Open-Source vs Closed-Source LLM Cost Comparison
Updated 12 July 2026 · first published 4 July 2026
When does self-hosting Llama, Qwen, or DeepSeek break even versus paying for API access? This analysis covers GPU costs, inference optimisation, operational overhead, and gives you a decision framework for your specific workload.
- Self-host costs assume cloud GPU pricing (on-demand)
- API costs use standard output pricing per provider
- Break-even based on 70% utilisation, 24/7 availability
Methodology
API pricing
Standard published rates from each provider's pricing page. No batch or volume discounts applied.
Self-host GPU
Cloud GPU on-demand pricing (Lambda, RunPod, AWS) for inference-optimised setups with vLLM or TensorRT-LLM.
Operational overhead
Includes monitoring, scaling, model updates, and a 15% ops-surcharge on raw GPU cost for managed infrastructure.
Quality gap
Relative to GPT-4o / Claude Sonnet 4 as baseline 100%. Based on public benchmarks and practical task performance.
Cost Comparison Table
| Model ↕ | API Cost/mo ↕ | Self-Host Cost (cloud GPU) ↕ | Break-Even Volume ↕ | Quality Gap |
|---|---|---|---|---|
| Llama 4 Maverick (DeepInfra) | $260/mo | $2,800/mo | ~350M tokens/mo | ~95% of GPT-4o |
| Llama 3.3 70B (Groq) | $277/mo | $1,200/mo | ~210M tokens/mo | ~87% of GPT-4o |
| Llama 4 Scout (Groq) | $136/mo | $1,400/mo | ~350M tokens/mo | ~88% of GPT-4o |
| Qwen 3.7-Max | $525/mo | $1,600/mo | ~100M tokens/mo | ~92% of GPT-4o |
| DeepSeek V4 Pro | $435/mo | $1,800/mo | ~130M tokens/mo | ~96% of GPT-4o |
| DeepSeek V4 Flash | $140/mo | $1,400/mo | ~450M tokens/mo | ~82% of GPT-4o |
| Mistral Medium 3.5 | $525/mo | $2,000/mo | ~120M tokens/mo | ~91% of GPT-4o |
| GPT-4o (API only) | $750/mo | N/A | N/A | Baseline (100%) |
API Cost/mo assumes 100M output tokens + 300M input tokens per month at standard rates.
Self-Host Cost includes GPU rental, 15% ops overhead, and inference stack (vLLM). Does not include initial setup engineering time.
Break-Even Volume is the monthly output token count where self-hosting becomes cheaper than API.
Quality Gap is approximate and task-dependent. Benchmarks like MMLU, HumanEval, and GPQA inform these estimates.
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Key Cost Factors
1) GPU rental is the dominant cost
An 8x H100 node for Llama 4 Maverick runs $2,000-$3,500/mo on-demand from Lambda, RunPod, or AWS. Spot/preemptible instances can cut this 40-60% but add restart latency and availability risk. For production workloads, reserved instances (1-year commitment) typically save 30-40% over on-demand.
2) Inference optimisation changes the equation
vLLM with continuous batching, KV-cache paging, and quantisation (GPTQ/AWQ 4-bit) can increase throughput 2-4x on the same hardware. TensorRT-LLM adds another 20-40% for supported architectures. A well-tuned 4-bit Llama 4 Scout on a single A100 can match a naive fp16 setup on 2x A100s.
3) Operational overhead is real but shrinking
Running inference infrastructure requires GPU monitoring, model updates, load balancing, and failover. Budget 15-25% of raw GPU cost for ops. Managed platforms (Anyscale, Modal, Replicate) absorb this but charge 30-50% premium over raw GPU - worth it below ~$5K/mo GPU spend.
4) Prompt caching narrows the gap for API
API providers increasingly offer prompt caching (50-90% discount on repeated prefixes). If your workload is cache-friendly (RAG, multi-turn), API costs drop significantly, pushing the break-even volume higher for self-hosting.
Decision Framework
Use API if...
- Monthly output volume is below 100M tokens
- You need frontier-model quality (GPT-4o, Claude Opus 4)
- Your team has no GPU/MLOps expertise
- Latency requirements are strict (sub-200ms TTFT)
- You want zero operational overhead
- Your workload is bursty or seasonal
Self-host if...
- Monthly output volume exceeds 200M tokens consistently
- Data privacy requires no third-party API calls
- You need custom fine-tuned models
- You have MLOps capacity on the team
- Workload is steady-state (predictable GPU utilisation)
- You can commit to 1-year reserved GPU instances
Hybrid approach: the pragmatic middle ground
Most teams land on a hybrid: use API for frontier reasoning tasks and low-volume workloads, self-host open-source models for high-volume, latency-tolerant tasks (content generation, data extraction, internal tools). This keeps API spend under control while building self-host capability incrementally.
Where to Run Open-Source Models
| Platform | GPU Options | A100 80GB Price | H100 Price | Managed Inference |
|---|---|---|---|---|
| Lambda Cloud | A100, H100, H200 | $1.10/hr | $2.49/hr | No (bring your own) |
| RunPod | A100, H100, L40S | $1.19/hr | $2.69/hr | Serverless option |
| AWS (p4d/p5) | A100, H100 | $3.67/hr | $6.37/hr | SageMaker |
| Google Cloud | A100, H100 | $3.22/hr | $5.07/hr | Vertex AI |
| Xiaomi MiMO ↗ | API-based | N/A | N/A | API ($0.50/$2.00 per 1M) |
GPU prices are on-demand hourly rates as of July 2026. Reserved/spot pricing varies significantly.
MiMO offers API-based access to MiMo-V2-Pro and MiMo-V2-Omni at competitive rates - a middle ground between full self-hosting and premium API pricing.
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