4d (edited) • General discussion
The Real Reason LiveKit Dashboards and DIY Setups Fail
I recently set up a LiveKit dashboard product to manage voice AI agents through a UI. After digging into how it actually works under the hood, I want to share what I found so others can make informed decisions.
My Experience
After a full day of troubleshooting the backend, the system is still not fully functional. Errors appear throughout the dashboard, and agent creation doesn't work reliably. What struck me most was the resource consumption - 10+ containers just to run a single test agent, and that agent still doesn't work correctly.
Why This Architecture Fails for Voice AI
When someone calls your business, they expect an immediate greeting. Not 2-5 seconds of silence while processes spawn.
The system spawns agents on demand instead of keeping them running. Every layer adds latency. Voice AI should have agents already running and waiting for calls.
Other problems I found:
- Single point of failure - one container runs everything
- No process management - no health checks, no automatic restarts
- Resource contention - all agents share the same memory and CPU
- Dynamic code generation - agents created on the fly with no version control
- Registry dependency - if the developer's server goes down, you can't rebuild
What Actually Works
I run a production voice AI system that handles real calls for my business. The key differences:
- Persistent processes - agents stay running, handle many calls
- Proper process management - automatic restarts, monitoring, per-agent logs
- Pre-loaded dependencies - everything initialized at startup, not per-call
- Independent agents - one agent's issues don't affect others
- Direct control - I own all the code, no external dependency
My agents answer immediately because they're already running and waiting.
Credit Where It's Due
The developer was responsive and helped troubleshoot. The UI shows real effort, and the concept is appealing. But building production voice AI requires more than coding skills. Understanding how real-time systems function under load, how processes should be managed, and what architecture choices impact latency - these come from experience. A good idea isn't enough if the foundation doesn't support the use case.
The Intended Use Case Won't Work
This was designed for developers who want to create and resell voice agents. The problem is the architecture doesn't support it:
- Multiple clients = resource explosion
- No tenant isolation
- No reliability for SLAs
- Debugging is a nightmare in shared logs
If you're a developer hoping to build a voice AI business on this, the foundation won't hold. If you're a business owner looking for a simple way to deploy voice agents, you'll spend more time troubleshooting than using it.
Before You Buy Any LiveKit Dashboard Ask these questions:
- How are agents executed? (Persistent vs spawned on demand)
- What process manager is used?
- Are agents isolated or sharing resources?
- Do you own the code or depend on their registry?
- Can you see and edit the actual agent code?
If the answers are vague, walk away.
Bottom Line - Don't waste time on dashboard products that look pretty but have architectural problems underneath. Voice AI requires infrastructure built for latency and reliability.
Happy to answer questions if anyone wants to discuss.
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The Real Reason LiveKit Dashboards and DIY Setups Fail
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