> ## Documentation Index
> Fetch the complete documentation index at: https://developer.upsun.com/llms.txt
> Use this file to discover all available pages before exploring further.

# So, what to scale next?

> Your web application is slow. Use these decision charts to connect CPU, memory, and disk pressure to your next scaling move, from replicas to fewer workers.


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<PostMeta data={{ author: ["ralt"], date: "2026-10-06T09:00:00.000Z" }} />

Your website is slow. You open the metrics, look at the graphs, and try to figure out what needs more resources. There are several graphs. They all seem very pleased to have information for you.

But which one tells you what to do?

When you're under pressure, you probably don't want to work through database internals while someone asks whether the checkout is working again.

Start with the metrics, then ask when the slowdown happens. The decision charts below help you choose what to scale once you know which service is struggling.

## Start with the metrics

There are 3 resources to look at first: CPU, memory, and disk I/O. Network delays and external services can also slow requests, even when these resources have capacity left.

Look at the metrics from when your site was slow. On Upsun Cloud, you can check each service's [resource metrics](/docs/observability/metrics/understand-metrics). If the application's CPU was at its limit while requests were taking longer, start there. Requests may be competing for CPU time: that's resource contention.

If you run several instances, check each one. One instance can be overloaded while the others have little to do, making the average look fine.

## Find where the request spends its time

For PHP and Python applications, [Upsun Blackfire](/docs/observability/application-metrics/blackfire) helps you see where a slow request spends its time. Profile the request to find which function calls or database queries need attention before deciding what to scale.

A slow page might spend most of its time running application code, querying the database, or waiting for an external service. Compare a slow request with a faster one for the same page to see which part takes longer.

## When does it slow down?

### During a traffic spike, or throughout the day?

Does the site slow down when more visitors arrive, then recover as traffic drops? Check which resource reaches its limit during that period. Adding capacity there may help absorb the extra requests.

If a short spike leaves requests waiting long after traffic drops, check whether the application is still catching up. A queue that keeps growing needs attention, too.

If the site stays slow throughout the day, compare busy and quiet periods. Does the same operation remain slow with only a few users? Use its profile to investigate before assuming you need more capacity for traffic.

### At the same time every day, even with little traffic?

Suppose the site slows down at 3 a.m., when hardly anyone is using it. Check what else starts at that time: an import, a report, or another scheduled job. If that job runs in the web container, it competes with your visitors for CPU and memory.

Move the heavy work to a worker or another container with its own resource allocation. You can give the job more CPU or memory without taking capacity away from web requests.

On Upsun Cloud, [cron jobs](/docs/configure-apps/image-properties/crons) run on the web instance. Have the cron enqueue the job, then let a [worker](/docs/configure-apps/image-properties/workers) process it with separately allocated resources. The cron stays short, and the expensive part runs elsewhere.

Check shared dependencies afterward. An isolated worker can still overload the database or shared storage it uses alongside the application.

## What if resource usage stays low?

Your application can be slow while CPU, memory, and disk usage all look comfortable. A request waiting for another service doesn't necessarily consume much of any of them.

Suppose your checkout calls a shipping provider, and that provider is down. Each request waits until the call times out. Your application has plenty of CPU available, but that doesn't make the shipping provider answer any sooner.

Check request traces and logs to see where the time goes. Look for slow outgoing calls, connection failures, and timeouts. Low resource usage is a clue, not proof: requests can also be waiting for locks or an available worker.

If a third-party service is unavailable, adding application instances won't restore it. Investigate the dependency, bound how long calls can wait, and decide what the application can do without a response. A shipping estimate might be temporarily unavailable; a payment confirmation needs different handling.

## If the application is struggling

For a stateless application, horizontal scaling is often the first thing to try. You add instances and distribute requests between them. Vertical scaling means giving each instance more resources.

