Build & iterate with speed and full control
Cake’s rapid prototyping stack is designed for developers who want to move fast without cutting corners. Easily plug in best-in-class, pure-play providers for Speech-to-Text (Deepgram, Daily.co) and Text-to-Speech (ElevenLabs, Cartesia), while maintaining flexible orchestration via the Cake Voice Builder.
Version-controlled prompts and agent configurations are managed through LangFuse, so every iteration is traceable and reproducible. With LiteLLM proxying your model of choice (OpenAI, Gemini, Anthropic), and built-in observability via Grafana and LangFuse, you get fast feedback loops and total visibility—all without vendor lock-in.
Scale fast without breaking the bank
With Ray powering orchestration, Cake lets you scale from zero to hundreds of concurrent voice agents at a fraction of the cost of traditional voice SaaS platforms.
LiteLLM proxies your model of choice while LangFuse and Grafana give you real-time observability into performance, usage, and cost. You can pair it with best-in-class tools for transcription and speech like Deepgram, ElevenLabs, and Cartesia to deliver high-quality voice interactions at scale.
Run large deployments confidently, without vendor lock-in or operational sprawl.
Bring voice to your data, not your data to someone else’s voice
With Cake, your voice agents connect directly to your own vector stores like Milvus, Weaviate, or pgvector. Everything runs in your cluster, so your data stays secure and fully under your control. No handoffs, no vendor lock-in, and no walled gardens.
Keeping all components in the same environment also reduces latency. Your agent can access relevant context faster, which leads to smoother conversations and better customer experiences. This is voice RAG built for teams that care about performance, privacy, and ownership.
Keep everything on your terms, including inference
In regulated industries like finance, healthcare, or insurance, sending data to external APIs might not be an option. With Cake, you can fully self-host your LLMs using frameworks like vLLM or LLaMA, keeping all inference inside your environment
This setup eliminates external ingress or egress entirely. You can proxy to your own hosted models through LiteLLM, or route calls to cloud services like Azure OpenAI or Bedrock—without rewriting your stack. It’s flexible, secure, and built for teams that need to keep sensitive data in place.
Balance performance and flexibility for production voice agents
When it comes to voice, latency matters. Hybrid inference lets you keep critical LLM calls in-cluster for faster response times while still routing to external providers like OpenAI or Anthropic when needed.
With LiteLLM handling the proxy logic, you can mix hosted and self-hosted models—LLaMA, vLLM, Azure OpenAI, and more—without changing your application logic. This setup gives you the flexibility to optimize for speed, cost, and control as you scale voice agents into production.
Run telephony at scale on your own infrastructure
For organizations running high-volume voice agents, self-hosting telephony and LLM inference can dramatically reduce costs and improve reliability. This setup lets you bring your own carrier and manage call flows directly within your cluster—no external voice dependencies required.
By keeping inference local with models like LLaMA or vLLM, you eliminate third-party LLM fees while maintaining full control over performance and privacy. It’s a streamlined, affordable stack built for scale, flexibility, and long-term sustainability.
Customer support automation
Deploy AI voice agents that handle high call volumes while maintaining high-quality service and reducing costs.
Outbound sales
Automate repetitive outreach tasks to boost productivity, increase conversion rates, and free up human teams for higher-value work.
Virtual receptionists
Provide 24/7 phone coverage without the expense of round-the-clock staff, improving responsiveness and customer satisfaction.
Order processing & status updates
Automate inbound calls for order placement, tracking, and status updates in industries such as retail, food delivery, or logistics.
Appointment assistant
Send proactive reminders, reschedule appointments, or confirm bookings without human intervention, reducing no-shows and cancellations.
Internal helpdesk automation
Handle routine employee requests such as password resets, benefits inquiries, or system troubleshooting without tying up internal teams.
Blog
Build vs. Buy for Voice Agents: A Practical Guide
Understand the "build vs. buy" decision when it comes to voice agents with this practical guide, exploring costs, customization, and performance to find the best fit for your business.
Editorial
The 5 Paths to Enterprise AI (And Why Most Lead to Trouble)
Cake CTO and co-founder Skye Thomas explores why not all AI development journeys are created equal. See how five common strategies stack up in cost, control, and innovation speed—plus which one sets you up to win.
Scott Stafford
Chief Enterprise Architect at Ping
CEO
InsureTech Company
Felix Baldauf-Lenschen
CEO and Founder
How to Build an AI Customer Service Agent: A Practical Guide
The idea of creating an AI agent from scratch can feel overwhelming, like a project reserved for massive tech companies with unlimited resources. But...
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