In addition, they are prone to following fallacies:[6][7] Monitoring and debugging serverless applications can present unique challenges due to their distributed, event-driven nature and proprietary environments.
However, modern solutions such as distributed tracing tools (e.g., AWS X-Ray, Datadog), centralized logging, and cloud-agnostic observability platforms are mitigating these challenges.
Emerging technologies like OpenTelemetry, AI-powered anomaly detection, and serverless-specific frameworks are further improving visibility and root cause analysis.
This issue is exacerbated in serverless computing, as with its increased level of abstraction, public vendors only allow customers to upload code to a FaaS platform without the authority to configure underlying environments.
For workloads requiring sustained or predictable resource usage, bulk-provisioned servers can sometimes be more cost-effective than the pay-per-use model typical of serverless platforms.
However, serverless computing is increasingly capable of supporting specific HPC workloads, particularly those that are highly parallelizable and event-driven, by leveraging its scalability and elasticity.
[16][17][18] The "Grain of Sand Anti-pattern" refers to the creation of excessively small components (e.g., functions) within a system, often resulting in increased complexity, operational overhead, and performance inefficiencies.
In contrast, published interfaces involve an explicit stability contract, including formal versioning, thorough documentation, a defined deprecation policy, and often support for backward compatibility.
Published interfaces may also require maintaining multiple versions simultaneously and adhering to formal deprecation processes when breaking changes are introduced.
However, this approach is noted to involve a steeper learning curve, and integration limitations may vary even within the same cloud vendor ecosystem.
However, shared resources with long spin-up times, such as AWS RDS clusters and landing zones, are often managed in separate repositories, deployment pipeline, and stacks.