Serverless Architecture

Serverless computing has moved well past the hype cycle. AWS Lambda launched in 2014, and a decade later, serverless architectures power everything from simple webhooks to complex event-driven systems processing millions of transactions. Yet despite this maturity, many organizations still struggle with the fundamental question: should we go serverless?

The answer, as with most architectural decisions, is “it depends.” But that’s not particularly helpful when you’re trying to make a strategic technology decision. Let’s dig into the specifics.

What Serverless Actually Means

Before evaluating fit, we need to be precise about terminology. “Serverless” has become an overloaded term that means different things to different people.

At its core, serverless computing refers to execution environments where:

Function-as-a-Service (FaaS) platforms like AWS Lambda, Azure Functions, and Google Cloud Functions are the most common serverless compute options. But serverless extends beyond compute to include databases (DynamoDB, Aurora Serverless), message queues (SQS, EventBridge), API gateways, and storage services.

When Serverless Makes Sense

Event-Driven and Asynchronous Workloads

Serverless architectures excel at processing discrete events. If your workload involves responding to triggers like file uploads, database changes, HTTP requests, message queue events, or scheduled tasks, serverless is often an excellent fit.

Consider a document processing pipeline: a user uploads a file to S3, which triggers a Lambda function to extract metadata, another function to generate thumbnails, and a third to update a search index. Each function does one thing, scales independently, and costs nothing when idle.

This pattern works because:

• Events arrive unpredictably, making pre-provisioned capacity wasteful
• Each processing step is independent and can scale separately
• The work is naturally parallelizable
• Individual executions are short-lived

Variable or Unpredictable Traffic and Server Load

If your traffic patterns are spiky or unpredictable, serverless can be significantly more cost-effective than maintaining always-on infrastructure sized for peak load.

A real estate company we worked with had traffic that varied significantly between their slowest and busiest periods. Their previous architecture required substantial over-provisioning to handle large import and processing flows, infrastructure that sat idle most of the day. Moving to a serverless architecture reduced their compute costs dramatically while cutting time-critical processing times by 90%.

Rapid Prototyping and MVPs

When speed to market matters more than architectural purity, serverless removes significant friction. You can go from idea to deployed API in hours rather than days. There’s no infrastructure to provision, no CI/CD pipelines to build for deployment, no scaling policies to tune.

This makes serverless ideal for:

• Testing new product ideas quickly
• Building internal tools that don’t justify infrastructure investment
• Proof-of-concept implementations
• Hackathons and innovation sprints

Just be aware that what works for an MVP may not be the right architecture at scale. Plan for potential migration if the project succeeds.

Stateless API Backends

REST APIs that perform CRUD operations against a database, call external services, or execute business logic are often good serverless candidates. The request-response pattern maps naturally to function invocations, and API Gateway handles routing, authentication, and rate limiting.

This works particularly well when:

• Individual requests complete quickly (under a few seconds)
• There’s no need to maintain state between requests
• Traffic is variable or has natural idle periods
• The API serves as a backend-for-frontend or mobile backend

Scheduled and Batch Processing

Cron jobs, nightly data processing, report generation, and similar scheduled tasks are excellent serverless use cases. Instead of maintaining an always-on server to run a job that executes for 10 minutes each night, you pay only for those 10 minutes of execution.

AWS EventBridge (formerly CloudWatch Events), Azure Timer Triggers, and Google Cloud Scheduler can invoke functions on schedules ranging from once per minute to once per year.

When Serverless Doesn’t Make Sense

Long-Running Processes

Most FaaS platforms impose execution time limits. AWS Lambda caps at 15 minutes, Azure Functions at 10 minutes (on the Consumption plan), and Google Cloud Functions at 9 minutes. If your workload regularly exceeds these limits, you’ll need to either redesign for chunked processing or choose a different compute model.

Workloads that typically hit these limits include:

• Video transcoding and media processing
• Large-scale data transformations
• Machine learning training jobs
• Complex report generation
• Legacy batch processes that weren’t designed for chunking

You can sometimes work around these limits by breaking work into smaller pieces coordinated by Step Functions or Durable Functions, but this adds complexity that may not be justified.

Consistent High-Volume Traffic

When your application maintains steady, predictable traffic at significant volume, the serverless pricing model starts to work against you. The per-invocation pricing that makes serverless cheap for variable workloads becomes expensive when you’re processing millions of requests per hour, every hour.

