Written by Jakub Rusinowski · Last updated 2026-07-08 · Prices verified 2026-03-01
At enterprise volume the question stops being cost-first: 20M tokens/day costs roughly $2,850/month on GPT-4o versus about $2,200/month for an on-prem fleet sized to match (≈11 dual-GPU nodes amortized over 24 months, plus power) — real savings, but not transformative next to the compliance, vendor-risk, and data-sovereignty stakes. Enterprises choose on-prem primarily for control: regulated data, auditability, and independence from a vendor's pricing and policy changes. The dominant real-world architecture is hybrid — open models on owned or colocated GPUs for bulk internal workloads, frontier API contracts for the quality-critical slice.
Fleet-scale on-prem vs enterprise API contracts. (The calculator sizes machine count automatically — 20M tokens/day needs ~11 dual-GPU nodes.)
| Dimension | Local | Cloud |
|---|---|---|
| Cost at 20M tok/day | ~$2,200/mo (fleet amortized + power) | ~$2,850/mo list (GPT-4o); volume discounts negotiable |
| Capital & procurement | ~$32k up-front for a workhorse fleet; datacenter/colo space | OpEx only; enterprise agreement cycle |
| Compliance & residency | Data never leaves your perimeter; audit trail is yours | DPAs, regional processing, subprocessor monitoring |
| Vendor risk | Open weights: no deprecation, no price changes, no policy shifts | Model retirements, price and terms changes on vendor schedule |
| Quality ceiling | 70B-class + large MoE open models | Frontier models, always current |
| Scaling | Buy/rack ahead of demand; utilization risk is yours | Elastic; burst capacity is the provider's problem |
| Team required | Platform/MLOps capability (vLLM, monitoring, capacity) | Integration engineering only |
Org-wide internal platform: ~20M tokens/day across assistants, RAG, and pipelines — 20M tokens/day, Dual RTX 3090 48GB (70B-class) ×11 vs GPT-4o (OpenAI), electricity $0.15/kWh. Adjust every input in the interactive calculator on this page.
| Cloud cost / month | $2,850 (GPT-4o, $2.5/M input + $10/M output) |
| Local cost / month (24-mo TCO) | $2,213 — $898 electricity + hardware amortization |
| Hardware up-front | $31,570 (11× Dual RTX 3090 48GB (70B-class)) |
| Break-even | Month 17 — cumulative cloud spend passes local |
Estimates: 70/30 input/output mix, 24-month amortization, no resale value, load-time electricity only. Cloud prices last verified: 2026-03-01. Hardware street price checked: 2026-07-06.
Run the naive math and on-prem wins: 20M tokens/day is ~$2,850/month at GPT-4o list prices, while a fleet of ~11 dual-RTX-3090 nodes (48 GB each, ~$32k total at current street prices) running Llama 3.3 70B costs ~$2,200/month amortized over 24 months including power — and drops toward $900/month once amortized. A ~25% saving that compounds with volume.
Then reality complicates it in both directions. Cloud-side: enterprise agreements discount list prices materially, and routing the easy 80% of traffic to Mini-class models collapses the bill — Gemini Flash costs 4% of GPT-4o. On-prem-side: consumer-card fleets are the scrappy version; proper datacenter GPUs (or rented H100s for burst) cost more per unit but less per token at scale, while colo space, redundancy, and the platform engineer's salary all belong in the TCO. Model both honestly and the pure-cost gap usually lands within ±40% either way. At enterprise scale, cost is a tiebreaker, not the decision.
Three control properties push large organizations on-prem, and none appear on an invoice:
The counterweights are real too: frontier quality (no open model matches the frontier on the hardest reasoning), organizational capability (an on-prem platform needs MLOps ownership — if that team doesn't exist, the project fails independent of the hardware), and utilization risk (a fleet sized for peak sits idle at trough; the cloud never does).
Mature enterprise deployments in 2026 converge on the same shape:
1. An internal gateway (OpenAI-compatible) that all applications call — one URL, centralized auth, logging, and routing policy. 2. Owned/colocated open-model capacity behind it for the bulk tier: internal assistants, RAG over corporate knowledge, document pipelines, classification. This is 80–95% of token volume and the part where on-prem economics and governance both bite. 3. Frontier API contracts for the quality-critical tier, reached through the same gateway with data-classification rules enforced at routing time — sensitive classes never route out. 4. Rented GPU burst (RunPod, Lambda-class) for training runs and load spikes, so the owned fleet is sized for baseline, not peak.
This shape keeps the compliance surface small (one egress point), captures bulk-tier savings, and preserves frontier access where it earns its premium. It also de-risks the buy-in: start with the gateway plus rented capacity, prove utilization, then convert the baseline to owned hardware with real usage data instead of forecasts.
The calculator below models the scrappy end (consumer dual-GPU nodes) and scales machine count with your volume automatically — useful for directional TCO, not a substitute for capacity planning. For a real deployment, benchmark your actual workload mix on one node first (our benchmark data gives starting points), then multiply. And involve procurement early: GPU lead times and colo contracts, not software, set enterprise timelines.
At enterprise scale the honest framing is control vs capability: on-prem buys data sovereignty, vendor independence, and auditability at roughly comparable cost — if you have the platform team to run it. Cloud buys frontier quality and elasticity with a thicker compliance binder. The winning architecture is rarely either/or: an internal gateway routing bulk traffic to owned open-model capacity and the critical slice to contracted frontier APIs captures both, and lets usage data — not forecasts — drive each expansion decision.
Jakub Rusinowski, the founder of LLM Configurator, runs corporate workshops and lectures on deploying local LLMs — hardware sizing, model selection, compliance-friendly architectures, and hands-on setup for your team. Ask about a workshop, or join the biweekly local AI digest.
Methodology & assumptions. All cost figures are estimates from one shared model (lib/costCompare.ts): cloud costs = tokens/day × published per-1M-token prices at a 70/30 input/output split × 30 days; local costs = hardware amortized over 24 months with no resale value + electricity for load time only at your rate, with machine count scaled when volume exceeds one machine’s throughput. Cloud prices carry per-entry source URLs and verification dates; hardware prices come from the curated /build catalog (street prices with check dates). Real bills vary with usage mix, discounts, and idle power — treat break-even months as directional, not contractual.