The Complete Guide to Choosing STT, LLM, and TTS Providers for Your Voice AI Stack
Voice AI systems use a modular architecture consisting of three distinct components: speech-to-text (STT) transcription, large language model (LLM) processing, and text-to-speech (TTS) synthesis. Choosing the right combination of STT, LLM, and TTS providers determines your voice agent's accuracy, latency, cost, and language capabilities. Vapi is an orchestration layer over these three modules, allowing developers to swap providers based on specific use case requirements without changing underlying infrastructure.
Understanding the Modular Voice AI Architecture
The Sandwich architecture separates voice AI into three independent layers that can be mixed and matched. This modularity provides flexibility that end-to-end solutions lack, enabling optimization for specific use cases.
Speech-to-text converts spoken audio into text that language models can process. The LLM layer analyzes transcribed text, understands intent, maintains conversation context, and generates appropriate responses. Text-to-speech converts the LLM's text response into natural-sounding audio delivered to users.
Each layer has multiple provider options with different strengths. A customer support agent might use Deepgram (fast STT) + GPT-4 (capable reasoning) + ElevenLabs (natural voices), while a cost-optimized application might choose OpenAI Whisper (free) + GPT-3.5 (affordable) + OpenAI TTS (bundled pricing).
Speech-to-Text Provider Comparison
Deepgram
Deepgram delivers industry-leading speed with 200-300ms transcription latency and 95-98% accuracy across diverse accents. Streaming architecture enables real-time transcription that begins before users finish speaking.
Best for: Low-latency applications like customer support and sales where fast response times are critical
Pricing: $0.0043 per minute for Nova-2 model
Languages: 36 languages including English, Spanish, French, German, Japanese
Unique capability: Custom model training for industry-specific vocabulary
AssemblyAI
AssemblyAI provides 96-99% accuracy with superior performance on technical terms, proper nouns, and complex vocabulary. Processing takes 300-400ms with comprehensive speaker diarization and sentiment analysis.
Best for: Applications requiring high accuracy with technical or medical terminology
Pricing: $0.00025 per second ($0.015 per minute)
Languages: 30+ languages with automatic language detection
Unique capability: Advanced audio intelligence including topic detection and content moderation
OpenAI Whisper
Whisper excels at handling diverse accents, noisy environments, and multilingual content. The large-v3 model supports 97 languages with robust accuracy but adds 400-600ms latency in API mode.
Best for: Multilingual applications and noisy audio environments
Pricing: $0.006 per minute
Languages: 97+ languages with automatic detection
Unique capability: Exceptional multilingual performance and accent robustness
Google Speech-to-Text
Google Cloud Speech-to-Text provides 95-97% accuracy with 120+ languages and automatic punctuation. Latency ranges from 300-500ms depending on region and model selection.
Best for: Enterprise applications requiring broad language coverage
Pricing: $0.006 per 15 seconds for standard model
Languages: 125+ languages and variants
Unique capability: Automatic punctuation and word-level timestamps
Comparison Matrix
| Provider | Latency | Accuracy | Languages | Best Use Case |
|---|---|---|---|---|
| Deepgram | 200-300ms | 95-98% | 36 | Low-latency conversations |
| AssemblyAI | 300-400ms | 96-99% | 30+ | Technical vocabulary |
| OpenAI Whisper | 400-600ms | 95-97% | 97+ | Multilingual, noisy audio |
| Google STT | 300-500ms | 95-97% | 125+ | Enterprise scale |
Language Model Selection
OpenAI GPT-4
GPT-4 provides the most capable reasoning with nuanced conversation handling, complex problem-solving, and strong instruction following. Inference latency ranges from 400-800ms depending on prompt complexity.
Best for: Complex conversations requiring sophisticated reasoning
Pricing: $2.50 per 1M input tokens, $10.00 per 1M output tokens
Context window: 128K tokens
Unique capability: Superior reasoning and multi-step problem solving
OpenAI GPT-3.5-turbo
GPT-3.5-turbo delivers fast responses (200-400ms) with solid quality for most conversational use cases. Cost efficiency makes it ideal for high-volume applications.
Best for: Cost-optimized, high-volume applications
Pricing: $0.50 per 1M input tokens, $1.50 per 1M output tokens
Context window: 16K tokens
Unique capability: Best cost-performance ratio for standard conversations
Anthropic Claude
Claude offers 300-500ms latency with exceptional instruction following, nuanced conversational ability, and strong safety characteristics. Extended context windows support long conversations.
Best for: Conversations requiring nuance and long context
Pricing: $3.00 per 1M input tokens, $15.00 per 1M output tokens (Claude 3.5 Sonnet)
Context window: 200K tokens
Unique capability: Extended context and nuanced conversation handling
Google Gemini
Gemini provides competitive performance with strong multilingual capabilities and tight integration with Google Cloud services. Latency ranges from 350-550ms.
