Model size and quantization
A 7B or 13B model can fit on smaller GPUs, while 70B+ models need larger VRAM pools, tensor parallelism, or more aggressive quantization.
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LLM Cost Calculator for Private LLM Hosting
A free LLM cost calculator for both sides of the decision: what your workload costs on OpenAI, Anthropic, and Google APIs, and what it costs to host a private LLM on your own GPUs, with the break-even between them.
Last reviewed October 3, 2026 by NavyaAI Research. The API price table was verified the same day; the study comparisons use prices verified September 17, 2026.
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16 models, list prices verified October 3, 2026
LLM API pricing calculator
Enter the tokens you send and receive in a month to see the bill on each model, cheapest first. Prices are each provider's published rate per million tokens at standard context length, before batch or caching discounts. Use the cheapest model that clears your quality bar as the number a private deployment has to beat, then size that deployment with the estimator above.
| Model | Input $/M | Output $/M | Monthly cost |
|---|---|---|---|
| GPT-6 LunaOpenAI | $0.10 | $0.50 | $35.00 |
| GPT-5.6 LunaOpenAI | $0.20 | $1.20 | $80.00 |
| Gemini 3.8 FlashGoogle, introductory price through Dec 31, 2026; doubles Jan 1, 2027 | $0.75 | $3.75 | $263 |
| Gemini 3.7 FlashGoogle, introductory price through Dec 31, 2026; doubles Jan 1, 2027 | $0.75 | $3.75 | $263 |
| Claude Haiku 4.5Anthropic | $1.00 | $5.00 | $350 |
| GPT-6.1 SolOpenAI | $2.00 | $10.00 | $700 |
| GPT-6 SolOpenAI | $2.00 | $10.00 | $700 |
| Claude Sonnet 5.5Anthropic | $2.00 | $10.00 | $700 |
| Claude Sonnet 5Anthropic | $2.00 | $10.00 | $700 |
| GPT-5.6 TerraOpenAI | $2.00 | $12.00 | $800 |
| Gemini 3.1 Pro (preview)Google, up to 200K context; $4 / $18 above | $2.00 | $12.00 | $800 |
| GPT-5.6 SolOpenAI, promotional price through at least Nov 21, 2026 | $4.00 | $20.00 | $1,400 |
| Claude Opus 5.5Anthropic | $4.00 | $20.00 | $1,400 |
| Claude Opus 5Anthropic | $5.00 | $25.00 | $1,750 |
| GPT-6 AstraOpenAI | $10.00 | $50.00 | $3,500 |
| Claude Fable 5.1Anthropic | $10.00 | $50.00 | $3,500 |
For long-context tiers, promotional expiry dates, and discount levers, see the LLM API pricing comparison.
900 scenarios, 3,600 API comparisons
What this calculator finds when you run it on everything
We ran this calculator's engine across every combination of model size, precision, deployment, latency target, use case, and concurrency, against API prices verified September 17, 2026. The script and full results are public, so you can re-run it and check our numbers.
- tokens/day median break-even vs OpenAI GPT-5.6 Terra
- 10.5M
- of scenarios where self-hosting beats OpenAI GPT-5.6 Luna once a $1,500/mo ops floor is included
- 8.2%
- win rate vs OpenAI GPT-5.6 Terra going from 5 to 100 concurrent users: concurrency decides, not token volume
- 10.7% → 98.3%
- median monthly cost from serving INT8 instead of FP16
- −22%
Full method and findings: self-hosted LLM vs API cost study. Raw data: CSV (CC BY 4.0).
Private LLM hosting cost
What changes the cost to host private LLM workloads?
The cost to host private LLM systems is not just the GPU invoice. Production teams also pay for memory headroom, batching strategy, uptime, monitoring, networking, security controls, and the engineering time needed to keep inference latency predictable. For the monthly ranges by model size, the operations floor, and where break-even sits against current API pricing, see our guide to the cost to host a private LLM.
Daily token volume
Private hosting improves when traffic is steady. Low or bursty workloads often stay cheaper on APIs until usage grows.
Latency and concurrency
Interactive workloads need more headroom than offline jobs. Concurrency targets drive GPU count, memory, and serving architecture.
