Calculate the break-even point for self-hosting Llama 3.3 70B FP8 on 2× H100 versus using a managed LLM API. See how request volume, average context length, and API provider choice determine whether owning GPUs beats paying per-token.
| Provider | In $/M | Out $/M | API monthly | Break-even | Status |
|---|---|---|---|---|---|
| Claude 3.5 Sonnet (Anthropic) | $3.00 | $15.00 | $3,150 | 11,587/day | retired Oct 2025 |
| GPT-4o (OpenAI)selected | $2.50 | $10.00 | $2,250 | 16,222/day | current |
| o3-mini (OpenAI) | $1.10 | $4.40 | $990 | 36,869/day | current |
| Claude 3.5 Haiku (Anthropic) | $0.80 | $4.00 | $840 | 43,452/day | retired Feb 2026 |
| Llama 3.1 70B (Together) | $0.88 | $0.88 | $396 | 92,172/day | current |
| Mistral Large 3 | $0.50 | $1.50 | $375 | 97,333/day | current |
| Llama 3.3 70B (Groq) | $0.59 | $0.79 | $296 | 123,519/day | current |
| GPT-4o mini (OpenAI) | $0.15 | $0.60 | $135 | 270,370/day | current |
| DeepSeek V4.1 Flash | $0.15 | $0.60 | $135 | 270,370/day | current |
| Gemini 2.0 Flash (Google) | $0.10 | $0.40 | $90 | 405,556/day | shut down Jun 2026 |
Self-hosting wins at high volume, low price-per-token APIs win at low volume — the break-even for GPT-4o ($10/M out) is ~27K req/day; for Llama 3.3 70B on Groq ($0.79/M out) it's ~340K req/day. Cheap API providers like Groq make self-hosting harder to justify.
Utilization is the killer variable — at 50% utilization, 2× H100 produces 1258 tok/s effective. At 100% (saturated queue), the cost/M output halves. Most production deployments run 40-70% utilization; anything less is wasteful.
Self-hosting has hidden costs not in this calculator — ops engineering time (~$15K/mo fully loaded for a senior SRE), GPU failures (~3% annual A100/H100 replacement rate), and electricity (~$200-500/mo per H100 at $0.10/kWh, 700W TDP). Add 15-25% to the sticker price.
Quality parity matters — Llama 3.3 70B is roughly GPT-4o-class on most benchmarks, but if your workload needs Claude 3.5 Sonnet's stronger reasoning or GPT-4o's better tool-use, the comparison isn't apples-to-apples. Match the model class first, then compare cost.
For 2× H100 ($7.30/hr, ~$5,329/mo) running Llama 3.3 70B FP8 at 50% utilization: ~27,000 requests/day (1K in + 500 out tokens each) against GPT-4o ($10/M out). Below that volume, GPT-4o is cheaper. Above it, self-hosting wins by an increasing margin.
Roughly yes, with caveats. Llama 3.3 70B matches or beats GPT-4o on MMLU, HumanEval, and MATH. GPT-4o still leads on multi-modal (vision) and complex tool-use chains. For pure text reasoning, code generation, and structured output, parity is close enough that the cost difference dominates.
Groq runs Llama 70B on LPU hardware (specialized inference chips) at ~5-10× the throughput of H100. They can profitably sell at $0.79/M output tokens because their hardware cost per token is lower. Unless you need data residency, latency SLAs, or model fine-tuning, Groq is hard to beat on price for inference-only workloads.
Fine-tuning changes the math dramatically. Most API providers don't support fine-tuning (or charge heavily for it), so self-hosting becomes the only option. For private/sensitive data (HIPAA, SOC2, GDPR data residency), self-hosting is also the only compliant option regardless of cost.
For 70B-class models at standard contexts (≤32K), H100 is sufficient and cheaper. For long-context (128K+) or higher concurrency, H200's 141 GB VRAM gives meaningfully better throughput per dollar. See our H100 vs H200 comparison.
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