Akamai CDN Deal with Anthropic Shows Changing Value of CDNs

Anthropic's $1.8 billion, seven-year agreement to use the Akamai network shows the new uses of edge computing for frontier language models.

You might recall that in early April 2026, the launch of Claude Managed Agents caused Fastly, Akamai and Cloudflare share prices to plummet, as investors apparently viewed Claude Managed Agents as a direct infrastructure competitor to these platforms.

• Fastly plunged 18 percent

• Akamai sunk 13 percent

• Cloudflare collapsed by 11 percent. 

As has been the case for enterprise software as a service, security stocks and then content delivery networks, investors worried about the substitution effects. 

Some of us might already guess that the fears, while rational, are likely overdone. Perhaps the main damage is the resetting of  valuation multiples back to prior levels.

Software Segment	New "Floor" P/E	Historical Context
Mega-Cap SaaS (Microsoft, SAP)	28x – 32x	Historic: 35x+
High-Growth / "Rule of 40" (ServiceNow, CrowdStrike)	45x – 55x	Historic: 80x – 100x+
Mature / Cyclical Enterprise (Salesforce, Oracle)	18x – 24x	Historic: 25x – 30x
Infrastructure / Dev Ops (Datadog, Snowflake)	50x – 60x	Historic: 100x+
Mid-Market / "Broken" SaaS	12x – 16x	Historic: 25x

To be sure, low latency remains the paramount value, as has been the case for content providers for decades. But where traditional content delivery networks have largely been about edge storage, AI models benefit more from edge compute for inference operations. 

Where traditional content delivery minimized latency by storing popular content at the edge, AI inference operations benefit by conducting inference operations at the edge.

Content providers such as Netflix or Pandora deliver mostly pre-encoded, cacheable files (videos, audio) that can be pre-positioned at edges with high hit rates. 

In contrast, AI inference operations are:

• highly dynamic, as

• each prompt is unique

• limiting the value of caching

• so the value shifts more to distributed compute and intelligent routing.

But edge computing also helps with scale and demand spikes. CDNs also provide such features as:

• global load balancing

• auto-scaling across points of presence

• burst capacity without over-provisioning central clusters

• reduced data transfer (less backhaul to origin)

• efficient routing

• potential caching of reusable elements

• lower bandwidth and egress costs

• security 

Traditional CDNs provided value by moving content bits efficiently. 

For frontier AI models, the value comes from edge computation.

Lower latency is still the chief value, though.