At a certain scale, reserved or spot instances (even with the operational overhead of managing them) become dramatically cheaper than serverless. The crossover point depends on your specific workload, but it’s worth doing the math. We’ve seen cases where organizations reduced their compute costs by 70% by moving high-volume, steady-state workloads off Lambda onto containers.

Latency-Sensitive Applications

Cold starts remain a real issue in serverless architectures. When a function hasn’t been invoked recently, the platform needs to provision a new execution environment, load your code, and initialize your runtime. This can add hundreds of milliseconds to several seconds of latency.

Provisioned concurrency and similar features can mitigate cold starts, but they also reduce the cost benefits of serverless. If your application requires consistent sub-100ms response times, you’ll need to either accept the cost of provisioned concurrency or consider a different architecture.

Latency can influence your choice of programming language. Java is well known for a bad cold start penalty, especially when implementing Spring. Python and NodeJS are quick, but lack strong typing and compilation. Golang hits the sweet spot as it is fast and compiled. But you may not want to have language decisions made by your infrastructure choices.

Applications where cold start latency is particularly problematic:

• Real-time gaming backends
• Financial trading systems
• Interactive user-facing APIs where responsiveness is critical
• Synchronous microservice communication with strict SLAs

Complex Local State Requirements

Serverless functions are fundamentally stateless. Each invocation may run on a different execution environment, and you can’t rely on data persisting between invocations. While you can use external state stores (DynamoDB, Redis, S3), this adds latency and complexity.

Workloads that depend heavily on local state (in-memory caches, connection pools, loaded ML models) often perform poorly in serverless environments. The overhead of reestablishing state on each invocation can negate the benefits of the architecture.

Vendor Lock-In Concerns

Serverless architectures tend to be more tightly coupled to specific cloud providers than container-based or VM-based architectures. Your Lambda functions integrate with API Gateway, EventBridge, DynamoDB, and dozens of other AWS services through proprietary APIs and configurations.

If multi-cloud portability or exit strategy flexibility is a priority, serverless requires careful consideration. While frameworks like the Serverless Framework and SAM abstract some provider differences, true portability remains challenging. The deeper you go into a provider’s serverless ecosystem, the more expensive migration becomes.

The Hybrid Approach

Most mature organizations don’t make an all-or-nothing serverless decision. Instead, they use serverless selectively for workloads where it provides clear advantages while maintaining container or VM-based infrastructure for workloads where those models make more sense.

A common pattern:

This hybrid approach lets you capture serverless benefits where they matter while avoiding the pitfalls where they don’t. Just keep in mind that you’ll have multiple tech stacks to maintain.

Making the Decision: A Practical Framework

When evaluating serverless for a specific workload, consider these factors:

Traffic Pattern

• Highly variable or spiky → Serverless advantage
• Steady and predictable → Containers/VMs often cheaper

Execution Duration

• Sub-15-minute executions → Serverless viable
• Longer processes → Need different approach

Latency Requirements

• Tolerant of occasional cold starts → Serverless works
• Strict latency SLAs → Consider provisioned concurrency cost or alternatives

State Requirements

• Stateless or external state acceptable → Serverless works
• Heavy local state dependency → Likely better on containers

Team Experience

• Team comfortable with event-driven patterns and a solid serverless-friendly language → Serverless adoption smoother
• Team experienced with containers/Kubernetes or prefer Java/Spring → May be faster to deliver with familiar tools

Cost at Scale

• Low to moderate volume → Serverless often cheaper
• High sustained volume → Do the math carefully

The Operational Reality

One factor often underweighted in serverless decisions is operational overhead. While serverless eliminates server management, it introduces different operational concerns:

None of these are dealbreakers, but they require adjustment. Teams accustomed to traditional deployment models need time to adapt their practices.

Conclusion

Serverless architecture is a powerful tool, but it’s a tool, not a universal solution. The organizations that succeed with serverless are those that apply it thoughtfully to workloads where it provides genuine advantages, rather than adopting it as a default or avoiding it entirely.

Start with workloads that play to serverless strengths: event-driven processing, variable traffic, scheduled jobs, and rapid prototypes. Build organizational experience and observability capabilities with these lower-risk applications before considering serverless for more critical systems.

And remember that architectural decisions aren’t permanent. As your traffic patterns, team capabilities, and business requirements evolve, the right answer may change. Build with enough abstraction that you can migrate workloads between serverless and container-based architectures as economics and requirements shift.

The goal isn’t to be a “serverless company” or to avoid serverless entirely. The goal is to choose the right tool for each job and to make those choices deliberately rather than by default.