AgentBriefWhat it actually costs to run AI on your own hardware at a small company — architecture picks and honest pros/cons for 10–20, 20–50 and 50–100 employees, compared head-to-head with hosted VMs (Hostinger, Hetzner, Hyper-V). Use the cost calculator to compare self-hosted vs cloud for your team size.
Pick your team size
One GPU workstation-class server
A single box with a 24-48GB GPU (RTX 4090 / RTX 6000 class) running Ollama or vLLM behind an internal chat and document-Q&A front-end. Covers an assistant for everyone plus Q&A over company knowledge.
Cost
$4,000-$8,000 one-time one-time
$40-$90/mo power + ~2h/mo ops
Pros
- Full data privacy - documents never leave the building
- Predictable cost: hardware is a one-time purchase
- No per-token bills; usage scales freely
- Keeps working during internet outages
Cons
- Single box is a single point of failure (keep backups + a cloud fallback)
- You own patching, GPU drivers and model updates
- 24GB VRAM caps model size to 14B-32B class quantized
Dual-GPU server or 2x Mac Studio
Two 48GB-class GPUs (or Apple unified-memory nodes) running vLLM, a Docker Compose or small Kubernetes stack, SSO, and separate endpoints for chat, code and embeddings. Add a RAID NAS for the RAG corpus.
Cost
$12,000-$25,000 one-time one-time
$120-$250/mo power + ~6h/mo ops
Pros
- Concurrent users without per-seat API costs
- Different models per department (support vs code vs legal)
- Room to grow: add nodes instead of re-architecting
- Compliance-friendly for regulated data
Cons
- Needs someone accountable for uptime (can be a fractional admin)
- Failover is your problem - plan a small cloud escape hatch
- Depreciation on a 3-5 year refresh cycle
Rack server or GPU cluster (4-8 GPUs)
A 1U/4U rack server (Supermicro/HPE) or DGX Spark-class nodes with a 10GbE switch, redundant PSU, central monitoring and a model gateway with per-team quotas. Optionally hybrid: burst overflow to cloud.
Cost
$30,000-$80,000 one-time one-time
$300-$700/mo power + 0.25 FTE ops
Pros
- Serves the whole company with SLAs you control
- Blended cost per employee drops sharply vs API pricing
- Hybrid burst keeps you elastic without cloud lock-in
- Audit logs and data residency under your control
Cons
- Real infrastructure - budget for monitoring, backups, DR drills
- Power and cooling must be planned (200-400W per GPU)
- Refresh is a capital project, not a credit card
Self-hosted vs hosted VM vs Hyper-V
| Factor | On-prem GPU | Cloud VM (Hostinger, Hetzner, GPU clouds) | Hyper-V on an existing server |
|---|---|---|---|
| Upfront hardware | $4k-$80k (one-time) | $0 | $0 |
| Monthly cost | Power + ops only | $10-$60 per VPS; $500-$3,000+ for GPU VMs | Ops time on existing kit |
| 36-month TCO (50-seat) | ~$35k self-hosted | ~$130k+ GPU cloud | Depends on existing hardware |
| Data privacy | Stays on premises | Leaves your network | Stays on premises |
| Scaling | Add GPUs/nodes (capex) | Instant, pay per use (opex) | Limited by host hardware |
| Maintenance | Your team (or MSP) | Provider | Your team |
| Best for | Steady predictable load | Bursty or unknown load | Testing before committing |
Cost calculator - self-hosted vs cloud
Estimates only - hardware prices vary by region and the cloud column assumes GPU-capable VMs. A plain $10 Hostinger VPS hosts apps and APIs but cannot run the models themselves; on top of any VM you would still pay per-token API bills.
Under ~20 people, API pay-as-you-go often beats hardware on total cost because you skip the ops burden. From 20-50 up, steady daily usage usually crosses over: self-hosted wins on both privacy and price, and the gap widens with every concurrent user. Hyper-V on a spare server is the free way to prototype before buying any GPU.