Best for: Google Cloud ecosystems and multilingual applications
Pricing: $0.50 per 1M input tokens, $1.50 per 1M output tokens (Gemini Pro)
Context window: 32K tokens
Unique capability: Native Google Workspace integration
Open Source Models
Llama 3, Mistral, and other open models provide full control and potentially lower costs for high-volume deployments. Self-hosting requires infrastructure management but eliminates per-token costs.
Best for: High-volume deployments with custom requirements
Pricing: Infrastructure costs only (no per-token fees)
Unique capability: Complete customization and data privacy
Text-to-Speech Provider Comparison
ElevenLabs
ElevenLabs generates highly realistic voices with emotional nuance and natural prosody. First-chunk latency of 150-250ms enables responsive conversations. Voice cloning creates custom voices from sample audio.
Best for: Applications prioritizing voice quality and naturalness
Pricing: $0.18 per 1K characters ($0.30 per minute of audio)
Languages: 29 languages
Unique capability: Voice cloning and emotional range control
PlayHT
PlayHT offers extensive voice libraries with 600+ voices across 142 languages. Fine-grained control over speech rate, pitch, and pronunciation. Latency ranges from 200-300ms.
Best for: Applications requiring diverse voice options
Pricing: $0.12 per 1K characters ($0.20 per minute)
Languages: 142 languages
Unique capability: Largest voice library and language coverage
OpenAI TTS
OpenAI TTS provides solid quality with 250-400ms latency and simple API integration. Six pre-built voices with cost efficiency for high-volume deployments.
Best for: Cost-efficient, high-volume applications
Pricing: $15.00 per 1M characters ($0.015 per 1K characters)
Languages: 57 languages
Unique capability: Bundled pricing with OpenAI LLM services
Amazon Polly
Amazon Polly delivers 300-450ms latency with 60+ voices across 30+ languages. Neural TTS quality is good but not industry-leading. AWS ecosystem integration simplifies deployment.
Best for: AWS-based infrastructure deployments
Pricing: $4.00 per 1M characters for neural voices
Languages: 30+ languages
Unique capability: Deep AWS integration and SSML support
Comparison Matrix
| Provider | Latency | Voice Quality | Languages | Best Use Case |
|---|---|---|---|---|
| ElevenLabs | 150-250ms | Excellent | 29 | Quality-focused applications |
| PlayHT | 200-300ms | Very Good | 142 | Multilingual deployments |
| OpenAI TTS | 250-400ms | Good | 57 | Cost-efficient scale |
| Amazon Polly | 300-450ms | Good | 30+ | AWS ecosystems |
Choosing Providers Based on Your Use Case
Customer Support Agent
Requirements: Fast response, good accuracy, natural voice
Recommended stack: Deepgram STT + GPT-4 + ElevenLabs
Rationale: Speed critical for frustrated customers, GPT-4 handles complex issues, ElevenLabs provides empathetic voice quality
Sales Outreach Agent
Requirements: Low latency, conversational ability, professional voice
Recommended stack: Deepgram STT + Claude + PlayHT
Rationale: Fast responses build rapport, Claude excels at persuasive conversation, PlayHT offers diverse professional voices
Appointment Scheduling Agent
Requirements: Cost efficiency, basic conversation, clear voice
Recommended stack: Deepgram STT + GPT-3.5-turbo + OpenAI TTS
Rationale: Transactional task doesn't require GPT-4, cost optimization for high volume, OpenAI TTS clarity sufficient
Multilingual Information Hotline
Requirements: Language coverage, accent handling, cost control
Recommended stack: OpenAI Whisper STT + GPT-3.5-turbo + PlayHT
Rationale: Whisper handles 97+ languages, GPT-3.5 manages costs, PlayHT covers 142 languages
Technical Support Agent
Requirements: Terminology accuracy, reasoning capability, documentation
Recommended stack: AssemblyAI STT + GPT-4 + ElevenLabs
Rationale: AssemblyAI handles technical terms, GPT-4 solves complex problems, ElevenLabs clarity for instructions
How Vapi's Orchestration Layer Simplifies Provider Management
Traditional voice AI development requires manual integration of each provider through separate APIs, SDK management, authentication flows, and format conversion. Developers write custom code to handle streaming connections, error recovery, and failover.
Vapi provides unified API access to all STT, LLM, and TTS providers through a single integration point. Switch from Deepgram to AssemblyAI by changing one configuration parameter, not rewriting integration code. Vapi handles authentication, streaming, error handling, and provider-specific quirks.
The orchestration layer enables rapid experimentation. Test GPT-4 vs Claude vs GPT-3.5 on production traffic through dashboard configuration without code changes. Run A/B tests comparing ElevenLabs vs PlayHT voices to measure user preference and conversation completion rates.
Cost Optimization Strategies
Dynamic Provider Selection
Route calls to different providers based on detected characteristics. Use expensive high-quality providers for critical calls and cost-optimized providers for routine interactions.
