Big backend applications scale by identifying what is limiting the workload and adding capacity or changing the design at that layer. Applications often scale out across interchangeable servers, while databases need workload-specific measures such as query optimization, caching, read replicas, or partitioning. Queues absorb work that need not finish during a user request. Microservices and multiple regions can help when there is a concrete reason to adopt them, but they also add complexity.
The governing principle is simple: more capacity helps only if it addresses the bottleneck. Adding application servers cannot fix a saturated database, and distributing a system across more services does not make its work disappear.
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Start by finding the bottleneck
A backend request may pass through a load balancer, application code, a cache, a database, and other services. Each layer has different limits. Before scaling, measure the complete request path and identify where latency, errors, or saturation appear. Microsoft’s scale-out guidance warns that scaling out is not a fix for every performance issue.
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- If database queries or connections are the constraint, adding web servers can increase pressure on the database without improving throughput.
- If a workload arrives in bursts but can finish later, buffering it may be more effective than provisioning every component for the peak.
- If a problem affects a broad set of customers, the needed change might be isolation or fault containment rather than raw capacity.
Different workloads stress different resources. Read-heavy, write-heavy, bursty, and geographically dispersed traffic do not call for the same scaling plan. Latency and consistency needs, fault boundaries, operational effort, and cost also matter. There is no universal server count, shard count, or autoscaling threshold that fits every application.
#1 Best Overall
Scale application capacity with interchangeable instances
Vertical scaling adds capacity to a resource
Scaling up gives an existing machine or service more capacity. It can be a straightforward way to handle growth, but it does not remove every underlying limit or make a single resource redundant. The useful scale unit might be an application instance, a database, or another infrastructure component.
Horizontal scaling adds instances
Scaling out runs additional instances of a service and distributes requests among them. For this to work cleanly, any healthy application instance should be able to handle a request. Avoid relying on a particular server’s in-memory session or local files: shared state that must survive between requests belongs in an appropriate shared service. Microsoft’s reliability guidance on scaling describes horizontal scaling as a system-design requirement, not just a matter of launching more machines.
Autoscaling can add or remove capacity when configured conditions are met; scaling can also be scheduled or manual. Set useful limits on automatic capacity so a surge does not trigger unbounded resource allocation and cost. Scaling application instances this way does not automatically scale a database or another shared dependency.
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Rank #2
Reduce database work before adding more database capacity
Databases often become a shared constraint because many parts of an application depend on them. Start with the work the application asks the database to do: inspect access patterns and queries, reduce unnecessary reads and writes, and isolate workloads when doing so reduces contention or permits independent capacity choices. Caching can reduce repeated reads; read replicas can serve suitable read traffic. When a data set or write path outgrows a single database, partitioning or sharding may be an option, but it brings routing and operational complexity.
Choose database techniques for the workload
| Technique | Useful when | Main consideration |
|---|---|---|
| Query and access-pattern optimization | Unnecessary or inefficient database work is contributing to load. | It improves the work performed per request; it does not by itself add capacity for every workload. See OpenAI’s PostgreSQL account for an example of query and application-side work alongside infrastructure changes. |
| Caching | Data is read repeatedly and can be served within the cache’s freshness rules. | Results may be stale or incomplete, and a cache failure can push load back to storage. See Google Cloud’s scalable and resilient app patterns. |
| Read replicas | Suitable read traffic can be served from replicated copies rather than the primary. | Replication and application routing affect how reads behave; replicas do not remove the primary’s write workload. See OpenAI’s account for one reported read-heavy deployment. |
| Partitioning or sharding | A data set or write path needs to be divided across units. | Partitioning adds data placement and routing decisions and can make operations and transactions more complex. Shopify discusses these trade-offs in its account of scaling the Shop backend. |
| Different database model | The application’s data and consistency needs fit the alternative model. | A NoSQL database is not a universal upgrade: availability and scalability trade-offs depend on whether the application can tolerate eventual consistency and does not need all relational-database features. See Google Cloud’s guidance. |
Plan for cache misses as well as cache hits
A cache reduces downstream work when it has the needed value, but a sudden drop in hit rate can send many reads to the database at once. One mitigation is to coordinate requests for the same missing key: let one request fetch it while others wait for the cache to be repopulated. OpenAI describes cache locking or leasing for this purpose in its engineering account. The right cache behavior depends on how stale a result can be and what the application should do if the cache or storage is unavailable.
Use queues when work does not need to finish in the request
If a task can complete after the user-facing response, a queue can absorb a burst and let workers process it at a sustainable rate. Instead of forcing the application to handle every job immediately, the system records work for consumers to drain as capacity allows. Microsoft covers this pattern in its scale-out guidance and scaling guidance.
Queue consumers should be interchangeable: any healthy worker should be able to process an eligible message. Capacity can be adjusted in response to queued work, subject to the limits and behavior of the downstream services those workers use. The trade-off is that completion is no longer immediate; the product must be able to accommodate that delay.
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A large application does not have to become microservices to scale. A modular monolith can be deployed across multiple instances and may remain simpler to operate while its components share deployment and data needs. Splitting out a service is more attractive when it needs independent scaling, deployment, or fault isolation.
That flexibility comes with distributed-systems costs. Services communicate over a network; data may become eventually consistent; teams may operate different data stores; and a transaction spanning multiple stores is harder than a transaction inside one database. AWS describes these trade-offs in its cloud design patterns.
Shopify’s account of its Shop backend illustrates both the value of isolation and the cost of further splitting. Its “Pod Architecture” separates workloads so a problem affecting one merchant need not affect others; the account also explains that another database split would have increased application complexity and introduced cross-database transaction concerns. See Shopify Engineering’s write-up.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Use multiple regions for a geographic or availability need
Running in multiple regions can bring services closer to users or support availability goals. A global design may route traffic based on proximity, capacity, or availability and replicate data across regions. Google’s global deployment reference architecture describes global and cross-regional load balancing with a synchronously replicated database.
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What one large PostgreSQL deployment demonstrates
OpenAI’s January 2026 engineering account, “Scaling PostgreSQL to power 800 million ChatGPT users,” reports that its read-heavy workload used one Azure PostgreSQL Flexible Server primary and nearly 50 read replicas across regions. OpenAI also reports that PostgreSQL load had grown by more than 10× over the preceding year. These are figures and architectural details reported by OpenAI for its own system, not independent benchmarks or a general capacity guarantee.
The account describes more than replica count: it also discusses query and cache work, connection pooling, rate limits, workload isolation, and schema management. The example shows that a relational primary can remain part of a large system when the workload and surrounding architecture support it; it does not establish that the same layout will suit another application.
Quick Recap
A practical order for scaling decisions
- Define the workload and objective. Establish what is growing and which outcomes matter: response time, availability, throughput, or the ability to handle bursts.
- Measure the request path. Find the constrained component rather than assuming application servers are the limit.
- Remove avoidable work. Review queries, access patterns, repeated reads, and contention before choosing a larger architectural change.
- Choose a scale unit for the constrained layer. Add interchangeable application instances for application capacity; use caching, replicas, isolation, or data partitioning only when they match the data workload.
- Move deferrable work off the request path. Use queues where a delayed result is acceptable and workers can process jobs independently.
- Adopt service or regional boundaries for a specific benefit. Compare independent scaling or fault isolation with the added network, consistency, failover, and operating costs.
- Set and revisit capacity limits. Monitor behavior as demand changes and cap automatic resource allocation to keep scaling useful and controlled.
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