August 28, 2025
Comments Off on 3 Ways to Deploy Voice AI Agents: Managed Services, Managed Compute, and Self-Hosted

Voice AI agents have unique deployment needs. Operational complexity multiplies quickly. You’re not just deploying code; you’re orchestrating real-time audio pipelines that need to maintain call quality under load, coordinate between AI services that each have their own scaling characteristics, and handle the networking complexities of audio delivery across diverse client environments.

When choosing a deployment strategy, you’ll find three main options, each making different trade-offs:

  • Managed services handle all infrastructure but limit customization 
  • Serverless compute platforms for AI provide optimized hosting with flexibility but require platform-specific setup
  • Cloud/Self-hosted solutions offer maximum control but require significant DevOps expertise

We’ll walk through all three using a real multilingual language learning agent, showing you the actual deployment steps, conversational AI infrastructure requirements, and costs. By the end, you’ll be able to choose an approach that fits your project’s timeline, technical requirements, and growth plans.

Unique Aspects of Deploying Voice AI Agents

Traditional IT infrastructure wasn’t designed to handle these Voice AI agent operational realities:

  • Real-time processing requirements increase infrastructure complexity compared to standard web applications
  • Multi-service coordination between Speech-To-Text (STT), Large Language Models (LLM) and Text-to-Speech services, creates complex dependency management
  • Concurrent conversation scaling demands dynamic resource allocation that can spike unexpectedly
  • Audio quality requirements requires specialized networking and latency optimization
  • Compliance and security concerns multiply with voice data handling across multiple AI services

To address these challenges, you’ll need to choose between three deployment approaches that balance complexity, control, and cost differently.

Our Demo AI Agent Architecture

For this analysis, we will use a multilingual language learning AI companion built with:

On the Twilio Platform, users’ calls to a phone number are directed to a POST endpoint. This endpoint returns a TwiML noun, instructing the platform to send raw media packets to a websocket endpoint. An agent’s audio pipeline (composed of multiple external AI services) processes these packets. The resulting response media packets are then transmitted back to Twilio.

Our Demo AI Agent Workflow
Our Demo AI Agent Workflow

The agent helps English speakers practice other languages through natural conversation, providing corrections and encouragement in real-time. The complete code of the agent is available in the voice-ai-agent-example repository on the WebRTC.ventures Github. 

Now let’s see the multiple ways you can deploy this agent.

Deployment Level I: Voice AI Agents as a Service

The simplest deployment approach uses platforms like Vapi that abstract away infrastructure complexity entirely. You focus purely on agent logic while the platform handles:

  • Audio streaming infrastructure
  • Service orchestration
  • Scaling and load balancing
  • Telephony integration

As a matter of fact, you don’t even need our code example here, the first step simply involves logging into the Vapi Dashboard. From there, you can easily create and configure your assistant using the intuitive point-and-click interface.

Overview of model configuration for the Language Learning AI Assistant
Overview of model configuration for the Language Learning AI Assistant

In addition to configuring the prompt and desired LLM, you also set the configuration for the Voice and Transcriber services (TTS and STT models, respectively).

Voice and Transcriber configuration for the AI Assistant
Voice and Transcriber configuration for the AI Assistant

Integrating with telephony is just a matter of provisioning (or importing) a phone number in Vapi and assigning it to the assistant.

Telephony integration for the AI Assistant
Telephony integration for the AI Assistant

And just like that you have a Voice AI agent ready to support your customers!

Pros:

  • Fastest time to market
  • Zero infrastructure management
  • Built-in telephony features
  • Automatic scaling

Cons:

  • Limited customization
  • Vendor lock-in
  • Higher per-minute costs
  • Less control over audio pipeline

Best for: Rapid prototyping, simple use cases, teams without DevOps expertise

Deployment Level II: Managed Compute for Voice AI Agents

Platforms like Cerebrium, Modal and Baseten provide managed compute resources specifically designed for AI workloads. You deploy your complete application while the platform handles infrastructure and scaling.

Deploying to Cerebrium

Our demo includes a complete Cerebrium configuration. The deployment process consists of:

# Install Cerebrium CLI
pip install cerebrium

# Login to platform
cerebrium login

# Configure secrets in Cerebrium dashboard:
# - OPENAI_API_KEY
# - DEEPGRAM_API_KEY  
# - ELEVENLABS_API_KEY
# - TWILIO_ACCOUNT_SID
# - TWILIO_AUTH_TOKEN

# Deploy with single command
cerebrium deploy

The cerebrium.toml configuration file defines how the platform automatically manages infrastructure details, application dependencies, scaling, and a custom entry point for the FastAPI application.

