Table of Contents
What Total Cost of Ownership Includes
Comparing a dedicated GPU server to a cloud GPU rental on sticker price alone misses the full picture. Total cost of ownership accounts for every expense associated with running AI inference over the lifetime of the deployment: compute costs, storage, networking, engineering time, opportunity costs, and operational overhead.
This analysis goes beyond the per-token comparisons in our cost per million tokens breakdown to include every line item that shows up on your balance sheet. Whether you are presenting a business case to finance or making an infrastructure decision as a technical lead, this is the complete picture.
We compare three options: dedicated GPU hosting (like GigaGPU), cloud GPU instances (AWS, GCP, Azure), and API services (OpenAI, Anthropic). The reference workload is LLaMA 70B inference processing 200M tokens per month.
Dedicated GPU Server: Full TCO Breakdown
Here is every cost component for running LLaMA 70B on a dedicated 2x RTX 5090 server over 12 months:
| Cost Component | Monthly | 12-Month Total | Notes |
|---|---|---|---|
| Server rental (2x RTX 5090) | $450 | $5,400 | All-inclusive: GPU, CPU, RAM, storage, bandwidth |
| Additional storage (1TB NVMe) | $15 | $180 | For multiple model checkpoints |
| Initial setup engineering | – | $300 | ~3 hours at $100/hr (one-time) |
| Monthly maintenance | $50 | $600 | ~0.5 hours/month for updates, monitoring |
| Monitoring tools | $0 | $0 | Open-source tools (Prometheus, Grafana) |
| Software licensing | $0 | $0 | Linux, PyTorch, vLLM are free |
| Total 12-Month TCO | $6,480 |
The setup engineering cost drops to near zero with a managed provider where vLLM and drivers come pre-installed. Monthly maintenance is minimal: model updates, occasional PyTorch upgrades, and monitoring checks.
Cloud GPU Rental: Full TCO Breakdown
For comparison, here is the TCO for running the same workload on cloud GPU instances. We use AWS as the representative example (GCP and Azure are within 10-15% for equivalent configurations):
| Cost Component | Monthly | 12-Month Total | Notes |
|---|---|---|---|
| GPU instance (g5.12xlarge or p4d) | $2,500-4,000 | $30,000-48,000 | On-demand pricing, 24/7 uptime |
| Reserved instance (1-yr commit) | $1,700-2,800 | $20,400-33,600 | 30-40% discount requires upfront commitment |
| EBS storage (1TB gp3) | $80 | $960 | Model weights and data |
| Data transfer (egress) | $50-200 | $600-2,400 | $0.09/GB outbound after 100GB free tier |
| CloudWatch / monitoring | $20-50 | $240-600 | Detailed GPU metrics cost extra |
| Initial setup engineering | – | $500 | ~5 hours (IAM, VPC, security groups, deployment) |
| Monthly DevOps overhead | $200 | $2,400 | ~2 hours/month managing cloud infra |
| Total 12-Month TCO (on-demand) | $34,700-54,360 | ||
| Total 12-Month TCO (reserved) | $25,100-39,960 |
The cloud TCO is 4-8x higher than dedicated hosting for the same workload. Even with reserved instances and aggressive cost optimization, cloud GPU instances cost at minimum 3x more per month than equivalent dedicated hardware.
Side-by-Side 12-Month TCO Comparison
Bringing all three options together for the reference workload (LLaMA 70B, 200M tokens/month):
| Cost Category | Dedicated GPU (GigaGPU) | Cloud GPU (AWS Reserved) | API (GPT-4o) |
|---|---|---|---|
| Compute / inference | $5,400 | $20,400-33,600 | $10,512 |
| Storage | $180 | $960 | $0 |
| Networking | $0 (included) | $600-2,400 | $0 |
| Engineering (setup + maintenance) | $900 | $2,900 | $600 |
| Monitoring | $0 | $240-600 | $0 |
| 12-Month TCO | $6,480 | $25,100-39,960 | $11,112 |
| Effective cost per 1M tokens | $2.70 | $10.46-16.65 | $4.63 |
Dedicated GPU hosting delivers the lowest TCO by a significant margin: 42% cheaper than the API and 74-84% cheaper than cloud GPU instances. The self-hosting cost analysis explains why this gap exists and is widening.