Operations and facility costs
Power, cooling, colocation, observability, maintenance, and on-call coverage all belong in a realistic private LLM TCO model.
Private LLM cost formula
Monthly private LLM cost equals amortized GPU hardware plus power, cooling, hosting, maintenance, networking, storage, and engineering operations. Divide that by monthly tokens to compare against API prices on a per-million-token basis.
When on-prem breaks even
Self-hosting usually makes sense when private data requirements, fine-tuned models, predictable high token volume, or strict latency needs outweigh the flexibility of hosted APIs. The calculator estimates that break-even point from your workload.
Where NavyaAI can reduce cost
NavyaAI reduces LLM hosting cost through model selection, quantization, batching, caching, routing, and inference stack tuning. See our AI cost calculator first if you are choosing between Gemini API, private inference, and hybrid routing, then review our model inference optimization service or read the AI cost report.
Summary for AI infrastructure buyers
Private LLM hosting cost becomes attractive when usage is high, predictable, and sensitive enough that API flexibility is less valuable than control. The major decision is not whether GPUs are cheaper in isolation; it is whether the full workflow cost stays below API spend after operations, reliability, and optimization work are included.
Estimator methodology
- Sizes GPUs from memory bandwidth and measured vLLM batching, calibrated to four measured Llama 3 70B runs (worst error 7.8%).
- Defaults to INT8 weights; FP16 is available when you need it.
- Adds replicas until every user gets a minimum speed: 30 tok/s real-time, 10 tok/s interactive.
- Compares cost per million tokens against OpenAI, Anthropic, and Google prices verified September 17, 2026.
Private LLM hosting FAQ
How do I calculate LLM API cost?
Multiply your monthly input tokens by the model's input price and your output tokens by its output price, then add the two. At 100M input and 50M output tokens a month, Claude Sonnet 5.5 costs $700 and GPT-6 Luna costs $35, at list prices verified October 3, 2026. Output tokens cost 5 to 6 times as much as input tokens on every model in the table, so the mix matters as much as the volume.
What goes into the calculator's private LLM cost estimate?
Model size, precision, concurrent users, latency target, daily token volume, and whether you rent cloud GPUs or buy servers. The calculator sizes GPUs from measured throughput, then adds power, hosting, and maintenance. Across 900 scenarios the median private deployment runs $637/month for a 7B model and $3,178/month for a 70B model, before engineering time.
When should I self-host an LLM instead of using cloud APIs?
When you run steady volume for many concurrent users. In our 900-scenario study the median break-even is 10.5M tokens/day against OpenAI GPT-5.6 Terra, 12.4M against Anthropic Claude Sonnet 5, 33M against Google Gemini 3.7 Flash, and 82.2M against OpenAI GPT-5.6 Luna. With 5 users, self-hosting rarely wins; with 25 or more it usually does against mid-tier APIs.
Is a private LLM cheaper than OpenAI or Anthropic APIs?
Against mid-tier models, often: self-hosting beats OpenAI GPT-5.6 Terra in 67.3% of our scenarios after a $1,500/month operations floor. Against budget models, rarely: it beats OpenAI GPT-5.6 Luna in only 8.2%. Low or bursty traffic stays cheaper on APIs because you do not pay for idle GPUs.
What GPU should I use for LLM inference?
A 7B–13B model fits one 24GB card such as an NVIDIA L4 (about $2,700) or A10. A 70B model at INT8 fits one 80GB H100 (about $30,000); at FP16 it needs two. The calculator picks the cheapest GPU and count that meets your per-user speed target, so enter your real concurrency rather than choosing hardware first.
How do I calculate the cost per token for a private LLM?
Divide total monthly cost by the tokens you actually serve that month. Total cost includes hardware amortization or GPU rental, power, hosting, maintenance, and the people who run it. Utilization matters most: the same H100 costs about $0.35 per million tokens fully busy and $0.54 at 65% busy in our benchmark.
How much does LLM deployment cost?
Cloud APIs cost nothing upfront and bill per token. A private deployment's median monthly cost in our study is $637 for 7B, $1,606 for 34B, and $3,178 for 70B, plus the people who operate it. Buying servers trades upfront capex for a lower marginal cost once volume is sustained.