The Concept
Retell AI vs Bland AI vs Vapi
| Factor | Retell AI | Bland AI | Vapi |
|---|
| Management level | Fully managed | Fully managed | BYO-stack |
| LLM | Included | Included | BYO (your key) |
| TTS | Included | Included | BYO (your key) |
| Telephony | Included | Included | BYO (your Twilio) |
| Custom LLM | Optional (bring your own) | Limited | Required |
| Cost/min | $0.07-0.12 | $0.09-0.15 | $0.05 + providers |
| Best for | Enterprise, white-label | Quick deployment | Cost control |
| Customization | High (custom LLM, webhooks) | Medium | High |
What's the key difference between Retell AI and Vapi?
Retell AI includes LLM, TTS, and telephony — you don't need separate API keys for OpenAI, Cartesia, or Twilio. You just define the agent's prompt and Retell handles everything. Vapi requires you to bring your own LLM API key, TTS API key, and phone number. Retell costs more ($0.07-0.12/min vs Vapi's $0.05 + providers), but requires zero infrastructure management. Retell also offers custom LLM support (bring your own endpoint) for enterprise. Bland AI is similar to Retell but more focused on quick deployment with less customization.
Retell AI
import requests
def create_retell_agent():
"""Create a Retell AI voice agent."""
response = requests.post(
"https://api.retellai.com/create-agent",
headers={"Authorization": f"Bearer {RETELL_API_KEY}"},
json={
"agent_name": "DeVenture Airlines Agent",
"voice_model": "eleven_labs",
"voice_id": "rachel",
"llm_model": "gpt-4o-mini",
"system_prompt": "You are a DeVenture Airlines voice agent. Keep responses to 1-3 sentences.",
"general_prompt": {
"task_description": "Help customers book flights and check flight status",
"conversation_flow": [
{"role": "system", "content": "Greet the customer"},
{"role": "system", "content": "Ask how you can help"},
{"role": "system", "content": "Process the request"},
{"role": "system", "content": "Confirm and close"}
]
},
"begin_message": "Hi, thanks for calling DeVenture Airlines. How can I help you?",
"end_message": "Thank you for calling DeVenture Airlines. Have a great day!",
"retell_llm_dynamic_variables": {
"company_name": "DeVenture Airlines",
"support_hours": "24/7"
}
}
)
return response.json()
Retell AI Custom LLM
For enterprise, Retell supports custom LLM endpoints:
{
"llm_model": "custom",
"llm_endpoint": "https://your-server.com/llm",
"system_prompt": "You are a voice agent...",
}
Your endpoint receives conversation context and returns the next response:
@app.post("/llm")
async def custom_llm(request):
"""Custom LLM endpoint for Retell AI."""
data = await request.json()
messages = data["messages"]
# Use any LLM you want (Claude, local model, etc.)
response = await call_your_llm(messages)
return {"response": response}
Bland AI
def create_bland_agent():
"""Create a Bland AI voice agent."""
response = requests.post(
"https://api.bland.ai/v1/calls",
headers={"Authorization": f"Bearer {BLAND_API_KEY}"},
json={
"phone_number": "+1234567890",
"pathway_id": "your-pathway-id", # or use prompt
"prompt": "You are a DeVenture Airlines voice agent. Help customers book flights.",
"voice": "josh",
"model": "gpt-4o-mini",
"first_sentence": "Hi, this is DeVenture Airlines calling about your booking.",
"wait_for_user": True,
"max_duration": 12, # minutes
}
)
return response.json()
Bland AI Pathways
Bland AI uses "pathways" — pre-built conversation flows:
| Pathway | Purpose |
|---|
| Customer Support | Handle support calls with escalation |
| Appointment Scheduling | Book/reschedule appointments |
| Survey | Conduct surveys with branching questions |
| Sales Outreach | Cold calls with qualification |
| Custom | Define your own flow |
Bland Pathway Flow
Retell vs Bland: When to Choose
| Your Need | Choose | Why |
|---|
| Enterprise, white-label | Retell | Custom LLM, more control |
| Quick deployment | Bland | Pathways, simpler API |
| Custom conversation flow | Retell | Webhooks + custom LLM |
| Pre-built flows | Bland | Pathways ready to use |
| Cost-sensitive | Retell | Slightly cheaper ($0.07 vs $0.09) |
| Analytics dashboard | Both | Both have good dashboards |