The stateless part matters: another instance must be able to handle the next request without needing data stored only on the first.

```mermaid theme={null}
  flowchart TD
    APP[Stateless application is slow] --> B{What is limiting it?}
    B -->|Cron job competing with requests| CRON[Move heavy work to a container with its own resources]
    B -->|CPU under concurrent load| CPU[Add instances and distribute requests]
    B -->|Memory| M{What needs the memory?}
    M -->|Many simultaneous requests| H[Add instances or reduce per-instance concurrency]
    M -->|One request or the process itself| V[Review memory use and per-instance limits]
    B -->|Storage I/O| D{Where is the storage?}
    D -->|Independent local disks| LOCAL[Spread work across instances or improve local I/O]
    D -->|Shared storage| SHARED[Inspect the storage server and try less concurrency]
    B -->|Low usage but slow requests| DEP[Trace waits to dependencies, locks, or worker queues]
    DEP --> EXT{External service unavailable?}
    EXT -->|Yes| TIMEOUT[Bound waits and handle the unavailable service]
    EXT -->|No| FOLLOW[Investigate the service or queue causing the wait]
```

### CPU and memory: more instances often help

When many requests compete for CPU, more instances let you spread that work. Provided traffic is distributed and dependencies have spare capacity, this is often a straightforward scaling move.

Memory can work the same way. If each request needs memory, fewer simultaneous requests per instance can keep each process within its limit.

But those instances don't share one giant pool of RAM. If a single request needs more memory than an instance has, adding 10 identical instances won't make it fit. Review the operation, increase per-instance memory, or both. A memory leak also needs fixing; scaling may only postpone the next failure.

Java adds another reason to look beyond container metrics. The Java Virtual Machine (JVM) has its own heap limit. An application can fail to allocate objects without exhausting the container's entire memory allowance. Check heap usage, garbage collection, and the specific [`OutOfMemoryError`](https://docs.oracle.com/en/java/javase/24/troubleshoot/troubleshooting-memory-leaks.html), rather than assuming a flat graph means everything is fine.

### Local disk or shared disk?

If each instance uses independent local storage for temporary work, adding instances can spread that I/O. Check that they actually have independent storage capacity. Separate containers on the same saturated disk won't get you very far.

Shared storage behaves differently. If every application instance talks to the same Network File System (NFS) server, adding clients doesn't add storage capacity.

Inspect the server's CPU, memory, disk latency, and network, along with client-side waits. Extra memory may improve read caching; sustained writes still need storage throughput. File locks and metadata operations need their own investigation.

Sometimes the useful change is reducing application concurrency: fewer simultaneous requests or background jobs accessing the shared storage.

That can feel backward. The site is slow, and you're asking it to do less at once. But if workers spend their time competing for locks or overwhelming storage, more workers can mean longer waits.

We've seen applications become much more stable after reducing concurrency. Try a controlled reduction and compare completed requests, latency, and errors. The same principle appears in [overload control](https://aws.amazon.com/builders-library/using-load-shedding-to-avoid-overload/): admitting more work than a service can finish can reduce useful throughput.

Keep queues bounded and handle excess demand deliberately. Moving an unlimited queue somewhere else doesn't remove the overload.

## If the database is struggling

For a relational database such as PostgreSQL or MariaDB, separate reads from writes before choosing what to scale.

```mermaid theme={null}
  flowchart TD
    DB[Database requests are slow] --> B{What is limiting them?}
    B -->|Disk reads| R{Frequently used data misses the cache?}
    R -->|Yes| RAM[Add RAM]
    R -->|No| PLAN[Check scans, query plans, and storage latency]
    B -->|CPU serving reads| CPU[Add CPU, or route suitable reads to replicas]
    B -->|Sustained disk writes| W[Reduce write work or add storage throughput]
    W --> LIMIT{Still beyond one writer's capacity?}
    LIMIT -->|Yes| SHARD[Plan sharding across independent databases]
    LIMIT -->|No| CHECK[Measure latency and completed work again]
    B -->|Locks or connection waits| LOCK[Inspect blocking work and limit concurrency]
```

### Too many disk reads? You may need memory

Your database might contain years of orders. Most requests probably touch a much smaller set: current products, recent orders, and active customers.

