Now scheduling workload assessments

We make AI inference profitable and predictable.

We help AI-native companies and enterprises achieve capital efficiency with AI inference — reducing cost volatility and improving global performance by shifting from centralized cloud models to inference at the edge. Powered by NVIDIA Blackwell GPUs on Akamai's distributed cloud, fully managed by MobileRider.

Get GPU access See the economics

The problem — centralized cloud

Inference costs are outpacing revenue.

AWS, Azure, and GCP are pouring $364B into centralized AI infrastructure — creating cost structures and latency profiles that increasingly constrain enterprise AI at scale. The numbers from the field:

$1.64

spent on inference per $1.00 of revenue

AI-native companies are running cost structures that prevent margin formation at scale — compute grows faster than the business it powers.

$99M vs $11M

One AI platform's actual year

A 9-to-1 cost-to-revenue ratio, driven entirely by centralized inference economics.

150–500ms

Centralized inference latency

3 to 10× slower than edge execution — unsuitable for real-time AI where responsiveness directly impacts engagement and revenue.

$50M+

Cost to leave once locked in

Proprietary APIs, tooling, and egress economics make centralized inference expensive to unwind, raising long-term TCO.

The fix — inference at the edge

Same workloads. Different economics.

NVIDIA Blackwell GPUs on Akamai's distributed cloud, managed by MobileRider:

1.63xhigher inference throughput vs. H100

~20–40msP50 latency in edge-eligible workloads

$0.005/GBegress — no hyperscaler tax

4,400+edge locations worldwide

The way out: right-sized inference at the edge

Akamai's distributed cloud puts NVIDIA RTX PRO™ 6000 Blackwell GPUs closer to your users than any hyperscaler region — and right-sizing the GPU to the workload is how the economics flip. Match your workload to the right GPU to avoid overprovisioning and control inference costs.

Get the right GPU

Blackwell supports large-scale inference, while NVIDIA RTX™ 4000 Ada balances price and performance. Use our guide to find the right NVIDIA GPU for the job.

Find your GPU →

In the right shape

Matching the shape and card count to your specific memory and throughput needs is essential for maximizing GPU utilization.

View card plans →

At the right price

GPUs start at $0.52/hour — the same price as buying direct from Akamai, with MobileRider's 24/7/365 management included.

See pricing →

Know what your workload needs before you spend.

Every engagement starts with a workload assessment. Our team of AI domain experts evaluates your target workload and recommends the right configuration, with every number visible.

STEP 1

Tell us about the workload

Model size and type, throughput, latency targets, and where your users are.

STEP 2

Our experts assess

We map your workload against NVIDIA's guidance and Akamai's published benchmarks.

STEP 3

You get a recommendation

The right GPU shape and card count, transparent pricing, and a provisioning plan.

STEP 4

You decide

Start with a single card on hourly billing. Scale when the numbers work for you.

How the workload assessment works →

Why MobileRider

Akamai provides the grid. We bring the operators, playbooks, and measurement to make it real — a team that has run mission-critical streaming and media infrastructure on Akamai's network for over a decade.

Provisioned for you

From access request to running workload in days — including Kubernetes (LKE) setup, the AI software stack, and routing to the closest suitable GPU regions.

Managed 24/7/365

White-glove monitoring, governance, metering, and routing rules for cost control — from the team behind MobileRider Managed Security.

Same price as direct

Akamai's published rates, verified monthly. You pay nothing extra for the management layer.