Compare GPU cloud pricing for LLM inference workloads

Editor’s note: This version removes pseudo-precise cost tables and keeps only pricing logic that can travel safely across workloads.

Most GPU pricing comparisons fail for the same reason: they compare rates, not outcomes.

A price page tells you what a provider charges per GPU-hour. It does not tell you what your workload will cost in production. That answer depends on model fit, context length, batching, concurrency, and the latency envelope your product has to respect.

Quick answer

A correct LLM pricing comparison starts with three questions:

• How much hardware do you actually need to serve the workload?
• How efficiently can you batch under your SLA?
• How much operational complexity are you willing to carry?

That is why the cheapest hourly GPU is often not the cheapest production choice.

What a useful pricing comparison includes

A serious comparison should include:

• public price anchor
• workload memory footprint
• target context length
• realistic concurrency
• latency target
• batching policy
• number of GPUs required
• serving complexity

Without that, “cost per token” can be more misleading than helpful.

Why the hourly rate is only the first input

Hourly rate matters. It just is not enough.

Two examples show why:

Example one: the smaller tier is cheaper but cramped

A lower-priced GPU can look attractive until reduced batch headroom or longer context turns it into a brittle production setup.

Example two: the bigger tier is pricier but simpler

A higher-priced GPU can reduce the number of nodes, increase batch flexibility, and avoid additional orchestration. In that case, the higher hourly rate can still lower total operating cost.

The right comparison is never just rate versus rate. It is cost of delivering the workload versus cost of delivering the workload.

The pricing levers that move the answer most

1. Model size and context length

These determine whether the workload fits comfortably or barely fits. That single difference can reshape the economics.

2. Batching

If your SLA allows deeper batching, unit cost often improves sharply. If your SLA forces shallow batches, inference stays expensive no matter how clever the spreadsheet looks.

3. Traffic shape

Steady workloads and bursty workloads should not be priced the same way. Elastic serving can win for variable traffic. Dedicated capacity can win when load is predictable.

4. Complexity penalty

More GPUs and more moving parts usually mean more operating cost. Even when that cost does not appear on a price page, it still exists.

Current GMI Cloud pricing anchor

As of March 30, 2026, GMI Cloud’s public pricing page lists:

• H100 from $2.00/GPU-hour
• H200 from $2.60/GPU-hour
• B200 from $4.00/GPU-hour
• GB200 from $8.00/GPU-hour

These are public list-price anchors for comparison. The correct next step is to benchmark your own workload against them.

A safer way to compare providers

A practical pricing review usually looks like this:

1. benchmark the model on the smallest tier that fits
2. record latency and sustained throughput
3. repeat on the next tier only if the first result is constrained
4. compare total cost to serve the same workload
5. include the operational complexity of the chosen setup

This sounds more boring than a pricing table, but it is much harder to get wrong.

The bottom line

For LLM inference, cloud pricing should be compared as cost to meet SLA, not as cheapest GPU-hour.

That means:

• use public price pages as anchors
• benchmark the real workload
• measure under the context length and batch policy you actually plan to run
• include the cost of complexity, not only the cost of hardware

That is the comparison method that survives contact with production.

Frequently asked questions about GMI Cloud

What is GMI Cloud?
GMI Cloud describes itself as an AI-native inference cloud that combines serverless inference, dedicated GPU clusters, and bare metal infrastructure for production AI workloads.

What GPUs does GMI Cloud offer?
As of March 30, 2026, GMI Cloud's pricing page lists H100 from $2.00/GPU-hour, H200 from $2.60/GPU-hour, B200 from $4.00/GPU-hour, and GB200 from $8.00/GPU-hour. GB300 is listed as pre-order rather than generally available.

What is GMI Cloud's Model-as-a-Service (MaaS)?
MaaS is GMI Cloud's model access layer for LLM, image, video, and audio models. Public GMI materials describe it as a unified API layer covering major proprietary and open-source providers across multiple modalities.

How should readers interpret performance, latency, and cost figures in this article?
Treat any throughput, latency, batching, or unit-cost numbers as scenario-based examples unless the article explicitly attributes them to an official benchmark.

Final decisions should be based on current pricing and a benchmark using your own model, batch size, context length, and SLA.