 1 CI platforms run on per-seat pricing that can reach significant annual costs depending on team size.
Do I need separate vendors for diarization and transcription?
Not with Gladia. Speaker diarization powered by pyannoteAI's Precision-2 model is included in the base rate on Starter and Growth plans for async workflows, so there is no separate vendor, no separate billing line, and no pipeline seam where speaker attribution data can degrade.
What is a realistic WER for production contact center audio, not lab benchmarks?
Production deployments using Gladia have reported WER in the 1 to 3% range across 99+ languages, and our async benchmark tests against eight providers on 74+ hours across seven datasets with open and reproducible methodology, showing lower WER on conversational speech versus alternatives.
Key terms glossary
After-call work (ACW): The time an agent spends completing post-interaction tasks such as CRM updates, disposition coding, notes documentation, and follow-up scheduling. Measured in seconds per call and directly impacts contact center capacity.
Word error rate (WER): The percentage of words in a transcript that differ from the correct reference transcription, calculated as insertions plus deletions plus substitutions divided by total reference words. Lower WER means higher transcription accuracy.
Diarization error rate (DER): A metric measuring how accurately a system attributes spoken audio to the correct speaker, combining missed speech, false alarm speech, and speaker confusion errors. Lower DER means more reliable "who spoke when" attribution.
Code-switching: Mid-conversation language changes where a speaker shifts between two or more languages within a single turn or across turns in the same call. Legacy ASR models typically fail silently when this occurs.
Asynchronous (batch) transcription: A workflow where a complete audio file is submitted and processed as a single unit after recording is complete, enabling full-context accuracy, diarization, and multilingual handling with processing times typically under one minute per ten minutes of audio.
Total cost of ownership (TCO): The full cost of a vendor relationship at a given scale, including base API rates, feature add-on fees, infrastructure overhead, and engineering maintenance time, calculated at realistic usage volumes.
Speaker diarization: The process of segmenting an audio recording and attributing each segment to the individual speaker who produced it, enabling per-speaker analysis of transcript content.
