Multi-Cloud, Vendor Lock-in, and Exit Strategies: Cloudflare, AWS, Azure, and Google Cloud

Vendor lock-in occurs when switching away from a cloud provider is prohibitively expensive or technically difficult. Lock-in has multiple dimensions: data lock-in (egress fees make moving data expensive), API lock-in (proprietary APIs require code rewriting), operational lock-in (team skills are provider-specific), and contractual lock-in (multi-year commitments with financial penalties). Some lock-in is the natural consequence of optimization; the risk is when lock-in removes your ability to choose.

AWS has the most extensive lock-in surface due to its breadth: DynamoDB, SQS, SNS, Step Functions, CloudFormation, IAM, and 200+ other services have proprietary APIs. Azure's deepest lock-in is through identity (Entra ID/Active Directory) and enterprise licensing. Google Cloud has the least proprietary lock-in among hyperscalers, with strong open-source commitments (Kubernetes, Knative, Istio). Cloudflare's lock-in is narrower (Workers API, Durable Objects) but real for edge workloads.

Mostly yes. Workers implements Web Standard APIs: Fetch API, Request/Response, URL, TextEncoder/TextDecoder, Crypto, Cache API, Streams API, and WebSocket. Code written for Workers' standard APIs is more portable than code written for Lambda's event handler model. However, Cloudflare-specific APIs (KV bindings, D1 bindings, Durable Objects, Workers AI) are proprietary and not portable. The degree of lock-in depends on how much of your code uses standard vs proprietary APIs.

The primary exit cost is data egress. Moving 100TB from AWS costs ~,500 in egress fees. From Azure: ~,700. From Google Cloud: ~0,000. From Cloudflare R2: /bin/sh. Beyond egress, exit costs include: engineering time to rewrite provider-specific integrations, retraining operations teams, migrating databases, and replacing proprietary services with equivalents. For a mid-size company, a full cloud migration typically takes 6-18 months and costs hundreds of thousands in engineering hours.

For most organizations, true multi-cloud (running the same workload across providers for redundancy) is not worth the complexity. It doubles infrastructure management, testing, and debugging overhead while providing resilience against a risk (total cloud provider outage) that is extremely rare. What IS valuable: using different providers for their strengths (Cloudflare for edge, AWS for backend, Google for analytics) — this is multi-provider, not multi-cloud, and the complexity is manageable because each provider handles a distinct workload.

The Bandwidth Alliance is Cloudflare's partnership with cloud providers to waive or reduce data transfer fees between Cloudflare and partner networks. Members include Google Cloud, Microsoft Azure, IBM Cloud, DigitalOcean, Vultr, and others. Notably, AWS is not a member. This means egress from Google Cloud or Azure to Cloudflare may be free or discounted, further reducing the cost of using Cloudflare in front of these providers.

Kubernetes provides a standard container orchestration API that works across all major clouds (EKS, AKS, GKE) and on-premises. Workloads defined as Kubernetes manifests can theoretically run anywhere Kubernetes runs. In practice, cloud-specific extensions (AWS ALB Ingress Controller, Azure Disk CSI, GKE Workload Identity) introduce provider-specific dependencies. Kubernetes reduces compute lock-in but does not eliminate lock-in from managed services (databases, queues, identity) that your applications depend on.

Not necessarily. Proprietary services (DynamoDB, Cosmos DB, Workers KV) often provide better performance, lower operational overhead, and lower cost than open-source equivalents you manage yourself. The right question is not 'does this create lock-in?' but 'is the value I get from this service worth the switching cost it creates?' For critical differentiating workloads, evaluate portability. For commodity workloads, use the best tool and accept the lock-in as an optimization trade-off.

R2 reduces lock-in in two ways: (1) S3-compatible API means existing S3 code works with R2 with minimal changes, and (2) zero egress fees mean moving data out of R2 costs nothing. If you store data on R2 and later decide to move it to S3, Azure Blob, or GCS, you pay /bin/sh in data transfer. This is the opposite of hyperscaler storage, where egress fees create a financial barrier to leaving. R2 is the most portable object storage from a cost perspective.