AWS for Software and Technology

SaaS growth: How to design infrastructure for scalability

Fixing scaling errors later costs more. Use our 4-step framework to design a scalable SaaS infrastructure for business growth.

Overview

You are at the final stage of closing a major enterprise deal through AWS Marketplace. The buyer is ready, but their procurement team just handed you a rigorous technical questionnaire. They want to know exactly how you isolate tenant data, manage noisy neighbors, and prove scalable performance.

Suddenly, the infrastructure workarounds your engineering team used during early development are blocking your biggest potential deal and recurring revenue to date. Your value proposition isn’t standing up to the test.

This isn’t just a bad day. It is the culmination of multiple oversights that could have been detected early on, in an AWS Well-Architected Performance Efficiency Pillar review. If your multi-tenant architecture lacks a foundation for sustainable growth, your SaaS customer acquisition efforts will only uncover new architectural chokepoints. A strategically architected backend is what helps a SaaS business scale reliably. Aiming for market penetration with aggressive growth requires solid foundations.

For those in the SaaS industry, architectural decisions are commercial decisions. When you build scalable, enterprise-ready infrastructure with AWS, you make yourself ready to list on AWS Marketplace and use AWS co-sell programs. That technical readiness early in the sales cycle translates directly to business outcomes. In fact, ISVs that successfully align their architecture for these channels see 40% faster sales cycles and a 20% incremental increase in their sales pipeline.

Think of each layer as a foundation you use to build every one that follows. For example, you don’t decide on the type of database before you determine your multi-tenant isolation strategy. You start by deciding the tiering system, whether tenants share resources or have dedicated workloads, which then informs your schema design.

In this guide, you will learn key insights into how to build scalability into your ISV’s SaaS strategy to satisfy enterprise procurement by following the AWS Well-Architected SaaS Lens framework.

Choose your compute scaling strategy

Building successful SaaS growth strategies starts at the compute layer. For B2B SaaS providers, choosing the right scaling path ensures you can handle rapid growth without degrading stability.

There are several ways to scale compute as you onboard new customers alongside existing users:

Vertical scaling: You increase an existing server's computing power by adding more CPU, RAM, and storage. However, scaling up does not inherently solve tenant isolation. If you operate a pooled compute model where multiple tenants share the same infrastructure, a resource-heavy enterprise "noisy neighbor" will quickly consume those upgraded resources—leaving you with the same performance degradation for other tenants sharing that specific node.
Horizontal scaling: Instead of upgrading a single server, you add more servers or instances to distribute workloads. Although this protects against a total outage, it doesn't eliminate localized isolation events. If an enterprise customer overwhelms a shared node, the other tenants might still experience latency and Service Level Agreement (SLA) breaches. Tenant-aware routing strategies can help mitigate risks.
Serverless and event-driven architecture: This strategy offers low isolation risks if designed per tenant in code—as resources automatically scale to real-time demand, and each customer has a separate invocation.

When you’re validating product-market fit with a controlled audience, the vertical scaling SaaS model is often the simplest approach. However, when approaching a tipping point, many SaaS companies default to horizontal scaling. For example, landing a major enterprise customer whose sheer user volume threatens to degrade performance for your existing customers might result in adding more server instances.

The assumption is that adding more servers will solve all potential bottlenecks. But without knowing what you’re actually scaling for, you won’t be able to guarantee performance as you onboard more tenants.

It’s important to define your scaling unit. In most cases, using the tenant ID as the baseline is the most sensible decision. However, if, for example, you anticipate higher requests to the payment module, you might want to scale by service. Or, if you’re catering to both small businesses and enterprises, scaling by clusters or traffic cohorts is better.

Builder checkpoint

To make sure that you’ve chosen the right scaling unit, ask these questions:

What is our fair use policy in a pooled model?
If tenant A’s data is corrupt and causing crashes, will our retry logic impact tenant B?
When a tenant leaves, are we automatically deleting their scaling unit and data footprint?

AWS provides several services for SaaS companies that help you establish scaling unit economics with tenant isolation.

Amazon EC2 Auto Scaling lets you automatically add resources to EC2 instances based on pre-configured policies.
AWS Lambda provides process-level isolation, with separation of tenants in code.
Amazon EKS enables you to deploy isolated clusters for premium-tier customers while maintaining a shared pool for free-tier tenants.
AWS Fargate offers a serverless isolated workload environment without requiring infrastructure provisioning.

