The software architecture choice defines how a SaaS business will grow and adapt. Early decisions — picking a monolith, microservices or serverless — affect development speed, costs and scaling. For example, Netflix’s engineers moved their streaming service from an on-premises monolith to Amazon Web Services (AWS)-based microservices to handle explosive traffic growth. Today, SaaS leaders must balance rapid iteration against future complexity. This article explains each model, compares trade-offs, and shares real-world experiences to guide those choices.
Background
In SaaS, many customers use one application over the internet. Startups often begin with a simple monolith: one codebase that includes all features and serves all tenants. Monoliths ship quickly and are easy to test end-to-end. As the user base grows, limitations appear: Netflix and others found their single-app design could not keep up with demand. At that point, teams consider splitting components out.
At the same time, serverless functions (e.g. AWS Lambda) let developers write small code snippets that run on demand, with no servers to manage. Combined with microservices, serverless can simplify operations at small scale, though it may bring hidden costs at volume. Deciding which path to start on requires aligning architecture with current needs and future plans.
Monolithic Architecture
A monolith is one unified application where all features and logic run together. All customers share this codebase (and usually one database). The main advantage is simplicity: a single codebase is easier to develop and deploy at first. End-to-end testing uses the full system, and developers don’t have to manage service-to-service networking.
- Pros: Fast initial development and deployment. Everything is together, so testing and local runs are straightforward. Good for MVPs or small-traffic projects.
- Cons: Scaling is all-or-nothing. You must replicate the entire app to handle more load, even if only one feature needs it. Code complexity tends to grow; a change in one area can break others. Large monoliths become unwieldy, making releases slow and risky.
Example: Shopify’s commerce platform is a giant Rails monolith – its core codebase exceeds 2.8 million lines of code. Shopify’s engineers have built internal modules to control complexity, but much business logic still lives in that one app. Slack similarly started with a single Hack/PHP backend. These companies favored monoliths early on for developer productivity.
Microservices Architecture
A microservices architecture splits the application into many small services, each handling one responsibility (and often its own database). Services communicate through APIs or messaging, so each can be developed, deployed and scaled independently.
- Pros: Independent scaling and deployment. If one service (e.g. video transcoding) needs more capacity, you scale just that component. Fault isolation: a bug in one service won’t crash the whole app. Teams can choose different technologies per service. For instance, Netflix re-architected into over a thousand cloud-based microservices to handle peak traffic, allowing engineers to push code thousands of times per day.
- Cons: Added complexity. You must manage networked communications, service discovery, and data consistency across services. End-to-end testing is harder as a transaction may span many services. Operating many services incurs overhead: each needs its own CI/CD, monitoring, and hosting. Microservices can multiply infrastructure costs and require strong governance.
Example: Atlassian refactored Jira and Confluence into microservices on AWS for its cloud SaaS. This let distributed teams work autonomously and speed up releases. However, Atlassian also invested in tools (service catalogs, CI/CD systems) to manage the complexity that came with hundreds of services.
Serverless Architecture
Serverless (Function as a Service) abstracts away servers entirely. Developers write functions triggered by events (HTTP calls, database updates, file uploads, etc.). The cloud provider auto-provisions resources when the function runs. You pay only for execution time.
- Pros: No server management. Functions auto-scale instantly and go idle when not in use. You pay per execution (e.g. per millisecond of compute). Rapid deployment of small features (like image processing or webhooks) without configuring infrastructure. AWS Lambda and Azure Functions are popular serverless platforms that many startups use to move fast.
- Cons: Vendor lock-in and execution limits. Each cloud has its own serverless model. Limits on runtime and memory apply, and functions are stateless. “Cold start” delays can hurt latency. Complex workflows require stitching many functions together, which can become brittle. For example, Amazon’s Prime Video team found its Lambda-heavy design hit scaling limits and high costs, so it consolidated to a single ECS/EC2 service and cut costs by 90%.
Trade-offs and Choosing the Right Architecture
There’s no one-size-fits-all. Key considerations include:
- Stage of product: Monoliths or serverless often suit early-stage products to validate ideas quickly. Proven, high-growth SaaS often shift to microservices and cloud infrastructure once demand is clear.
- Scalability: Microservices and serverless let you scale parts of the system independently. Monoliths require scaling the entire stack together.
- Team size: Small teams may not want the overhead of managing many services; a single codebase can simplify collaboration. Large or distributed teams can use microservices to divide work and deploy in parallel.
- Costs and ops: Microservices increase infrastructure and maintenance costs. Serverless shifts costs to pay-per-use (which is great for low traffic but can spike for heavy use). Monoliths may use simpler hosting initially but can incur technical debt over time.
- Complexity: Microservices demand strong DevOps practices (APIs, monitoring, automation). A monolith adds complexity in the codebase. Serverless hides infrastructure but can hide complexity in the event flows.
Real-World Case Studies
- Netflix: Began with a monolithic video service. After repeated outages, Netflix moved to AWS and refactored into hundreds of microservices. This let them scale massively and deploy updates continuously.
- Shopify: An e-commerce SaaS serving many merchants. Shopify’s main app is a large Rails monolith (over 2.8M lines of code). Engineers improved development speed by making the monolith more modular internally, without fully breaking it into separate services.
- Slack: A messaging platform that launched on one backend. A 2021 outage motivated Slack to re-architect its core into “cells” – isolated service clusters spanning availability zones. This cell-based model improves resilience against failures and makes regional updates safer.
- Amazon Prime Video: Used AWS Lambda and Step Functions for video-quality analysis. Under load, this design ran into scaling limits and high costs. The team consolidated all functions into a single ECS/EC2 process, reducing costs by about 90% and eliminating latency issues.
- Atlassian (Jira/Confluence): Originally on-premise monoliths, then redesigned as multi-tenant cloud apps. Atlassian migrated to AWS and split its products into microservices. After migration, they achieved daily deployments instead of weekly, though it required extensive tooling and planning.
Architecture choices shape a SaaS product’s destiny. Early on, simplicity (monolith or serverless) can help teams move fast; at scale, microservices offer agility and resilience. But each approach has trade-offs in complexity and cost. Every company evolves: Netflix, Slack, Amazon and others all revised their systems as needs changed. By carefully weighing trade-offs and learning from these examples, SaaS leaders can pick the approach that best fits their current stage and needs