Is it cheaper to self-host an LLM or pay for an API? Compare GPT, Claude, and Gemini costs against buying GPUs or renting cloud compute. Break-even timelines, capacity checks, and June 2026 prices.
Running open models on your own hardware or rented cloud GPUs has very different economics from paying per token to a provider. This calculator compares the cost of self-hosting against API pricing for models like GPT, Claude, and Gemini, so you can see where the break-even point falls for your usage.
Self-hosting wins when a GPU stays busy. The key variables are:
A rough way to read the result: low or spiky volume usually favors APIs, while sustained high throughput favors self-hosting.
Use it before committing to a GPU purchase, choosing between cloud GPU rental and an API contract, or justifying a self-hosting plan to a team. To estimate the throughput figure that drives these numbers, pair it with the LLM Inference Speed Calculator and the LLM VRAM Calculator. All math runs locally in your browser.
The break-even calculation has a structure most people get wrong:
API cost scales linearly with usage: tokens x price. Double your usage, double your bill, forever.
Self-hosting cost is mostly fixed: the hardware costs the same whether you run it 1 hour or 24 hours a day. Electricity scales with usage but is small (a 4090 at full tilt costs about 1.60 dollars/day in power).
This means there is always a crossover volume above which self-hosting wins — the only questions are whether your volume is above it, and whether the hardware can physically serve that volume (the capacity check).
The two failure modes: buying hardware for low usage (a 2,000 dollar GPU to save 10 dollars/month of API calls never pays off), and underestimating capacity needs (one consumer GPU cannot serve a production app with thousands of daily users — you need batch serving or multiple GPUs, which changes the math).
Rules of thumb from current prices:
Use a budget API (Gemini Flash-Lite, GPT-4.1 Nano, DeepSeek): for almost any volume under 1B tokens/month, these are nearly impossible to beat — under 50 dollars/month for what would require dedicated hardware to self-host.
Use open-model hosting (Groq, Together): when you specifically want open models (Llama, Qwen) without operations work. Groq serves Llama 3.1 8B at 5 cents per million input tokens — cheaper than your electricity to self-host it.
Self-host on owned hardware: when you already have the GPU (gaming PC, Mac), value privacy, or your volume against a frontier API exceeds ~200-500 dollars/month and a capable open model genuinely covers your use case.
Rent cloud GPUs: for fine-tuning runs, batch processing jobs, and validating self-hosting before buying hardware.
Pay for frontier APIs: when capability is the constraint. No amount of self-hosting math makes Llama into Claude Opus.
It depends almost entirely on volume and which API you are replacing. Low volume (under ~50M tokens/month): APIs win — even budget hardware never pays for itself. High volume against frontier APIs (Claude Opus, GPT-5.4 Pro): self-hosting can pay for itself in weeks. High volume against budget APIs (Gemini Flash-Lite, Groq-hosted Llama): APIs usually still win, because providers run hardware at near-perfect utilization and you cannot. The honest comparison is against open-model hosting (Groq/Together), not against frontier models — a self-hosted Llama is not a GPT-5 replacement.
Three components: hardware (a used RTX 3090 at ~700 dollars to a Mac Studio at ~4,700 dollars, amortized over its useful life), electricity (a 350-450W GPU running a few hours a day costs 5-25 dollars/month at typical US rates), and your time (setup, updates, debugging — the hidden cost everyone forgets). For light personal use, electricity is nearly negligible; the hardware cost dominates.
A single RTX 4090 running Llama 3.3 70B... cannot (it does not fit). Running an 8B model at ~100 tokens/sec, one 4090 can theoretically generate ~260M tokens/month running 24/7. With realistic 30% utilization, ~80M tokens/month. Production serving frameworks (vLLM) with request batching multiply this 5-10x by processing many requests simultaneously. This calculator includes a capacity check that flags when your volume exceeds what the hardware can deliver.
Rent if: your usage is bursty (training runs, batch jobs), you need datacenter GPUs (H100s cost 25K+ to buy but ~2.50/hr to rent), or you are validating an idea. Buy if: you have steady daily usage, consumer hardware covers your needs, and you will use it for 18+ months. The crossover is roughly 6-10 hours of daily use — below that, renting wins; above it, owning wins. Cloud spot/community pricing (RunPod, Vast.ai) has made renting much more competitive.
This is the elephant in the room: a self-hosted Llama 3.3 70B is roughly comparable to mid-tier API models, not to frontier models like Claude Opus or GPT-5.4 Pro. If your workload genuinely needs frontier capability, self-hosting is not an alternative — it is a different product. The fair comparisons are: self-hosting vs open-model hosting APIs (Groq, Together), or accepting the capability trade-off in exchange for privacy, control, and cost.
Privacy: prompts and data never leave your infrastructure — relevant for healthcare, legal, and anything under NDA. No rate limits: your hardware, your queue. Latency consistency: no API outages or degraded performance during peak hours. Compliance: some regulations effectively require data to stay on-premises. Predictable costs: no surprise bills from a usage spike. For many organizations these matter more than the per-token math.
API and cloud GPU prices in this calculator were verified in June 2026. LLM API prices have been falling roughly 80% year-over-year, so check provider pages for the latest. Hardware prices are street prices for new cards (used market is typically 30-50% less). We update this dataset periodically; the "as of" date is shown in the methodology note.
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