In December 2025, an unbelievable announcement was made during the AWS re:Invent 2025 keynote in Las Vegas. The CEOs of AWS and Google Cloud (GCP) took the stage together to announce a “Mutual Connectivity” agreement.

This is akin to Coke and Pepsi declaring they will “use the same vending machine.” As a Cloud Architect, I analyze the impact this ‘sleeping with the enemy’ moment will have on the enterprise IT market.

1. The 10-Year Wall Collapses: End of Data Egress Fees?

The biggest barrier blocking multicloud adoption hasn’t been technology, but ‘money’. To analyze data stored in AWS using GCP’s AI models, one had to pay massive ‘Data Egress Fees’ to AWS every time data was extracted. This was the core of vendor lock-in.

However, with this agreement, a Direct Connect Bridge between the two clouds is opened.

• Change: Data transfer costs between AWS and GCP regions are reduced by 90% compared to before.
• Speed: By directly connecting the backbone networks of both companies rather than using the public internet, latency has been reduced to less than 2ms.

2. Why Now? Explosion of AI Workloads

Behind this historic agreement lies ‘AI’. Customers want to use AWS’s massive storage (S3) and GCP’s powerful AI models (Gemini, TPU) simultaneously.

2.1 Customer Outcry

“We have 10PB (petabytes) of data in AWS S3, but training it on GCP Vertex AI costs billions of won just in transfer fees.” As such complaints skyrocketed, both companies chose a ‘win-win’ strategy to grow the market by leveraging their respective strengths. AWS maintains leadership in storage and infrastructure, while Google captures AI workloads.

3. Changes from a FinOps Perspective

Now, corporate cloud cost management (FinOps) strategies must be completely revised.

3.1 Realization of Best-of-Breed Strategy

In the past, “digging one well” was cost-effective. But now, it is possible to pick and choose the ‘cheapest’ functions from each cloud.

• Storage: AWS S3 One Zone-IA (Cheap Archiving)
• Compute: GCP Spot VM (Cheap Computation)
• Database: AWS Aurora Serverless
• AI Inference: GCP TPU v6e

These components can now be woven together as if they were in a single data center.

4. Architecture Pattern Change: Cross-Cloud

Architects no longer need to worry about Single Point of Failure. A truly ‘Active-Active’ multicloud configuration has become easier.

# Hypothetical Multicloud Kubernetes Config
apiVersion: networking.k8s.io/v1
kind: Ingress
metadata:
  name: global-ingress
spec:
  rules:
  - host: api.techdepend.com
    http:
      paths:
      - path: /data
        backend:
          serviceName: aws-s3-proxy # Route to AWS Region
      - path: /analyze
        backend:
          serviceName: gcp-vertex-ai # Route to GCP Region

Not only traffic routing, but real-time database synchronization also becomes much cheaper and faster. A structure where GCP immediately takes over if AWS goes down, and vice versa, will become popularized.

5. Remaining Challenges: Security and Unified Management

Of course, the future isn’t entirely rosy.

1. IAM Integration: Mapping AWS IAM Roles and GCP IAM Service Accounts is still complex. The market for ‘Unified Identity’ solutions to solve this will grow.
2. Monitoring: A unified dashboard (Observability) that can visualize traffic moving between two clouds at a glance is essential. The importance of third-party tools like Datadog or New Relic has increased.

6. Conclusion: Isolation of MS Azure?

Microsoft Azure was missing from this announcement. The allied front of AWS and Google is largely seen as a move to check MS, which is running solo with OpenAI on its back.

The cloud market is now being reorganized into a two-way race: [AWS+Google] vs [MS Azure]. For corporate consumers, the options have expanded.

Don’t be trapped in a specific cloud anymore. In 2026, a hybrid strategy of ‘Data on AWS, Brain on Google’ will become the standard. Re-draw your infrastructure architecture right now.