Comparison of Google and Meta approaches to networking and infrastructure (Category Architecture)

In this post, I decided to compare Google’s approaches to its network architecture. The point is that both of these companies 2025 This year, I wrote articles on how infrastructure is changing to meet the challenges of the AI era, and I’ve discussed both.

• Meta’s Hyperscale Infrastructure: Overview and Insights
• Google's AI‑powered next‑generation global network: Built for the Gemini era

Generally speaking, both companies agree that their global infrastructure should work as a single entity. They even have the same slogans. WAN is the new LAN, the continent has become a data center Meta: All global data centers are one computer

But when implemented, the emphasis of Google and Meta is different:

1. The scale of the network and who it is for Both Google and Meta have built their own fiber-optic global networks linking data centers directly instead of relying on the public internet. Both aim to place nodes closer to users. (caches, PoP) for low delays. But Google makes Google Cloud for itself and its customers, and Meta only for itself and its products.

2. Scaling architecture Companies Approaches to Scalability WAN Very similar in concept, although implemented by their own methods. At the LAN level inside the DC everything is similar and oversubscription is not - both companies use scalable factory topologies. (Clos/fat-tree) and add switches on the upper levels At the WAN level, Google has shards, Meta has separate planes, but Google has no oversubscription at the WAN level, and Meta has separate planes. (This affects the possibility/impossibility of distributed learning foundation models.)

3. Reliability and updates In both companies, the network is designed with the idea of localizing problems and quickly repairing themselves. Google is talking about the autonomous network – the automatic response of the network itself to problems. The challenge is to make ultra-high reliability (beyond 9s) And this requires an autonomous system that has self-healing capabilities. Meta talks about network configuration automation – the ability to quickly change configuration and software without compromising work. That is, the level of automation is closed here, but changes must be initiated by a person.

4. Integration with AI loads Both giants are realizing that artificial intelligence is dictating new infrastructure requirements. However, approaches are different. Google’s network allows distributed training and can scale horizontally The Meta network allows you to distribute all loads, except for training large models. There, the guys focus on scaling through scale-up inside the DC. Next, they plan to complete the network for distributed training opportunities

5. Programmability of decisions Both players apply the principles of software-defined networking and control automation. But there is a difference. Google has many different customers. (Considering Google Cloud)Therefore, they needed convenient centralized management of network policies for different tasks. (Whether it’s cloud clients or internal services) Meta also has central controllers for network management – they constantly optimize the distribution of traffic from users. (PoP) To data centers, taking into account the load and delays, and in the data centers themselves, the controller can change routes during congestion or failures.

In summary, Google and Meta are going on parallel courses: they solve similar problems of a hyper-scale network, sometimes using different methods, but the overall goal is the same – a network capable of connecting the whole world into a single “computer” for their services and future AI applications. But the way companies publish results is very different. Google publishes scientific articles and sells commercial service, but does not publish tool code or hardware design Meta actively shares hardware designs through the community Open Compute Project, and also publishes many of its developments: frameworks, databases

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