Thanks — I’m responding to your question about what costs to plan for when self‑hosting LiveKit. Below are the main cost components you’ll want to budget and monitor, with practical notes to help you estimate each one. • Compute (CPU & memory) ◦ LiveKit nodes (SFU) need enough CPU and RAM to handle real‑time audio/video forwarding and any mixing/transcoding. A reasonable starting guideline for agent-style workloads is around 4 CPU cores and 8 GB RAM per server, but your needs depend on participants, tracks per participant, and whether you do server-side processing. ◦ If you run multiple nodes for scale or HA, multiply accordingly and factor in spare capacity for peaks. • Bandwidth (data transfer) ◦ Expect costs for both ingress and egress in self‑hosted setups. Large conferences, recordings, or many viewers dramatically increase bandwidth usage. ◦ Measure expected concurrent streams × average bitrate to estimate monthly transfer. • Storage ◦ If you record sessions or store logs/metrics long‑term, include object storage (S3/GCS or on‑prem storage) costs. The LiveKit runtime itself has a small disk footprint, but recordings and retained logs drive storage cost. • Transcoding / encoding (optional) ◦ Real‑time transcoding (adaptive bitrate ladders, advanced codecs) increases CPU usage and may require more powerful instances or specialized hardware, raising costs. • Agent hosting (if using AI agents / worker processes) ◦ Agent servers (for voice agents, bots, etc.) need compute and memory. Design for long‑lived sessions and autoscaling. Resource per agent depends on agent complexity and model usage. • Load balancers & networking ◦ Costs for load balancers, public IPs, cross‑region data transfer, and private networking. Also account for low‑latency networking needs if you run multi‑region nodes. • Observability & monitoring ◦ You’ll need monitoring/logging stacks (Prometheus, Grafana, ELK, hosted SaaS) and associated storage/compute for metrics and logs. These are essential to keep call quality reliable. • Operational overhead (people & processes) ◦ DevOps time for deployment, upgrades, security patching, autoscaling policies, incident response, and backups. Also include costs of backups, CI/CD, and testing environments. • Optional integrations and third‑party services ◦ Telephony/PSTN gateways, STT/TTS, LLM APIs, or other AI services are billed separately by those providers and should be estimated independently. • High availability & resilience ◦ Multi‑AZ or multi‑region deployments, redundancy for node failures, and warm spare capacity all increase cost but improve reliability. Key planning tips 1. Model expected concurrency first (peak concurrent users and average bitrate) — this drives compute and bandwidth estimates. 2. Start small and test: run a pilot to measure actual CPU, memory, and bandwidth for your usage pattern, then scale with autoscaling rules. 3. Use metrics to optimize costs: reduce retention on logs, tune bitrates, and use region placement to lower egress fees. 4. Remember LiveKit is open source (no licensing fees), but the infrastructure and third‑party services are where costs accrue. If you want, I can help you estimate numbers for your scenario. Could you share: • expected peak concurrent users, • average video/audio bitrate per participant (or whether you’ll use adaptive bitrate), • whether you plan to record sessions or use AI services (STT/TTS/LLMs), • and whether you need multi‑region redundancy? With those details I’ll give a rough monthly cost breakdown and suggested instance sizing. Sources: Self-hosted deployments | LiveKit Documentation | The end of Participant Minute pricing | Pricing | LiveKit