[cerebrium.deployment]
name = "cerebrium-demo"
python_version = "3.12"
docker_base_image_url = "debian:bookworm-slim"
disable_auth = true
include = ['main.py', 'bot.py', 'cerebrium.toml']
exclude = ['infrastructure/', 'Dockerfile', 'ecs-task-definition*.json', 
'*.sh', 'requirements.txt', '.gitignore', '.tool-versions']

[cerebrium.hardware]
cpu = 2.0
memory = 2.0
compute = "CPU"
provider = "aws"
region = "us-east-1"

[cerebrium.scaling]
min_replicas = 0
max_replicas = 2
cooldown = 30
replica_concurrency = 1
scaling_metric = "concurrency_utilization"

[cerebrium.dependencies.pip]
torch = ">=2.0.0"
"pipecat-ai[silero, daily, openai, deepgram, elevenlabs, twilio]" = "0.0.47"
aiohttp = ">=3.9.4"
torchaudio = ">=2.3.0"
channels = ">=4.0.0"
requests = "==2.32.2"
twilio = "latest"
fastapi = "latest"
uvicorn = "latest"
python-dotenv = "latest"
loguru = "latest"

[cerebrium.runtime.custom]
port = 8765
entrypoint = ["uvicorn", "main:app", "--host", "0.0.0.0", "--port", "8765"]
healthcheck_endpoint = "/health"

Once deployed, note the endpoint for POST requests, as this will be used for configuring Twilio telephony integration. Additionally, ensure a WEBSOCKET_URL secret is added in Cerebrium, set to the websocket endpoint in the format: wss://<your-cerebrium-endpoint>/ws.

Deploying to Cerebrium
Deploying to Cerebrium

Now you only need to point the voice configuration of your phone number in Twilio to the URL provided by Cerebrium.

Twilio Voice Configuration
Twilio Voice Configuration

Pros:

  • Full control over application code
  • Optimized for AI workloads
  • GPU support available
  • Simple deployment process

Cons:

  • Platform-specific configuration
  • Limited infrastructure customization
  • Potential cold start delays
  • Vendor dependency

Best for: AI-focused teams, applications requiring custom logic, moderate scale requirements

Deployment Level III: Deploy Your Own Infrastructure 

For maximum control and customization for your AI Agent, deploy to your own infrastructure using services like Amazon ECS. This approach gives you complete ownership of the deployment pipeline.

Deploying to Amazon ECS

Our demo includes complete Infrastructure-as-Code using OpenTofu and deployment scripts powered by AWS CLI tool. Note that since Twilio requires a secure WebSocket connection, you’ll need a custom domain for your agent and a public certificate in Amazon ACM that covers such domain in order to successfully provision this infrastructure:

# Configure AWS credentials
aws configure

# Deploy infrastructure
cd infrastructure
cp terraform.tfvars.example terraform.tfvars

# Edit terraform.tfvars with your API keys, ACM certificate ARN & custom domain

# Deploy infrastructure 
./deploy-infra.sh

# Deploy application
cd ..
./deploy.sh

# Get application load balancer URL and point your custom domain to it

Same as with Level II, after deployment you need to configure Voice settings in Twilio accordingly.

Pros:

  • Complete infrastructure control
  • Custom networking and security
  • Cost optimization opportunities
  • Integration with existing AWS services

Cons:

  • Complex setup and maintenance
  • Requires DevOps expertise
  • Longer deployment times
  • Infrastructure management overhead

Best for: Enterprise applications, strict security requirements, cost-sensitive deployments, teams with strong DevOps capabilities

Which Deployment Option Should You Choose?

The three deployment approaches represent different points on the complexity-control spectrum:

  • Level I (Vapi): Maximum simplicity, minimum control
  • Level II (Cerebrium): Balanced approach with AI-optimized infrastructure
  • Level III (ECS): Maximum control, maximum complexity

Choose based on your team’s expertise, timeline, and long-term requirements. For instance, you can start with Level I for rapid prototyping, then migrate to Level II or III as requirements evolve.

Whether you choose simplicity or control, ensure your platform can handle real-time audio processing, low-latency responses, and the coordination of multiple AI services that make voice AI agents truly conversational.

Ready to deploy voice AI agents for your business? 

Bring in the Voice AI implementation experts at WebRTC.ventures to assess the right deployment approach for your needs, recommend the best vendors and platforms, and handle the technical implementation to get your voice AI agents running smoothly in production, if desired. 

Contact us today to discuss your voice AI deployment strategy! 

Further Reading: 

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