Serverless Computing Pros and Cons : 2024

Serverless computing has grown tremendously in adoption and capabilities over the past few years. As we enter 2024, serverless continues to offer important benefits but also comes with certain downsides to consider. Below we outline the key pros and cons of serverless computing to help guide technology decision makers.

Serverless Computing

What is Serverless Computing?

Before diving into the specific advantages and disadvantages, it helps to level set on what exactly serverless computing entails.

Serverless computing is a cloud computing execution model where the cloud provider dynamically manages the allocation and provisioning of servers. A serverless application runs in stateless compute containers that are triggered event driven, ephemerally provisioned, and auto-scaled.

This means developers can deploy application code without having to manage the underlying infrastructure. The cloud provider handles provisioning, scaling, patching, operating system maintenance, capacity planning, etc behind the scenes.

Pros of Serverless Computing

Here are some of the most impactful benefits that come with leveraging a serverless architecture:

No Server Management

With traditional virtual machines and containers, developers still need to manage the OS, updates, security patches, scaling capabilities, and underlying hardware even though it is abstracted away into a virtual environment.

Serverless computing removes this undifferentiated heavy lifting so developers can focus on writing business logic and delivering value to the end customer. The cloud provider handles all infrastructure management.

This is a huge benefit that frees up engineering resources and enables faster iteration. Companies no longer need dedicate as many resources towards DevOps and system administration activities.

Pay-Per-Use Billing Model

The serverless billing model is very granular and aligned to consumption. Users only pay for the actual amount of resources used to execute their code down to the millisecond.

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There is no charge when code is not running. This is very different than provisioning virtual machines or compute instances that incur hourly/monthly charges regardless of whether workloads are actively running on them.

The serverless pay-per-use model brings cloud computing resource usage and billing down to a very fine grained economical model.

Auto-Scaling

Serverless platforms auto scale event triggered functions dynamically to meet workload demands. This enables serverless architectures to inherently handle spikes in traffic without any additional configuration.

Traditional hosting requires manual intervention to scale out infrastructure. Serverless auto-scaling properties makes applications highly available while also maintaining cost efficiency.

Faster Time to Market

The combination of infrastructure management abstraction and auto-scaling capabilities allows developers to get products to market much faster.

By removing the need to setup, configure, and manage infrastructure, serverless computing liberates developers to deliver more functionality more quickly for end users. This improved time to market can help drive competitive advantage.

Cons of Serverless Computing

While the pros clearly showcase immense value, there are still some downsides and compromises to consider with serverless:

Operational Complexity

Serverless offloads infrastructure capacity planning, scaling, patching and provisioning to the cloud vendor which simplifies a developer’s job in many ways. However, it also introduces operational challenges around tracking countless discrete event triggers, execution environments, and microservices.

The highly decentralized event driven execution model makes it harder to monitor, visualize, and troubleshoot problems. This can create opacity in system behavior that makes it more complex to operate serverless systems at scale.

Specialized monitoring and observability tooling is necessary to gain insights into system health and performance. This additional tooling adds some incremental overhead and cost.

Increased Vendor Dependence

The auto-scaling serverless execution model provides immense flexibility…up to a certain point. Developers experience great freedom focused purely on functions.

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However, relying so heavily on a cloud provider’s proprietary serverless platform also creates a much stronger vendor dependence.

There are still constraints around number of functions, concurrency controls, available memory, timeout limits, custom runtimes, and overall scalability ceilings. If applications grow beyond a cloud vendor’s capacity or need greater customization, it introduces migration friction.

While vendor dependence is common with virtually all cloud services, it is especially acute in serverless given how much control over the infrastructure is ceded to the platform.

Performance Overhead

The event-driven execution model enables auto-scaling and operational efficiency but also incurs performance overhead. Every function trigger initializes a new container which bootstraps language runtimes, dependencies, libraries, etc which can add latency.

For workloads that emphasize consistent low latency response times, serverless may not meet performance SLAs right out of the box. Careful optimization around initialization and container reuse is necessary to improve speed.

This is an area of ongoing improvement across offerings but still represents a current compromise to weigh.

State Management Complexity

Serverless computing excels at processing ephemeral event data statelessly. However, many applications require stateful data persistence across executions to operate properly.

Serverless platforms provide state storage services but synchronizing state across functions and handling consistency introduces application complexity. Stateful serverless use cases require additional logic to orchestrate state lifecycles across functions which increases development burden relative to monoliths.

As with monitoring, purpose built tools help manage state but represent another layer in the operational stack.

Conclusion

Serverless computing represents a major evolution in cloud platforms that delivers important advantages around resource efficiency and developer productivity. By abstracting away infrastructure management, serverless drives faster delivery of business value. And built-in auto scaling delivers inherent availability and cost savings.

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However, it also comes with distinct downsides to evaluate. The decentralized event model creates operational opacity that demands improved monitoring/observability capabilities. Relying so heavily on a proprietary serverless platform also creates a much stronger vendor lock-in dependence. As serverless platforms continue maturing, they have the potential to meaningfully accelerate digital transformation initiatives if the unique pros and cons are appropriately weighed.

The gains in developer velocity and infrastructure automation are very compelling. But the discrete event driven programming paradigm also introduces distinct complexity tradeoffs to consider especially at scale. Hybrid and multi cloud architectures that leverage both serverless functions and traditional containers/VMs may enable organizations to balance these various factors most effectively based on their specific workload needs and technical readiness.

Frequently Asked Questions

Is serverless computing more cost effective?

Generally yes, the granular pay-per-use billing model and auto scaling properties allow serverless computing to be very cost efficient relative to overprovisioned VMs.

Is vendor lock-in worse with serverless computing?

Yes, serverless increases dependence on the cloud provider’s proprietary platform given the degree of infrastructure abstraction.

Is serverless computing faster for developers?

Yes, by removing infrastructure management responsibilities, serverless enables developers to iterate and release new capabilities much faster.

Does serverless computing offer unlimited scale?

No, serverless platforms still have upper limits around concurrency controls, memory available per function, timeouts, etc. Extremely spiky workloads may require hybrid serverless+VM architecture.

Is monitoring and observability harder with serverless?

Yes, the event driven execution model is more opaque and complex to monitor/troubleshoot. Purpose built tools help provide better visibility.

MK Usmaan