Example: Sentiment analysis detects frustrated customer → route to GPT-4 + ElevenLabs. Routine appointment booking → route to GPT-3.5 + OpenAI TTS.
Prompt Engineering for Token Efficiency
Reduce LLM costs by optimizing prompts for conciseness. Every 100 tokens eliminated from system prompts saves $0.00025 per conversation with GPT-4.
Avoid verbose examples and excessive context. Use structured outputs only when necessary. Stream responses to enable interruption, preventing payment for tokens users never hear.
Voice Caching and Reuse
Cache TTS audio for frequently repeated phrases. Greetings, disclaimers, and common responses don't require synthesis on every call.
Example: "Thank you for calling. How can I help you today?" synthesized once and reused across thousands of calls saves $0.05 per 1000 conversations.
Provider Reliability and Failover
Production voice AI systems require provider failover to maintain uptime during outages. Vapi's orchestration layer automatically switches providers when primary services become unavailable.
Configure failover chains: Primary Deepgram → Failover to AssemblyAI → Final fallback to Whisper. Users experience seamless conversations even during provider outages.
Monitor provider status through dashboard alerting. Real-time latency tracking and error rate monitoring identify degraded providers before they impact user experience.
Future-Proofing Your Voice AI Stack
Voice AI technology evolves rapidly. New providers launch, existing providers update models, and pricing fluctuates. Modular architecture through Vapi's orchestration layer protects against vendor lock-in.
When a new STT provider offers superior accuracy or lower cost, switching requires configuration changes rather than code rewrites. This architectural flexibility enables continuous optimization as the competitive landscape evolves.
Multimodal models like GPT-4o that process audio natively may eventually replace the STT → LLM → TTS pipeline entirely. Vapi's architecture will support these next-generation models alongside current providers, enabling gradual migration rather than wholesale replacement.
Frequently Asked Questions
What is the best STT provider for voice AI?
The best STT provider depends on use case requirements. Deepgram offers the fastest transcription at 200-300ms latency, ideal for low-latency applications. AssemblyAI provides 96-99% accuracy for technical vocabulary. OpenAI Whisper handles 97+ languages and noisy environments. Production deployments should test multiple providers on real audio to identify the best fit for specific use cases.
Should I use GPT-4 or GPT-3.5 for my voice AI agent?
Use GPT-4 for voice agents requiring sophisticated reasoning, complex problem-solving, or nuanced conversation handling. GPT-3.5-turbo works well for transactional conversations like appointment scheduling or information lookup where speed and cost matter more than reasoning depth. GPT-4 costs 5x more and adds 200-400ms latency compared to GPT-3.5, so choose based on whether use case justifies the tradeoff.
How do I switch providers in Vapi?
Vapi enables provider switching through dashboard configuration without code changes. Navigate to your agent's settings, select the layer you want to modify (STT, LLM, or TTS), choose a new provider from the dropdown, and save. Changes apply immediately to new conversations. This orchestration layer eliminates manual API integration, authentication, and format conversion required when switching providers directly.
What does a complete voice AI stack cost per minute?
Voice AI costs range from $0.05 to $0.15 per conversation minute depending on provider selection. A cost-optimized stack using Deepgram STT ($0.0043/min) + GPT-3.5 ($0.002/min equivalent) + OpenAI TTS ($0.015/min equivalent) costs approximately $0.05 per minute. A quality-optimized stack using AssemblyAI ($0.015/min) + GPT-4 ($0.02/min equivalent) + ElevenLabs ($0.30/min) costs approximately $0.15 per minute.
Can I use different providers for different languages?
Yes, Vapi supports language-specific provider routing. Configure your agent to use OpenAI Whisper STT for non-English languages (superior multilingual performance) and Deepgram for English (faster). PlayHT TTS handles 142 languages, while ElevenLabs covers 29 with higher quality. Dynamic routing based on detected language optimizes both cost and quality across multilingual deployments.
Do I need to integrate each provider's API separately?
No, Vapi's orchestration layer provides unified API access to all STT, LLM, and TTS providers through a single integration point. Vapi handles provider authentication, streaming connections, format conversion, error recovery, and failover. Developers configure provider selection through dashboard or API without writing provider-specific integration code, significantly reducing development time and maintenance burden.
How do I test different provider combinations?
Test provider combinations through Vapi's dashboard by creating multiple agent configurations with different STT, LLM, and TTS selections. Run A/B tests splitting production traffic between configurations. Monitor metrics including latency (P50, P95, P99), conversation completion rate, user satisfaction, and cost per conversation. Dashboard analytics show comparative performance enabling data-driven provider selection for your specific use case and user base.
What happens if my STT provider goes down?
Vapi's orchestration layer supports automatic failover between STT providers. Configure a failover chain specifying primary, secondary, and tertiary providers. When the primary provider becomes unavailable due to outage or elevated error rates, Vapi automatically routes requests to the next provider in the chain. Users experience seamless conversations without manual intervention, maintaining uptime during provider incidents.