24-Month TCO Projection
Over longer time horizons, the savings compound because the one-time setup costs are amortized and the monthly gap continues to accumulate:
| Time Period | Dedicated GPU | Cloud GPU (Reserved) | API (GPT-4o) |
|---|---|---|---|
| 6 months | $3,390 | $13,050-20,480 | $5,856 |
| 12 months | $6,480 | $25,100-39,960 | $11,112 |
| 24 months | $12,660 | $49,200-78,920 | $21,624 |
| Savings vs Cloud (24mo) | – | $36,540-66,260 saved | – |
| Savings vs API (24mo) | – | – | $8,964 saved |
Over two years, dedicated hosting saves $37,000-66,000 versus cloud GPU instances and nearly $9,000 versus API pricing. For organisations running multiple GPU workloads, multiply these savings accordingly.
Cut Your AI Infrastructure Costs by 70%+
Dedicated GPU servers deliver the lowest total cost of ownership for AI inference. No cloud markup, no per-token fees, no surprise bills.
Browse GPU ServersHidden Cloud Costs That Inflate TCO
Cloud GPU pricing is notoriously opaque. Several cost components are easy to miss when estimating TCO:
Data transfer charges. AWS charges $0.09/GB for outbound data after the free tier. Serving model responses, downloading generated images, or streaming audio all incur egress fees. These add up silently and unpredictably.
Spot instance unreliability. Spot instances are 60-70% cheaper than on-demand, but they can be terminated with two minutes notice. For production inference workloads, spot interruptions mean dropped requests, degraded user experience, and engineering time building fault-tolerance around interruptions.
Instance right-sizing complexity. Cloud instances bundle GPU, CPU, RAM, and storage in fixed ratios. If you need 48GB of VRAM but only 4 CPU cores, you still pay for the full instance with 48+ cores. Dedicated servers can be configured to match your actual requirements.
Cross-AZ and cross-region traffic. If your application servers are in a different availability zone from your GPU instances, inter-AZ traffic adds $0.01/GB in each direction. For high-throughput inference serving, this accumulates quickly.
DevOps overhead. Cloud infrastructure requires ongoing management: IAM policies, security group updates, instance scheduling, cost alarm configuration, and capacity planning. The RunPod alternatives comparison details how this overhead compares across different providers.
Which Model Fits Your Organisation
Choose dedicated GPU hosting when:
- Your AI workload runs consistently (5+ days/week)
- You want predictable, fixed monthly costs
- You need data privacy and compliance controls
- You run multiple AI workloads that can share hardware (LLM + speech + image generation)
- Your team wants full root access without cloud platform constraints
Choose cloud GPU instances when:
- Your workload is highly bursty with multi-day idle periods
- You need to spin up dozens of GPUs for short training runs and then release them
- You are already deeply integrated into a cloud ecosystem (AWS SageMaker, GCP Vertex AI)
- You need GPU types not available in dedicated hosting (RTX 6000 Pro 96 GB, RTX 6000 Pro clusters)
Choose API services when:
- Your volume is under 50M tokens/month
- You need access to frontier proprietary models (GPT-4.5, Claude Opus)
- You are in early development and the speed of integration matters more than unit economics
- Your team has no infrastructure management capacity
For the majority of production AI inference workloads, dedicated GPU hosting offers the best TCO by a wide margin. The break-even analysis confirms that the crossover point is lower than most teams expect, and the LLM cost calculator lets you verify with your specific workload parameters.
The trend over the past two years is clear: dedicated hardware is winning on cost, and the gap is growing. As open-source models like LLaMA, DeepSeek, and Mistral continue to close the quality gap with proprietary APIs, the economic case for self-hosting on dedicated GPUs only gets stronger.
