The Pros and Cons of Serverless Computing: A Closer Look

Serverless computing has emerged as an innovative approach to deploying applications and services. By separating the server layer from the application process, serverless enables developers to focus on building features without managing infrastructure. While this model offers compelling benefits like cost savings and operational efficiency, it also comes with drawbacks to consider. In this post, we'll examine the key advantages and disadvantages of serverless computing.

What is Serverless Computing?

In serverless architectures, the cloud provider dynamically allocates machine resources to run code in response to requests or events. The provider handles all server management tasks like provisioning and maintenance. You only pay for the compute time used to execute your code.

Some key characteristics of serverless computing:

• Event-driven: Code execution is triggered by events like HTTP requests, database changes, file uploads, etc. This allows the application to respond to real-time data inputs and scale based on activity, rather than having to run continuously.
• Ephemeral: Compute resources are provisioned temporarily for each execution. This means containers or virtual machines are spun up on-demand when a function is invoked and shut down when it completes. No idle capacity sits around unused.
• Scalable: Serverless applications can scale seamlessly based on demand. The cloud provider monitors traffic and automatically allocates more serverless compute power to handle spikes. This auto-scaling ensures consistent performance at any volume.
• Managed infrastructure: The provider handles infrastructure maintenance and capacity provisioning. Developers don't have to worry about server patching, scaling, or managing resource pools.
• Usage-based pricing: Pay only for the compute time consumed during executions. There is no charge when code isn't running, which minimizes costs.

Popular serverless services include:

• AWS Lambda - The first major serverless platform. Provides functions with automatic scaling, performance monitoring, and integration with AWS services.
• Microsoft Azure Functions - Integrates seamlessly with Azure PaaS offerings like storage, security, and cognitive APIs.
• Google Cloud Functions - Enables building event-driven applications using Google's machine learning capabilities.

Serverless architectures typically rely on microservices - modular components that work together through APIs. This makes it easy to connect specialized functions into a larger application.

Here are the pros and cons of serverless computing.

Benefits of Serverless Computing

Cost Efficiency

With serverless, you avoid paying for idle capacity when demand is low. The pay-per-use model brings significant cost savings compared to purchasing dedicated servers or containers that run 24/7 regardless of traffic volumes. Studies show serverless can reduce infrastructure costs by 70% or more for intermittent workloads.

The granular billing per execution makes resource costs predictable and easy to optimize based on real usage data. The savings add up quickly for organizations running multiple applications with variable workloads.

Operational Efficiency

Serverless removes the need to provision and manage infrastructure. No time is spent on server admin tasks like patching, scaling, or managing resource pools. Streamlined workflows let developers focus on building features instead of ops work. This accelerates deployment and productivity.

The automated scaling delivered by serverless platforms provides the right-sized capacity for workloads of any size. Sudden spikes won't take down applications. This resilience enables teams to deliver robust, reliable applications faster.

Scalability

Serverless applications can scale seamlessly based on demand. The cloud provider monitors traffic and resource utilization to allocate more computing power instantly when needed. This auto-scaling ensures consistent performance at any volume without capacity planning.

Serverless platforms make it easy to distribute processing across thousands of compute nodes for massive parallelism. This enables use cases that require crunching vast amounts of data like IoT analytics. Geo-distributed functions also improve responsiveness.

Simplified Code

Serverless enables developers to focus on writing lean, modular backend code. The simplified codebase with discrete functions is easier to develop, deploy, and maintain.

Code executes in ephemeral containers, so there's no need to provision infrastructure dependencies like databases or message queues. All the scaffolding code is replaced with a few configuration lines. This reduces complexity.

Serverless also eliminates the need to manage complex distributed application architectures. The platform handles load balancing, failover, and other coordination tasks behind the scenes.

Rich Ecosystem

Major cloud providers offer a suite of integrated services like databases, analytics, machine learning, monitoring tools, and more. This provides a rich ecosystem to incorporate into serverless applications with just a few lines of configuration.

Pre-built functions and templates make it easy to connect complementary services. For example, a simple e-commerce site could use functions for user authentication (Auth0), payments (Stripe), email (SendGrid), and more.

