Table of Contents
The Core Question: Rent vs Own
Every team running LLM workloads faces the same decision: keep paying per token through an API, or invest in dedicated GPU hosting with a fixed monthly cost. The answer depends entirely on volume. At low usage, APIs are cheaper. At high usage, self-hosting saves thousands per month. This guide finds the exact crossover point for every major provider and model tier.
We built an interactive version of this analysis — the GPU vs API cost comparison tool — but this article walks through the methodology and presents the full data tables. For per-GPU token costs, see our cost per million tokens breakdown.
How API Pricing vs GPU Hosting Costs Work
API pricing scales linearly. Process 10x more tokens, pay 10x more. There is no volume discount for most providers (some offer committed-use discounts, but they still charge per token).
Dedicated GPU hosting is a flat monthly rate. Whether you process 1 million or 100 million tokens, the server costs the same. Your effective cost per token drops as utilisation increases.
| Cost model | API providers | Dedicated GPU hosting |
|---|---|---|
| Pricing structure | Per token (input + output) | Fixed monthly fee |
| Cost at low volume | Low (pay for what you use) | Higher (fixed cost regardless) |
| Cost at high volume | High (linear scaling) | Low (amortised across tokens) |
| Predictability | Variable — spikes with traffic | Fixed — same bill every month |
| Scaling cost | Proportional to usage | Step function (add another GPU) |
Break-Even Analysis by Provider
We compared a dedicated RTX 3090 running vLLM against each API provider. The RTX 3090 produces approximately 42 tokens/sec on LLaMA 3 8B, yielding roughly 109M tokens per month at full utilisation. Here is where the break-even falls:
| API provider | Model | API cost / 1M tokens | RTX 3090 cost / 1M tokens | Break-even volume |
|---|---|---|---|---|
| OpenAI | GPT-4o | $10.00 (output) | ~$0.28 | ~3M tokens/month |
| OpenAI | GPT-4o mini | $0.60 (output) | ~$0.28 | ~50M tokens/month |
| Anthropic | Claude 3.5 Sonnet | $15.00 (output) | ~$0.28 | ~2M tokens/month |
| Anthropic | Claude 3.5 Haiku | $4.00 (output) | ~$0.28 | ~8M tokens/month |
| Together.ai | LLaMA 3 8B | $0.20 | ~$0.28 | ~150M tokens/month |
| Together.ai | LLaMA 3 70B | $0.90 | ~$0.53 (RTX 5090) | ~60M tokens/month |
Against premium API providers like OpenAI GPT-4o or Anthropic Sonnet, self-hosting breaks even almost immediately — at just 2-3M tokens per month. Even against the cheapest option (Together.ai for LLaMA 3 8B), a dedicated server wins once you push past roughly 150M tokens per month. Check the LLM cost calculator for your specific volume.
Break-Even by Model Size
Larger models need more expensive GPUs, which shifts the break-even point. Here is how self-hosting economics change by model size:
| Model size | Recommended GPU | Self-hosted cost / 1M tokens | Together.ai cost / 1M tokens | Break-even vs Together.ai |
|---|---|---|---|---|
| 7-8B (LLaMA 3 8B) | RTX 3090 (24 GB) | ~$0.28 | $0.20 | ~150M tokens/month |
| 13B (CodeLlama 13B) | RTX 3090 (24 GB, 4-bit) | ~$0.45 | $0.30 | ~180M tokens/month |
| 34B (CodeLlama 34B) | RTX 5090 (32 GB, 4-bit) | ~$0.85 | $0.60 | ~110M tokens/month |
| 70B (LLaMA 3 70B) | 2x RTX 3090 or RTX 5090 | ~$1.10 | $0.90 | ~140M tokens/month |
The pattern holds across model sizes: self-hosting wins at moderate-to-high volume. The break-even zone sits between 100-200M tokens per month for most configurations. For a detailed look at which GPU handles which model size, read our best GPU for LLM inference guide.
ROI Timeline: How Fast Do You Recoup?
If you are currently spending on API tokens, switching to dedicated GPU servers pays for itself quickly. Here is the timeline for a team processing 1B tokens per month on LLaMA 3 8B:
| Compared to | API cost / month (1B tokens) | RTX 3090 cost / month | Monthly savings | ROI timeline |
|---|---|---|---|---|
| OpenAI GPT-4o mini | $600 | ~$30 | $570 | Immediate (month 1) |
| Anthropic Haiku | $4,000 | ~$30 | $3,970 | Immediate (month 1) |
| Together.ai (8B) | $200 | ~$30 | $170 | Immediate (month 1) |
Because open source LLM hosting on dedicated hardware uses a monthly subscription model (not capital expenditure), there is no large upfront investment to recoup. Savings start from the first billing cycle. The only additional effort is initial setup — which tools like Ollama and vLLM reduce to minutes.
See Your Break-Even Point
Enter your monthly token volume and current API spend. We’ll show exactly when dedicated GPU hosting pays off.
Browse GPU ServersHidden Costs and Considerations
Self-hosting is not free beyond the server bill. Factor in these costs when making your decision:
- Setup time: 1-4 hours for initial deployment. Our self-hosting LLM guide covers the full process.
- Maintenance: Model updates, security patches, monitoring. Typically 1-2 hours per month for a stable deployment.
- Utilisation risk: If your GPU sits idle, you are paying for unused capacity. APIs charge nothing when idle.
- Scaling friction: Adding capacity means provisioning another server (same-day with GigaGPU). APIs scale instantly.
For teams running private AI hosting for compliance reasons, the cost comparison is secondary — you need dedicated hardware regardless. The cost savings are a bonus.
Decision Framework
Use API pricing if:
- You process fewer than 50M tokens per month
- Your traffic is highly unpredictable with long idle periods
- You need access to proprietary models (GPT-4o, Claude) not available as open source
Use dedicated GPU hosting if:
- You process more than 100M tokens per month consistently
- You need predictable, fixed monthly costs
- Data privacy or GDPR compliance requires on-premises or single-tenant hosting
- You run open source models (LLaMA, Mistral, DeepSeek) that perform well self-hosted
- You want full control over latency, throughput, and model configuration
Not sure where you land? Run your numbers through the GPU vs API cost comparison tool, or compare providers in our alternatives guides. When you are ready, browse dedicated GPU servers with same-day deployment from our UK datacenter.
