Accurate speech-to-text and speech AI for production apps
AssemblyAI is a developer-focused Speech-to-Text and speech AI API that converts audio to text, extracts insights, and powers voice features for apps; it’s ideal for engineering teams and data scientists who need high-accuracy transcription, diarization, and content moderation at scale, and its pricing includes a limited free tier with pay-as-you-go paid plans and enterprise volume discounts.
AssemblyAI is a speech and voice AI platform that delivers automatic speech recognition (ASR), transcription, and downstream speech intelligence via API. The service focuses on high-accuracy transcription, speaker diarization, and semantic features like topic detection and sentiment for audio analysis. AssemblyAI distinguishes itself by offering production-ready SDKs and a model-first API tailored for developers, data teams, and enterprises needing scale. The platform runs as a Voice & Speech solution with a freemium entry point and pay-as-you-go pricing, making it accessible for testing before committing to larger volumes.
AssemblyAI is a cloud-first speech and audio intelligence platform founded in 2017 and positioned as a developer-centric provider of automatic speech recognition (ASR) and downstream speech analysis. Its core value proposition is to turn audio and video into structured text and metadata at scale via a REST API and SDKs, enabling companies to add transcription, content moderation, and conversational features without building ML stacks in-house. AssemblyAI emphasizes model updates, latency options, and enterprise-grade security and compliance for audio processing workflows.
The service offers several distinct capabilities. First, ASR/transcription supports multiple sampling rates and long-form audio, with speaker diarization and timestamps. Second, it provides conversation intelligence features such as topic detection, auto-generated summaries (highlights and full summaries), and sentiment analysis that extract semantic labels and structured JSON outputs for downstream use. Third, it includes content moderation and PII redaction tools—such as profanity filtering and automatic PII detection/removal—useful for compliance. Fourth, AssemblyAI exposes streaming and batch APIs, real-time WebSocket streaming for low-latency transcription, and prebuilt SDKs (Python, Node) that simplify integration into production applications.
AssemblyAI’s pricing is usage-based with a free tier suitable for evaluation. The free tier provides limited free transcription minutes per month (historically around a few hundred minutes for trial) and access to core models; paid usage follows a per-minute model that varies by model and features used, such as advanced speech intelligence (summarization, diarization, content moderation) which carry additional per-minute charges. Enterprise and custom plans are available for high-volume customers and include dedicated SLAs, custom model training and fine-tuning, and invoiced contractual terms. AssemblyAI also publishes per-minute pricing and charges separately for real-time streaming versus batch transcription, and volume discounts apply for large monthly commitments.
Common users include engineering and product teams building voice features, analysts extracting insights from call recordings, and compliance teams wanting content moderation. For example, a Product Manager at a fintech firm uses AssemblyAI to transcribe and redact PII from support calls to reduce compliance risk, while a Data Scientist at a media company uses topic detection and summarization to generate searchable metadata across thousands of hours of interviews. Compared with competitors like Google Cloud Speech-to-Text, AssemblyAI differentiates by packaging conversation intelligence features and text-level moderation in the same API surface, appealing to teams wanting a single vendor for transcription plus semantic analysis.
Three capabilities that set AssemblyAI apart from its nearest competitors.
Current tiers and what you get at each price point. Verified against the vendor's pricing page.
| Plan | Price | What you get | Best for |
|---|---|---|---|
| Free | Free | Limited free transcription minutes for evaluation, API access to core models | Developers testing core ASR capabilities |
| Pay-as-you-go | Varies (per-minute pricing) | Per-minute billing; advanced features (summaries, diarization) billed higher | Small teams with variable monthly usage |
| Enterprise | Custom | Contracted SLA, volume discounts, custom models, dedicated support | Large organizations with heavy, compliant workloads |
Copy these into AssemblyAI as-is. Each targets a different high-value workflow.
