LLM Inference Cost Calculator: Cloud vs. On-Prem
Should your healthcare LLM workload run on cloud APIs or on-prem GPUs? Calculate the breakeven volume in 30 seconds. Compare GPT-4o, Claude Opus, AWS Bedrock, and Vertex Gemini against a Llama 3 70B or Mistral Large deployment on your own hardware.
The numbers tell a clear story for most healthcare orgs — but the line moves fast as cloud prices drop and open-weight models close the quality gap. This calculator uses 2026 pricing and current open-weight performance benchmarks.
Why this decision is harder for healthcare than for other industries
Three factors shift the cloud-vs-on-prem economics specifically for healthcare AI:
- BAAs with cloud LLM providers — OpenAI, Anthropic, AWS Bedrock, and Google Vertex all sign BAAs, but the contract tier required (often Enterprise) carries a 30–50% list-price markup over the public API rate. Most cloud-cost calculators use the public rate; this one accounts for the healthcare premium.
- Audit logging requirements — every PHI byte sent to a cloud LLM is a covered transmission requiring §164.312(b)-compliant logging. On-prem deployments still need audit logging but don’t have the per-token transmission cost.
- Quality gap is closing fast — Llama 3 70B and Mistral Large now match GPT-4-class models on most clinical text tasks (summarization, extraction, classification). For ambient documentation and clinical copilots, on-prem is increasingly viable. For complex reasoning workflows, cloud still leads.
Workload size
Combined input + output. ~2,000 is the median for clinical text workloads (ambient docs, copilots, predictive).
Total daily LLM calls across the deployment.
How the math works
Cloud cost (per year)
Cloud annual cost = Tokens per inference × Inferences per day × Working days × ($/1M tokens) / 1,000,000
The calculator pre-loads 2026 list prices for each cloud model with the healthcare BAA premium baked in. Token counts include both input and output (the playbook default of 2,000 tokens per inference assumes ~1,200 input + 800 output, typical for clinical summarization).
On-prem cost (per year, amortized)
On-prem annual cost = (Hardware ÷ 3 years) + Power & cooling + 0.5 FTE ML ops engineer
- Hardware — typical: NVIDIA H100 cluster ~$200K (1× DGX H100 8-GPU, sufficient for Llama 3 70B at production-grade throughput)
- Power & cooling — ~$30K/year (assumes datacenter or hospital server-room rates)
- ML ops — 0.5 FTE at $180K fully-loaded = $90K/year (model updates, capacity tuning, on-call)
Total amortized: roughly $187K/year for the standard configuration. Adjust upward for higher-throughput models (Llama 3 405B needs an H100×16 or H200 cluster), downward for smaller models (Phi-3 runs on a single A100).
Breakeven volume
The calculator finds the inferences-per-day volume at which annual cloud cost equals annual on-prem cost. Below that volume: cloud wins. Above: on-prem wins. Within ±20% of breakeven: “you’re near breakeven, choose based on non-cost factors” (data residency, latency, model lock-in).
When to choose on-prem regardless of breakeven
The breakeven volume answers the cost question. Three other factors override it:
- Data residency requirements — some health systems contractually cannot send PHI off-prem to any cloud, regardless of BAAs. On-prem is the only option.
- Latency requirements — real-time clinical alerts (sepsis, deterioration) typically need <500ms inference latency. On-prem GPUs in the same datacenter beat cloud APIs by 200–400ms round-trip.
- Model customization — fine-tuning on your own clinical corpus is mostly an on-prem advantage. Some cloud providers offer fine-tuning, but BAA-tier contracts often exclude it.
Inverse case for cloud:
- Variable / spiky workloads — if your inference volume swings 10× day-to-day, the on-prem hardware is mostly idle. Cloud’s pay-per-token wins regardless of breakeven.
- Frontier model requirements — if your workflow genuinely needs GPT-4-level reasoning that open-weight models can’t match yet, you’re cloud regardless of cost.
- No infrastructure team — on-prem needs an MLOps engineer who can debug a CUDA out-of-memory error at 2am. If you don’t have that capability, the $90K FTE in the calculation is actually $200K+ once you account for hiring.
Frequently asked questions
It matches typical healthcare AI workloads. Ambient documentation: 1,500–3,000 tokens (transcript in, SOAP note out). Clinical decision support: 1,000–2,500 tokens (patient context in, recommendation out). Predictive analytics: 800–1,500 tokens (structured features in, classification + rationale out). 2,000 is the median.
Approximately: OpenAI Enterprise: 30–40% over public API. Anthropic via AWS Bedrock with BAA: 25–35%. Google Vertex AI with BAA: 20–30%. Amazon Bedrock direct (Claude/Titan): 20–25%. The calculator pre-loads the BAA-tier price for each model so you’re comparing apples-to-apples with on-prem.
For summarization, extraction, and classification: yes, within 2–4% on standardized benchmarks (MedQA, MMLU-Med, MultiMedQA). For complex multi-step reasoning, differential diagnosis, or rare-disease workups: GPT-4o still leads by 8–15%. For most ambient documentation and clinical copilot use cases, the gap doesn’t matter.
Production-grade throughput (50+ requests/min) needs 8× H100 80GB GPUs (one DGX H100 box, ~$200K) or 4× H200 GPUs. For lower throughput (10–20 req/min), 4× A100 80GB ($85K used market) is adequate. The calculator defaults to the H100 config since most production healthcare deployments need the throughput headroom.
Because on-prem LLMs aren’t fire-and-forget. You need someone to: monitor GPU utilization and queue depth, push model updates (every 6–12 weeks), tune batch size and KV-cache config for your workload mix, debug CUDA failures, manage hardware warranty and replacement cycles, and run security patches on the inference stack. 0.5 FTE is a defensible minimum; some orgs run a full FTE.
Hybrid is the third option the calculator doesn’t model directly, but it’s often the right answer for variable workloads. Run on-prem for the bottom 70% of your volume curve (predictable steady-state) and burst to cloud for the top 30% (spikes, edge cases, unusual inputs). Implementation complexity is high but the economics can be 20–30% cheaper than pure-cloud OR pure-on-prem at large volumes.
They’re a third path: open-weight models (Llama 3, Mistral) hosted by a third party, with usage-based pricing similar to OpenAI but typically 60–80% cheaper. They sign BAAs at enterprise tier. Worth modeling if your volume is below the on-prem breakeven but above the GPT-4 breakeven. Use the “Other” option in the cloud-model dropdown and enter their $/1M token rate.
Want the full on-prem deployment guide?
The calculator gives you the cost comparison. Actually deploying on-prem LLMs is a different problem — hardware sizing for your specific workload mix, datacenter or hospital-server-room placement, model selection (Llama 3 vs Mistral vs Phi-3 vs Qwen for your specialty), inference stack choice (vLLM vs TensorRT-LLM vs SGLang), and operational runbook.
The result page above includes a CTA for our 35-page on-prem LLM deployment guide — emailed to you as a PDF. Includes hardware sizing tables, inference-stack benchmarks, and a sample procurement spec.
Or book a 30-min call if you want to talk through the trade-offs for your specific environment with a Taction Software® healthcare AI architect.