However, organizations must weigh the trade-offs of deepening serverless adoption, especially with proprietary abstractions like durable functions. Serverless models promote agility and efficiency, but can also increase vendor dependence. For example, migrating complex workflows from AWS Lambda durable functions to another cloud platform (or back to on-premises infrastructure) will be costly and complex because the code relies on AWS-specific APIs and orchestration that don’t translate directly to Microsoft Azure, Google Cloud, or open source options.
There’s also a broader architectural consideration. Serverless, by its very nature, expects statelessness and composability, but it also introduces new patterns for observability, testing, and operational troubleshooting. While AWS Lambda durable functions make workflow orchestration less burdensome, they also increase the “magic” that must happen behind the curtain, sometimes making debugging and understanding cross-step failures more challenging. Enterprisewide visibility, compliance, and cost control require investments in new monitoring practices and possibly some third-party or proprietary tools.
Pros and cons of serverless lock-in
Some in the cloud community have taken a myopic approach to vendor lock-in, sounding alarms at any whiff of proprietary technology adoption. In reality, completely avoiding lock-in isn’t practical, and seeking absolute portability can undermine access to genuine innovation, such as Lambda durable functions. The calculus should focus on risk management and exit strategies: Does the value delivered by automation, embedded error recovery, and operational efficiency justify the increased dependency on a specific cloud provider at this stage of your evolution?
