AWS Inferentia (the Inf2 family) usually beats a comparable GPU instance on cost per token for steady, high-volume inference, because the hourly rate is lower for similar sustained throughput once the model is compiled with the AWS Neuron SDK. GPUs win when traffic is bursty or low, when the model will not compile to Neuron, or when portability across clouds matters. The decision is never the spec sheet; it is your measured tokens per second at target latency divided into the hourly price. In 500+ environments, the teams that benchmark both on their real traffic, rather than trusting a vendor chart, are the ones that cut inference cost without breaking latency.
Last updated: June 2026
Cost per token is the metric that matters for inference. It is the instance hourly price divided by the number of tokens that instance can produce per hour at your target latency. AWS Inferentia and NVIDIA GPUs reach that number by different routes: Inferentia trades a lower hourly price and a required compilation step for strong throughput on supported models, while GPUs cost more per hour but run almost anything with no porting work. Whichever produces more usable tokens per dollar on your specific model and traffic is the right answer, and it varies more than most teams expect.
This article is part of our AI, GPU and ML cluster. For the full picture start with the complete guide to AI and GPU cost optimization, the pillar this piece links up to. For the training side of the same choice, see our companion comparison of AWS Trainium vs GPU for training.
What is the difference between AWS Inferentia and a GPU?
AWS Inferentia is a custom inference accelerator built by AWS, exposed through the Amazon EC2 Inf2 instance family and programmed with the AWS Neuron SDK. A GPU is a general-purpose parallel processor, available on AWS through the G and P instance families, that runs CUDA and the standard ML frameworks directly. The practical difference is that Inferentia is cheaper per hour and purpose-built for serving, but requires you to compile and sometimes adapt the model to Neuron first. A GPU costs more per hour and runs your model as-is. You can confirm current Inf2 specs on the Amazon EC2 Inf2 page and check model support in the AWS Neuron documentation.
How do the hourly prices compare?
Inferentia carries a lower on-demand hourly price than a GPU instance of similar serving capacity. As of June 2026, an inf2.xlarge lists at roughly 0.76 USD per hour and a full inf2.48xlarge at roughly 12.98 USD per hour, per the AWS EC2 on-demand pricing page. A GPU inference instance such as a g5.xlarge (one NVIDIA A10G) lists near 1.01 USD per hour, while the large training-class GPU boxes like p4d.24xlarge (eight A100) run about 32.77 USD per hour. Hourly price alone is misleading though, because what you pay for is tokens, not hours. Verify the live rate for your region before modeling, since AWS updates these regularly.
| Decision criterion | AWS Inferentia (Inf2) | GPU (G / P family) |
|---|---|---|
| Hourly price | Lower for similar throughput | Higher per hour |
| Setup effort | Compile to Neuron first | Runs model as-is |
| Model coverage | Neuron-supported architectures | Effectively everything |
| Best traffic shape | Steady, high volume | Bursty, low, or spiky |
| Portability | AWS only | Portable across clouds |
| Cost per token | Usually lower when busy | Lower when underused |
Verdict: for steady, high-volume inference on a Neuron-supported model, Inferentia is normally the cheaper cost per token. For low or unpredictable traffic, unsupported models, or multi-cloud portability, a right-sized GPU wins.
Why does cost per token depend on utilization, not the price tag?
Cost per token falls as the accelerator stays busy, because you pay for the hour whether the chip is working or idle. A cheaper-per-hour Inferentia instance that sits at twenty percent utilization can cost more per token than a pricier GPU running near capacity. This is why a like-for-like spec comparison is worthless on its own: the winner is decided by your sustained tokens per second at the latency you promise users, divided into the hourly price. Bursty traffic that leaves either accelerator idle for long stretches erodes the advantage of the cheaper hourly rate, which is the same idle-capacity problem we cover in what is GPU utilization and why idle accelerators cost so much.
Not sure which accelerator your inference should run on?
Our cost audit benchmarks your real model and traffic on Inferentia and GPU, reports measured cost per token at your target latency, and tells you which to run and where to keep a GPU fallback. On the performance model, you pay only from realized savings. No savings, no fee.
Book a cloud cost audit →When does a GPU still make more sense than Inferentia?
A GPU is the better choice when the porting cost or rigidity of Inferentia outweighs the hourly saving. Three cases dominate. First, the model will not compile cleanly to Neuron, or depends on custom operators the SDK does not support, so the engineering effort is unbounded. Second, traffic is low or spiky, so neither accelerator stays busy and the simpler GPU path wins on total effort. Third, the workload must stay portable across clouds or move on-premises, which an Inferentia-bound deployment cannot do. In each case the GPU costs more per hour but less in total once you count engineering time and flexibility.
How do I actually test cost per token on my own model?
Benchmark both, identically, on your real traffic. Deploy the same model on an Inf2 instance compiled with Neuron and on a comparable GPU instance, send a representative prompt mix at your real concurrency, and measure sustained tokens per second at the latency your product requires. Then compute cost per token as the hourly price divided by tokens per hour. Do this at production concurrency, never a single request, because utilization is the variable that decides the outcome. The same disciplined measurement underpins our broader method for right-sizing GPU instances for model serving, and it is the step most teams skip.
The AI and GPU Cost Control Guide includes our cost-per-token benchmarking worksheet for Inferentia and GPU, with the utilization break-even math. It is the downloadable companion to this article.
Frequently asked questions
Is AWS Inferentia cheaper than GPU for inference?
Inferentia is usually cheaper per token than a comparable GPU instance once your model is compiled with AWS Neuron and the accelerator stays busy, because the hourly rate is lower for similar throughput. The saving disappears if the model will not compile cleanly or if traffic is too low to keep the chip utilized, in which case a small GPU instance is simpler and can cost less.
What models run on AWS Inferentia?
Inf2 runs models supported by the AWS Neuron SDK, which covers most mainstream transformer architectures for text generation, embeddings, and classification, plus many vision models. Models that depend on unsupported custom operators or the newest research architectures may not compile, so confirm support in the Neuron documentation before committing.
How do I measure cost per token on each option?
Run the same model and a representative prompt mix on both instance types, measure sustained tokens per second at your target latency, then divide the instance hourly price by tokens per hour. Cost per token equals hourly price divided by tokens per second times 3600. Always test at your real concurrency, not a single request, because utilization drives the result.
Does Inferentia work outside AWS?
No. Inferentia is an AWS-only accelerator, so a workload built on it is tied to AWS and the Neuron toolchain. GPUs are portable across clouds and on-premises, which is a real consideration if avoiding lock-in matters to your architecture.
The short version
For steady, high-volume inference on a Neuron-supported model, AWS Inferentia normally delivers the lower cost per token. For low or bursty traffic, unsupported models, or workloads that must stay portable, a right-sized GPU is the safer and often cheaper choice once engineering time is counted. Benchmark both on your real traffic before you decide. When you want that benchmark run and the cheaper option put into production safely, that is exactly what our FinOps implementation service delivers.
Written by Fredrik Filipsson and reviewed by Morten Andersen, applying the See, Cut, Lock, Run method. Independent and vendor neutral. Figures verified against AWS documentation as of June 2026; verify current pricing before relying on it.
Cloud pricing and service behavior change frequently. Verify the specifics in this guide against the providers’ own current documentation and the FinOps Foundation: AWS pricing ↗, AWS documentation ↗ and FinOps Foundation Framework ↗. This article also reflects Cloud Cost Room’s hands-on, vendor-neutral engagement experience.