That's the active dataset, also called the working set. It includes the indexes needed to find those records. Its size matters more for caching than the total size of your database.

When frequently accessed data fits in memory, the database can reuse it without fetching it from storage every time. If it doesn't fit, useful pages keep getting replaced. The next request needs them again, and back to storage you go.

The memory graph can look perfectly stable throughout this. The cache stays full while its contents churn. Stable memory usage doesn't mean enough memory.

Check whether slow reads coincide with physical disk reads and poor cache reuse. MariaDB exposes counters for requests served through its [InnoDB buffer pool](https://mariadb.com/docs/server/server-usage/storage-engines/innodb/innodb-buffer-pool). PostgreSQL's [I/O statistics](https://www.postgresql.org/docs/current/monitoring-stats.html) need care: a miss in its buffer cache may still be served by the operating system's cache.

If the working set doesn't fit, add RAM. On Upsun Cloud, the platform handles the database's cache configuration for you. If you manage the database yourself, check that its cache settings make use of the extra memory. Leave room for connections, queries, and the rest of the process, too.

Yes, you can solve a disk read problem by adding memory.

But check what those reads are doing. A query scanning a huge table because it lacks a useful index needs attention. Buying enough RAM to make an unnecessary scan less painful can become an expensive habit.

### Too much CPU serving reads? Spread the queries

If the data is already cached but executing read queries consumes the available CPU, more memory may do very little.

Adding CPU to the database can help it handle more concurrent work. It's a reasonable immediate option when resizing is available. A single expensive query may still need a better query plan.

For growing read traffic, read replicas give you another option. They maintain copies of the primary database, and your application routes suitable read-only queries to them. Those queries use the replicas' CPU instead of competing with writes on the primary. PostgreSQL supports this through [hot standby](https://www.postgresql.org/docs/current/hot-standby.html).

On Upsun Cloud, you can add a [`mariadb-replica`](/docs/add-services/mysql/mysql-readonly-replication) or [`postgresql-replica`](/docs/add-services/postgresql/postgresql-readonly-replication) service alongside your primary database.

Creating a replica isn't enough: something must actually send reads there. Replication also takes resources, and replicas still have to apply changes. They don't make the primary's work disappear.

Replication lag matters, too. A customer checking an order immediately after placing it probably expects that order to exist. Keep reads that require the latest committed data on the primary unless your setup provides the necessary consistency guarantees.

Replicas are useful for distributing many reads. They don't automatically make an individual query faster.

### Too many writes? Eventually, split the work

Writes have to become durable. Memory can absorb bursts, but sustained writes eventually need enough storage capacity to keep up.

First, check whether you can reduce unnecessary writes, batch suitable operations, or increase storage performance. Depending on the workload, that means more I/O operations per second (IOPS), more throughput, or lower commit latency.

Read replicas won't remove those writes from the primary. Each replica receives the changes, too.

If the workload outgrows what 1 writer can sustain, sharding becomes an option. You divide the data across databases with independent resources, so they can handle different writes concurrently. You can take this further by giving each group of customers its own application and database, as described in [natural scaling](/posts/insights/the-third-way-to-scale-that-nobody-talks-about).

A shop might separate its data by country. Orders for France go to one database, orders for Germany to another. This works best when most operations stay within their country's data. Shared inventory and reporting across countries need additional design, and traffic won't necessarily divide evenly.

You probably don't want to design that migration during an incident. Extra storage performance can buy you time to plan it.

## Check whether the change helped

After changing the resources, check whether requests finish faster and fewer of them fail. Compare periods with similar traffic so a quieter hour doesn't make the change look better than it is.

On Upsun Cloud, you can test changes in a [preview environment](/docs/environments/actions) before [adjusting production resources](/docs/manage-resources/adjust-resources). Reproduce the conditions that caused the slowdown: the same traffic spike, expensive request, or scheduled job.

The next limit may appear somewhere else. More application instances can expose a database bottleneck; more database memory can leave CPU as the constraint. Check the other services as well as the one you changed.

Keep the charts handy. Under pressure, having fewer things to remember helps.


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