Drawbacks of Serverless Computing

• Performance Issues: Cold starts can cause delays when initializing unused functions. Latency variability is a concern for applications with critical response times. However, options like provisioned concurrency can mitigate cold starts.
• Vendor Lock-in: Reliance on proprietary tools and services makes it difficult to switch cloud providers without significant rearchitecting. Multi-cloud deployment tools are emerging to address portability concerns.
• Limited Flexibility: There is little control over the underlying infrastructure. Serverless platforms have restrictions around runtimes, execution environments, and more. Workarounds exist, but lack the flexibility of IaaS.
• Security Considerations: The distributed nature of serverless functions increases potential attack surfaces. Identity management and secrets management are growing more complex. Providers' security measures are critical.
• Monitoring/Debugging Challenges: Distributed functions and the ephemeral nature of resources make monitoring, logging and debugging more difficult in serverless apps. New tools are helping, but remain a challenge.
• Architectural Constraints: Serverless imposes limits on execution duration, memory/CPU usage, and disk space. Workloads that don't fit within constraints require rearchitecting. State management also requires design changes.

Key Assessment Criteria Before Adopting Serverless

Carefully evaluating the readiness of your organization, applications, processes and staff skills is crucial before shifting to serverless architectures. Key considerations:

• Impact on existing operational workflows and cost management - evaluate tooling and process changes needed.
• Application architecture and feasibility of restructuring for serverless - decompose monoliths into microservices and assess state management needs.
• Staff skills in cloud-native development and need for training - serverless use new patterns like functions as a service and event-driven programming.
• Serverless platform choice based on language support, tooling, pricing and community - the major clouds have similar core capabilities but differ in ecosystems.
• Security, compliance, and regulatory requirements - review provider measures and additional controls are needed.
• Workload profiles and cost/performance requirements - variable and bursty workloads are best suited to serverless architectures.

Thoroughly test potential pain points like cold starts, debugging, and vendor lock-in during any pilot project. This will reveal the real-world implications before a full production rollout.

Everyday Use Cases Showcasing Serverless Benefits

Here are some common scenarios where businesses can benefit from using serverless computing:

• Data processing - Serverless functions scale massively for processing large datasets, streaming analytics, and ETL workflows. Usage-based billing is cost-efficient for bursty workloads.
• Web applications - API gateways and functions handle fluctuating traffic efficiently. No idle servers sit unused during low-traffic periods. Geo-distributed functions provide low-latency responses.
• Mobile apps - Serverless backend services power features like image processing, push notifications, and chatbots cost-effectively.
• IoT data processing - Functions triggered by sensor data enable real-time processing at any scale. No infrastructure management is needed.
• ETL pipelines - Replace cron jobs with functions that trigger data transformation flows upon events like file arrival. Handles variability in workload volumes flexibly.

Choosing the Right Serverless Platform

Platform	Key Features
AWS Lambda	Original serverless pioneer with seamless AWS integration. Massive scale and auto-scaling capabilities.
Azure Functions	Powerful event-driven computations with Azure service integrations. Enterprise-friendly capabilities.
Google Cloud Functions	Leverage Google's machine learning expertise. Strong for data processing workloads.

When evaluating options, consider language support, pricing models, and overall ecosystem maturity. Vendor lock-in is a major factor as well - portability between platforms remains challenging.

AWS Lambda is the most mature and widely-adopted serverless platform currently. However, Azure Functions and Google Cloud Functions also have compelling capabilities and continue to evolve rapidly.

Open-source options like OpenFaaS are also gaining interest in avoiding vendor lock-in. But they lag behind proprietary platforms in features and ecosystem breadth.

Architecting Serverless Applications

Adopting a serverless architecture requires changes in how applications are designed, built, and operated. Here are key considerations:

• Stateless logic - Serverless functions should be stateless, getting any required data from parameters or external storage like databases. State machines can manage multi-step workflows.
• Ephemeral infrastructure - With no permanent virtual machines, infrastructure dependencies like databases or queues must be externalized as managed services.
• Event triggers - Functions are invoked in response to events like HTTP requests, database changes, queue messages, file uploads, scheduled timers, etc.
• Atomic functions - Break processing into small, discrete functions of limited duration. Chain together to form workflows.
• Error handling - Plan for retries, failovers, and alerts to handle errors cleanly in a distributed environment.
• Monitoring - Instrument functions to log metrics, and traces for observability into the system. This is critical for debugging.
• Security - Control permissions minimally per function, validate inputs, and manage secrets appropriately to limit attack surfaces.

Conclusion

While serverless can deliver greater efficiency and agility, it also brings unique challenges around performance, security, and vendor dependence. Assess organizational readiness and application needs before adopting. When thoughtfully implemented, serverless enables developers to focus on delivering business value vs. just “running servers.” The automated scaling and usage-based cost model provide cloud-like agility with operational simplicity. Serverless computing is proving to be a versatile option for workloads that fluctuate or require massive parallel processing.