Don’t Let Infrastructure Bottlenecks Slow Your AI

When compute is scarce and data is costly to move, AI is harder to scale. Make it easier. Put GPUs and workloads close to users.

AI doesn’t run in one place. So why should your infrastructure?

Models, data, GPUs, APIs, and users span locations as AI moves into production. See what happens when you change how data moves, where workloads run, and the compute they require.

Watch video (00:58)

AI doesn’t run in one place. So why should your infrastructure?

Models, data, GPUs, APIs, and users span locations as AI moves into production. See what happens when you change how data moves, where workloads run, and the compute they require.

Watch video (00:58)

New research: How AI affects costs, strategy, and infrastructure decisions

See what IT leaders have learned as AI moves into production. Results from a survey reveal how they are responding to pressures from infrastructure and costs — and the shifts they must make to keep pace.

AI performance is becoming a business imperative

95% say AI experiences are essential to compete

As your AI moves further into production, your infrastructure must support more AI applications, greater complexity, and growing demand.

Centralized cloud limits AI performance for 86%

Distributed demand is testing infrastructure limits

Distributed cloud allows you to scale AI with top performance

96% plan to use multiple cloud providers. Diversifying infrastructure is the answer to meeting the demands of AI.

Better performance shouldn’t mean higher costs

90% make cost trade-offs for AI performance

61% rank portability as a top edge requirement

Dynamic, variable compute requires the flexibility to move workloads without creating new costs or other constraints. Portability diminishes trade-offs.

Cloud built for AI

Akamai gives you the distributed infrastructure to run AI workloads closer to users, place them where they perform best, and scale with greater control over performance, security, and cost.

Deliver faster, more responsive experiences

Place inference closer to users and data to reduce latency and support responsive, real-time AI experiences.

Move AI data without the high egress fees

Reduce data transfer costs as AI scales

Process more data closer to where it’s created to reduce unnecessary movement back to centralized infrastructure.

Increase GPU efficiency for inference workloads

Match AI workloads with the right compute resources to improve utilization and get more value from GPU capacity.

Scale production AI with Kubernetes

Run AI on Kubernetes-native infrastructure to coordinate compute, networking, and operations as workloads grow.

Cut data transfer costs for AI

Get up to US$5,000 in free cloud credits and put more of your AI budget toward workloads — not data transfer fees.

Secure GPU capacity for AI

Access NVIDIA Blackwell GPU capacity for demanding AI workloads before compute availability becomes a constraint.

The agentic web is here. Is your infrastructure ready?

Use these questions to evaluate where performance, cost, GPU efficiency, or infrastructure flexibility could be holding you back.

Slow AI experiences
Rising AI costs
Low GPU utilization
Infrastructure lock-in

Can you deliver the performance your users expect?

Latency can quickly become a problem when inference happens far from users and data. Consider whether your infrastructure can maintain responsive experiences as demand grows and becomes more distributed.

Next step: Learn how to match compute, workload, and location for better AI performance.

Watch webinar

On-demand webinar: Right compute. Right time. Right cost.

Can you scale AI without costs getting out of control?

Compute is only part of the equation. Data movement, utilization, and infrastructure choices can all increase costs as AI workloads grow.

Next step: See where your AI infrastructure stands today and where you may have opportunities to optimize.

Take assessment

Are you getting enough from your GPU capacity?

More GPU capacity doesn’t automatically mean better economics. Matching the right compute to the right workload can help improve utilization and make infrastructure spend work harder.

Next step: Get guidance on matching GPU capacity to your workloads.

Talk to a GPU expert

Talk to an Expert: Choose the right GPU for your workload

Can your workloads move as requirements change?

AI requirements evolve quickly. Consider whether your workloads can move across environments as performance, cost, and business requirements change — without forcing you to start over.

Next step: Explore the path from API-first inference to more flexible, distributed architectures.

Read report

Map your AI adoption path | Read the report

Get more from your AI infrastructure

Improve performance, control costs, and get more from your GPU capacity.

Assess your current setup
Explore the right GPU and deployment options
Plan for what comes next