Monthly cloud vs on‑prem OpEx comparison for private AI

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Monthly cloud vs on‑prem OpEx comparison for private AI deployments

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Cloud private AI deployments typically start at hundreds to a few thousand dollars per month and scale linearly with usage, while serious on‑prem private AI tends to require much higher upfront spend but can be 30–50% cheaper per month at high, steady utilization once amortized. The right choice depends on workload size, predictability, and your appetite for capital expenditure and in‑house operations.

Below is a concise monthly OpEx comparison using realistic 2025 numbers.

Typical monthly ranges (illustrative mid‑market scenario)

Assume a mid‑market company running a 7B–13B model for internal assistants/RAG, with moderate but steady traffic.

Cloud private AI (VPC / managed GPUs)
- Small/medium workloads: roughly 1,000–20,000 USD/month across compute, networking, and storage, with the higher end driven by GPU hours and high concurrency.
- Example: hosting an LLM on AWS with multiple high‑end GPUs can reach ~71,000 USD/month at large scale.
- Pros: near‑zero capex, fast start, but can be 2–3× more expensive than on‑prem over several years at high, steady usage.
On‑prem private AI (owned hardware)
- Capex: a single high‑end H100 server is cited around 800,000+ USD upfront in some TCO studies.
- Operating costs: power and cooling for such a server are roughly 0.87 USD/hour, ≈630 USD/month at full 24/7 use, plus maintenance.
- Amortized over three years, hardware plus power and overhead commonly translates to mid‑five‑figure to low‑six‑figure annual OpEx, often delivering 30–50% savings vs cloud if GPU utilization consistently exceeds 60–70%.

Cost breakdown by category (per month)

Cloud private AI

Typical monthly OpEx components for a mid‑market private cloud deployment:

Compute (GPUs/CPUs)
- Managed cloud GPUs and supporting instances are usually 30–70% of total LLM cost.
- Guides report cloud LLM deployments ranging from about 1,000 up past 20,000 USD/month depending on GPU type, hours, and autoscaling.
Storage and networking
- Object and block storage (models, embeddings, logs) plus backup and egress typically add 10–35% to monthly cloud spend.
Platform and observability
- Managed databases/vector stores, gateways, and observability tools (logging, metrics, traces) can add hundreds to a few thousand dollars a month depending on volume and SLAs.

Cloud cost characteristics:

Highly variable with traffic and context length.
Public cloud bills often exceed budget estimates by around 15% due to under‑estimated utilization and hidden fees.

On‑prem private AI

Monthly OpEx for on‑prem is dominated by amortized hardware, energy, and staffing.

Hardware amortization
- Example TCO comparisons show an H100 system costing about 833,000 USD upfront; amortized over 3 years, this is ~23,000 USD/month before power and staff.
- For smaller GPU setups, monthly amortization can be much lower, but still typically in the five‑figure range for serious workloads.
Power and cooling
- For a high‑end GPU server, estimated at ~0.87 USD/hour (server + HVAC), about 630 USD/month at full continuous use.
- Multiple servers and racks scale this linearly.
Maintenance and overhead
- Hardware maintenance contracts, spare parts, and facility overheads add a few to several thousand dollars per month depending on scale.

Cost characteristics:

High fixed monthly cost once hardware is bought, relatively stable regardless of utilization.
When utilization is high and predictable, TCO analyses show ~30–50% lower three‑year cost than equivalent cloud deployments.

Team and operational costs (both models)

Whether cloud or on‑prem, you still need people:

A small core team (e.g., ML/LLM engineer, data engineer, MLOps/infrastructure) often exceeds 30,000–40,000 USD/month in total fully loaded cost in many markets.
Additional spend for security, compliance, and support can push AI operations personnel costs higher than pure infrastructure costs over time.

On‑prem generally requires more hands‑on infra and SRE work (hardware lifecycle, patching, capacity planning), while cloud reduces hardware overhead but still requires MLOps, governance, and optimization.

When does on‑prem beat cloud on monthly OpEx?

Recent TCO and build‑vs‑buy analyses converge on a similar pattern:

For low to moderate usage (roughly under 50,000 USD/year in projected API or cloud spend), cloud is usually cheaper and far simpler; hardware investment does not pay back.
Between roughly 50,000 and 500,000 USD/year in LLM spend, a hybrid model (cloud for experimentation and peaks, limited private hosting for heavy or sensitive workloads) often optimizes cost and flexibility.
Above ~500,000 USD/year with steady demand, well‑utilized on‑prem clusters often achieve 30–50% savings vs cloud over a three‑year horizon, despite higher operational complexity.

In practice, many organizations end up with a hybrid model: core, high‑volume workloads on private or dedicated infrastructure; experimentation and spike workloads in cloud.

Summary snapshot (monthly OpEx)

Aspect	Cloud private AI (monthly)	On‑prem private AI (monthly, amortized)
Typical infra range	≈ 1k–20k+ USD for mid‑market workloads	Mid‑five‑figures for serious GPU setups (incl. amortization)
Cost behavior	Variable with usage, can spike 2–3× at scale	Fixed, predictable; cheaper at high utilization
Power & cooling	Included in cloud rates	Hundreds to thousands USD (e.g., ≈630 USD/month per high‑end server)
Team requirements	MLOps, data, security; moderate infra overhead	All of cloud roles plus hardware/SRE responsibilities
Best suited for	Burst/uncertain workloads, fast start, lower capex	Steady, high‑volume workloads, strict data control, long horizon

Designing a private AI deployment with monthly OpEx in mind means sizing models and infrastructure to real workloads, using cloud for uncertainty and peaks, and only moving to heavy on‑prem when long‑term, high‑volume demand makes the higher operational overhead financially worthwhile.

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