Pod AI vs Sesame

Comparing the features of Pod AI to Sesame

Feature
Pod AI
Sesame

Capability Features

All Calls Answered and Tracked
Answer Questions
Appointment Booking
Automated Deployment
Available 24/7
Bank-level Encryption
Call Analytics Dashboard
Check Status
Consistent Personality
Context Awareness
Conversational Dynamics
Conversational Speech Generation
Data Retention Policies
Dataset Size
1 million hours
Demo Available
Emotional Intelligence
End-to-End Encryption
Evaluation Suite
Full Data Control
Handle Requests
Inbound Call Support
Industry Compliance
Lead Qualification
Lead Qualification Calls
Model Sizes
Tiny: 1B backbone, 100M decoderSmall: 3B backbone, 250M decoderMedium: 8B backbone, 300M decoder
Multilingual Support
30
Multiple Speaker Handling
Natural Language Conversations
No Coding Required
No Phone Tree Menus
Objective Metrics
Word Error RateSpeaker SimilarityHomograph DisambiguationPronunciation Consistency
Outbound Engagement
Partial Multilingual Support Planned
Planned for 20+ languages
Process Payments
Pronunciation Correction
Schedule Appointments
Sequence Length
2048
Single-Stage Model
Smart Escalation to Humans
Smart Routing
Subjective Metrics
Comparative Mean Opinion Score
Support Call Handling
Text and Audio Input
TextAudio
Training Epochs
5
Transparent Privacy Practices
Verify Information
Web Demo

Integration Features

API Integrations
Calendar Integrations
CRM Integrations
Database Integration
GitHub Release
Knowledgebase Integration
LLama Architecture Backbone
Mimi Split-RVQ Tokenizer
Telephony System Integration
Zapier Supported Apps

Limitation Features

Cannot Model Conversation Structure
English Language Dominance
Memory Bottleneck in Training
No Free Tier
No Pre-trained Language Model Use
Pricing Information Missing
Real-Time Generation Delay
RVQ time-to-first-audio scales poorly

Pricing Features

Free Preview
Open Source
Apache 2.0