Design your data layer to match your scaling unit

Once you achieve product-market fit, your data layer must scale to maintain a seamless user experience. A robust database architecture helps meet high customer satisfaction even as tenant query volumes multiply. As the Cloud Security Alliance’s State of SaaS Security Report 2025 states, 57% of enterprise users report fragmented administration as a primary challenge; poorly defined data architecture often forces teams into manual, siloed management of their customers’ data. To avoid this, anchor your database performance to a clear scaling unit. Otherwise, unresolved bottlenecks could potentially impact your growth right as your business begins to peak.

To maintain this alignment, address several critical data layers that are frequently neglected during the initial sprints.

Partition strategy
Storing a large table across smaller partitions improves the database’s performance. Your partition strategy should align with your scaling unit. It is often easier to use the tenant ID as the scaling unit and also use it as the partition key. Otherwise, you create throughput ceilings when the database engine must expend more effort to perform CRUD operations. Amazon DynamoDB lets you partition tables by tenant ID, enabling horizontal scaling while helping with your customers’ logical data separation. It reduces cloud spend by preventing full table scans, which can inflate AWS bills, instead performing per-partition queries.

Read/write separation
For many SaaS applications, using the same database for read and write operations results in bottlenecks because they usually perform significantly more reads than writes. When confined to the same database, you more quickly hit read throughput limits. Therefore, ISV builders can add read replicas to reduce data retrieval latency. Each read replica resides on a different server to help prevent congestion. To further expedite retrieval, you can add tenant-aware routing so that requests from specific customers are directed to dedicated clusters. For example, you can use Amazon Aurora to deploy read replicas and configure custom endpoints to isolate enterprise read traffic.

Connection management
Connection pools help overcome a database’s connection limit. Multiple tenants can query a database without repeated connections and disconnections. Connection pooling can simplify scaling; however, a tenant with heavy usage can quickly exhaust available resources. To help ensure fair access, isolate connections based on the tenant tier’s query requirements. For example, you can use Amazon RDS Proxy to set up connection pools that prioritize premium tenants, such as those of a major new enterprise customer, while adding safeguards to ensure other tenants still have access to resources.

Caching
Caches help avoid read traffic from overwhelming the database. However, a misaligned scaling unit can result in cache namespace collision if multiple tenants share the same cache, which has the potential to expose data to adjacent customers. Therefore, you must isolate the cache using tenant-aware namespaces to help meet data privacy and compliance obligations. You can use Amazon ElastiCache to enforce tenant-aware key prefixing for preventing cross-tenant data leakage.

Builder checkpoint
Database decisions made in testing can cascade and magnify after you deploy workloads to production. If you find that you’ve overlooked tenant namespacing, fixing the entire database is costly. As you design the data layer, ask these questions:

What happens to our read replicas if a tenant runs an unoptimized query?
Does our caching client library enforce namespacing via tenant ID?
Does our database schema support tiered storage that separates cold and hot data?

Build traffic management around your data layer's limits

Traffic management is a critical component of SaaS growth. Yet, many ISVs overlook the decisions required to sustain that growth potential.

When scaling, API access and data volumes drive up infrastructure costs, which can negatively impact your overall gross margins. Customer acquisition cost and customer lifetime value can be negatively impacted by other planning decisions. Protecting your margins requires intelligent rate-limiting measures to keep compute expenses predictable. Yet, these cost-control tactics frequently ignore the throughput limits of the underlying database.

If you use AWS WAF, you can set rules that allow the web application firewall to drop malicious traffic at the edge before it can consume your backend compute or database resources. So instead of asking, “How many requests should we distribute to a CDN?”, ask, “Can the database handle this without degrading the experience for other tenants?”

Here are several ways to align your routing strategy with the data layer’s capacity.

Set rate limits based on the tenant’s data access frequency
Use access frequency as your baseline instead of setting blanket coverage. For example, one of your enterprise tenants with thousands of daily transactions should have a higher request limit than smaller existing customers with significantly less traffic. In this example, you can use Amazon API Gateway to map tenants to their respective usage tiers for targeted traffic control.