You are an automated transcription assistant. Task: transcribe the provided audio and redact all personally identifiable information (PII). Constraints: 1) Replace each PII token with [REDACTED_TYPE] where TYPE is one of NAME, PHONE, EMAIL, SSN, ADDRESS, CREDIT_CARD; 2) Preserve non-PII speech and punctuation; 3) Provide original timestamps for each redaction. Output format: JSON with fields: transcript (redacted full text), redactions (array of {type, original_text, start_time, end_time}). Example: {redactions:[{type:PHONE, original_text:'(555) 123-4567', start_time:12.3, end_time:12.9}]}. Return only valid JSON.
You are a content-moderation assistant. Task: analyze the provided audio transcript and identify profanity, hate speech, sexual content, threats, and self-harm mentions. Constraints: 1) For each finding output category, severity (low/medium/high), exact text, start_time, end_time, and a short rationale; 2) Aggregate counts per category and top 3 repeated phrases; 3) Do not alter non-flagged text. Output format: return a JSON object: {summary:{counts...}, findings:[{category,severity,text,start_time,end_time,rationale}], top_phrases:[]}. Return only JSON.
You are a transcription engineer. Task: produce a speaker-diarized transcript with short segments. Constraints: 1) Label speakers as Speaker 1, Speaker 2, etc., and merge same-speaker adjacent segments; 2) Segment length must be <=30 seconds each and include start_time and end_time; 3) Exclude filler-only segments shorter than 1.5 seconds. Output format: JSON array of segments: [{speaker:'Speaker 1', start_time:0.0, end_time:7.2, text:'...'}, ...]. Also include metadata: total_duration, speaker_count. Return only JSON, no extra commentary.
You are an interview summarizer. Task: convert the audio into 8 concise highlights for indexing. Constraints: 1) Produce exactly 8 highlights, each 20-40 words; 2) For each highlight include start_time and end_time, 1-2 topic tags, and sentiment (positive/neutral/negative); 3) Avoid subjective wording and base each highlight on explicit spoken content. Output format: JSON array of 8 objects: [{highlight:'...', start_time:12.5, end_time:19.0, topics:['hiring','compensation'], sentiment:'neutral'}, ...]. Return only JSON.
You are a senior QA analyst. Multi-step task: 1) Transcribe the call and identify agent vs customer turns; 2) Score the call on five rubric categories (Greeting, Issue Clarification, Product Knowledge, Compliance, Empathy) using 0-5 integers and brief justification (one sentence each); 3) Provide top three coaching actions tailored to the agent and one compliance risk if present. Constraints: output a single CSV row for this call with columns: call_id,agent_id,greeting_score,clarification_score,product_score,compliance_score,empathy_score,greeting_note,clarification_note,product_note,compliance_note,empathy_note,coaching_actions(combined),compliance_risk. Example row given: CALL123,AGENT42,5,4,4,3,5,"Good greeting","Clarified need","Accurate product info","Missed disclosure","Empathetic","Action1; Action2; Action3","Missed mandatory disclosure". Return only CSV-compatible output (one row).
You are a data engineer preparing ASR training manifests. Multi-step task: 1) Transcribe audio; 2) Split into speaker-homogeneous segments no longer than 10 seconds; 3) Remove or redact PII; 4) Assign intent/label per segment (e.g., question, affirmation, negative), and include model confidence. Output format: CSV manifest rows with columns: source_uri,start_time,end_time,speaker,transcript,label,confidence. Provide three example rows as few-shot examples: s3://bucket/call1.wav,0.0,3.2,Agent,"Hello, how can I help?",question,0.98; s3://bucket/call1.wav,3.2,6.1,Customer,"My internet is down",problem_report,0.95; s3://bucket/call1.wav,6.1,9.0,Agent,"I'll run a diagnostic",action,0.93. Return only CSV rows for all segments.
Choose AssemblyAI over Google Cloud Speech-to-Text if you want integrated conversation intelligence (summaries, topics, PII redaction) in one API.
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