Deepgram Enterprise Voice AI vs Sesame

Comparing the features of Deepgram Enterprise Voice AI to Sesame

Feature
Deepgram Enterprise Voice AI
Sesame

Capability Features

Audio Intelligence API
Batch Processing
Cloud Deployment
Consistent Personality
Context Awareness
Context Maintenance
Conversational Dynamics
Conversational Speech Generation
Custom Business Logic
Custom Models
Dataset Size
1 million hours
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Emotional Intelligence
English Language Support
Evaluation Suite
File Upload Support
Function Calling
LLM Orchestration
Model Sizes
Tiny: 1B backbone, 100M decoderSmall: 3B backbone, 250M decoderMedium: 8B backbone, 300M decoder
Multiple Speaker Handling
Objective Metrics
Word Error RateSpeaker SimilarityHomograph DisambiguationPronunciation Consistency
Partial Multilingual Support Planned
Planned for 20+ languages
Prompt Updates
Pronunciation Correction
Real-time Transcription
Response Injection
Self-hosted Deployment
Sequence Length
2048
Single-Stage Model
Speak Input
Speech to Text
Subjective Metrics
Comparative Mean Opinion Score
System Updates for AI
Text and Audio Input
TextAudio
Text to Speech
Training Epochs
5
Transcripts Copy
Voice Agent

Integration Features

API Integrations
External System Integration
GitHub Release
LLama Architecture Backbone
Mimi Split-RVQ Tokenizer
Partner Integrations
Telephony partners (via PSTN/SIP)

Limitation Features

Cannot Model Conversation Structure
English Language Dominance
Memory Bottleneck in Training
No Explicit Quota Limits
No File Format Support Listed
No Plugin Marketplace
No Pre-trained Language Model Use
No Pricing Details Listed
Real-Time Generation Delay
RVQ time-to-first-audio scales poorly

Pricing Features

Free Preview
Free Tier
Open Source
Apache 2.0