Enable asynchronous processing
Use asynchronous processing to prioritize requests that require real-time responses. A common solution here is to create an asynchronous queue in Amazon SQS that handles traffic spikes. For example, you could delegate report generation to a separate background worker while routing authentication requests to the processing path.

Apply a backpressure mechanism

Use a backpressure mechanism to inform client services that the backend server is experiencing high traffic. For example, you use Amazon VPC Lattice to set service-to-service policies, including implementing a circuit breaker pattern. This allows the server ample time to clear the request pipeline before accepting new messages again.

Use a CDN to prevent traffic from overloading the origin server

For example, you can deploy workloads on tenant-aware edge locations with Amazon CloudFront and CloudFront Functions to reduce latency. Additionally, CloudFront Functions can inspect the HTTP request header to ensure the Tenant ID matches the origin server before allowing it through. But remember, the traffic limit is only accurate if anchored on the data layer, not the other way around.

Builder checkpoint
If you need help in shaping your traffic policy, ask these questions of your team:

Do we know the cost per request for our largest tenant?
Do we have a hard cap on the number of database connections per tenant in the proxy?
If we need to isolate a single tenant, can we block them at the CDN edge?

Use telemetry to close the observability loop

Building a scalable SaaS architecture requires telemetry to govern it. Early-stage SaaS startups often keep observability to a minimum during prototyping, but scaling workloads demands a range of metrics. Unfortunately, some SaaS companies continue to prioritize measuring overall uptime and aggregate latency as they accelerate their growth rate. This can create operational blind spots that inflate support costs and delay event resolution.

Granular, tenant-aware tracing is what allows ISVs to prove strict SLA compliance during enterprise contract renewals. The argument here is that if you can’t measure API requests, latency, compute capacity, and other metrics in real time, you won’t be able to respond to network events. For example, if you experience a sudden spike in traffic, aggregated request metrics won’t identify specific regions, servers, and tenants from which rogue packets originate. Similarly, an average latency can be a misleading indicator because it can’t reflect a performance bottleneck caused by a single server. Instead, you need real-time data correlation for timely responses and crucial insights.

Making data-driven decisions about your infrastructure ensures long-term success. To help with continuous growth for your SaaS, you must build in granular telemetry from the start.

Log important events
Log events such as user access, code errors, and security violations to support post-incident response. When deploying SaaS apps, you can use AWS CloudTrail to capture and store events from AWS and ingestion from external environments in immutable records.

Use metrics for immediate observability of SaaS health
This can show indicators such as response latency, database throughput, and storage usage. You can use Amazon CloudWatch Contributor Insights to analyze time-series logs to identify tenants that are impacting performance.

Use traces to capture API requests
Use traces that span multiple services to provide complete visibility across SaaS components. Application Observability (APM) provides application telemetry that helps diagnose possible issues before they affect customer experience.

Builder checkpoint
When properly instrumented, telemetry signals guide decisions you made in the previous compute, data, and traffic layers. They also allow you to prioritize fixes through evidence-based root cause analysis. You will get clearer observability indicators by asking these questions:

If we need to perform a post-mortem for a specific customer, can we isolate every database query they ran during the event window?
If we deploy a code change, can we see the performance impact per tenant during a canary release?
Does your P99 latency metric show per-tenant or for your whole product?

Design infrastructure to scale from the ground up

Successful SaaS transformation is the sum of decisions made across the scaling units, data layer, traffic management, and observability. Defining your scaling unit in the early stage allows you to architect a data layer that survives the transition from prototype to production for continuous innovation. Understanding your database’s limits allows you to build a traffic distribution mechanism that remains resilient under pressure. Binding these layers together is a tenant-aware telemetry system that delivers the insights needed to manage a complex environment.

Overlooking any of these layers can create cascading business risks and detract from customer success. Running your architecture through an AWS Well-Architected Performance Efficiency review and the AWS Well-Architected SaaS Lens helps ensure your offering is built for resilience and cost-efficiency. Getting this foundation right is what prepares you for admission to AWS ISV Accelerate and the broader Amazon Partner Network. These programs give a competitive edge to help successful companies thrive. By becoming an AWS Partner, you can use cross-selling opportunities on AWS Marketplace and benefit from our sales playbook. To qualify, your SaaS must pass AWS Foundational Technical Review to prove that it’s built and positioned to scale in a global marketplace. Start with an AWS Well-Architected review to assess your infrastructure's growth capacity.