Coolify controls an application’s container resources from its Resource Limits settings. Enter the CPU and memory values, save them, then redeploy or restart the application so Coolify recreates the containers with the new Docker limits. Confirm the result in Metrics and Runtime Logs while the app handles representative traffic.
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What resource limits do in Coolify
Each Coolify application runs in one or more Docker-managed containers. The values in Resource Limits are translated into Docker runtime settings when Coolify creates those containers. A limit therefore applies when the container is created or recreated; merely typing a value into the form does not alter an already-running container.
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CPU and memory controls solve different problems. CPU settings govern how much processing time a container may use and how it competes with other containers. Memory settings determine the amount of RAM Docker accounts for, the hard ceiling at which the process can be terminated, and whether swap can contribute to the total. A setting that looks reasonable in isolation can still break an application if it is below the application’s actual working set or leaves too little CPU for health checks.
Measure the application before choosing values
Start with observed behavior rather than a default number. Run the application under the traffic and jobs it normally handles, then record the values that matter:
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- CPU utilization during ordinary requests and during the busiest recurring operation.
- Peak memory usage, not just the idle value.
- Whether swap is used and how memory behaves as traffic continues.
- Health-check success and response latency while the workload is active.
- Container restarts, out-of-memory exits, or other failures in Runtime Logs.
- Unused CPU and memory on the host after the application and its neighboring services are running.
The goal is to leave headroom above the measured working set. A hard memory limit below that working set allows Docker to terminate the process for running out of memory. A CPU limit that is too low can delay the application enough for health checks to time out, even when the code itself is functioning correctly.
Set limits in the Coolify interface
- Open the relevant application in Coolify.
- Select Resource Limits.
- Enter the CPU and memory settings described in the sections below. Leave a field at
0when you want no configured limit for that resource. - Select Save changes.
- Redeploy or restart the application. This causes Coolify to recreate the container with the saved Docker settings.
- After the new container is running, open Metrics, review Runtime Logs, and send representative traffic while watching response times.
If you save a value but do not redeploy or restart, the existing container may continue running with its previous configuration. Verification is part of the change: a value shown in the form is not proof that the running container received it.
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CPU limit
CPU limit is the maximum CPU quantity available to the container. It accepts a whole or decimal number, such as 2 or 0.5. A value of 0 means no configured CPU limit, allowing the container to use all CPUs available to it under the host and other container constraints.
Use a decimal when the workload should receive less than one CPU, and a whole number when it may use multiple CPUs. The limit is a ceiling, not a promise that the container will receive that amount continuously. Actual throughput still depends on contention, the application’s concurrency model, and the other services on the host.
CPU set
CPU set pins the container to specific logical CPU numbers. Enter individual CPUs or ranges, for example 0-2 or 0,1,3. A range such as 0-2 includes CPUs 0, 1, and 2; a comma-separated value selects the listed CPUs.
Pinning can isolate a workload from other services, but it also reduces the CPUs available to that container. Check the host’s CPU numbering and leave enough processors for the operating system, Coolify, and other containers. Do not use a CPU set as a substitute for a CPU limit when your objective is simply to cap total CPU time.
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CPU weight
CPU weight controls relative priority when containers compete for CPU time. The documented default is 1024, and the field accepts a whole number of 0 or greater.
Weight is proportional priority during contention, not reserved capacity. Increasing a container’s weight does not reserve a CPU or guarantee a fixed percentage of processing time; it only changes how Docker shares the CPU time that is available at that moment. A high weight therefore cannot compensate for a host that is already out of CPU capacity.
| CPU field | Accepted example | Effect |
|---|---|---|
| CPU limit | 2, 0.5, or 0 |
Sets a maximum CPU quantity; 0 leaves the limit unconfigured. |
| CPU set | 0-2 or 0,1,3 |
Restricts execution to the selected logical CPUs. |
| CPU weight | 1024 by default |
Changes relative sharing during contention; it does not reserve capacity. |
Configure the memory controls
Accepted memory format
Memory fields accept 0 or a number followed by b, k, m, or g. Units are case-insensitive, so values such as 256m and 1g are valid. Use one unit consistently in each field and check the saved value before redeploying.
| Memory field | Meaning | Example |
|---|---|---|
| Memory reservation | Advises Docker’s memory-pressure accounting about the amount the container should have available. It is not the hard kill threshold. | 256m |
| Memory limit | Sets the hard RAM cap. Exceeding it can lead Docker to terminate the process for running out of memory. | 1g |
| Memory and swap limit | Sets the total of memory plus swap available to the container. | 1g |
| Swappiness | Controls the container’s willingness to use swap, from 0 through 100. The documented default is 60. |
60 |
How the memory values interact
The reservation and limit serve different purposes. Reservation influences pressure accounting, while the memory limit is the enforceable ceiling for RAM. The memory-and-swap limit is a combined total, so it should be considered alongside the RAM limit rather than treated as additional physical memory.
Swappiness is a policy value, not extra capacity. A setting between 0 and 100 changes how readily memory may move to swap; it does not increase the amount of RAM installed in the server. If swap activity rises, application latency may rise as well, so inspect both memory and response-time metrics after changing it.
Choose CPU and memory values without guessing
CPU sizing
Compare the application’s measured CPU utilization with the CPU limit you intend to apply. Test both normal requests and the heaviest operation that users or scheduled jobs generate. If health checks begin timing out after a cap is applied, the limit is too restrictive for that workload or the check interval is being reached before the process can respond.
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When several containers share the host, decide whether you need a ceiling, isolation, or priority:
- Use a CPU limit to stop one container from consuming more than a defined amount.
- Use a CPU set when the workload must run only on selected logical CPUs.
- Use CPU weight to influence which containers receive more of the available time during contention.
These controls can be combined, but none creates CPU capacity that the host does not have. A container with a generous weight can still be throttled by its CPU limit or by the host’s total load.
Memory sizing
Measure peak working-set usage while the application performs its real work. Set the hard memory limit above that peak so short-lived allocations do not cross the cap, while keeping the value within the RAM the host can actually supply. If the process grows beyond the limit, Docker may terminate it rather than allowing uncontrolled host-wide memory pressure.
Choose the reservation to reflect the memory the service should have available under pressure, then evaluate the memory-and-swap total and swappiness together. Watch for swap use and latency, not just whether the process remains alive. A configuration that avoids an immediate out-of-memory exit can still produce unacceptable response times if it relies heavily on swap.
Verify the change after redeploying
Check Metrics
Open the application’s Metrics view after the recreated container is serving traffic. Confirm that CPU and memory behavior matches the intended settings and compare the observed peak with the new limit. Look for sustained CPU saturation, memory growth, and swap activity rather than relying on a single instantaneous reading.
Review Runtime Logs
Inspect Runtime Logs for out-of-memory exits, repeated restarts, and health-check failures. An application that repeatedly starts and stops may have a limit below its startup working set, while a service that stays up but fails checks may be CPU-starved or otherwise unable to respond in time.
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Exercise the normal workload
Send representative traffic, including the endpoints or jobs that normally consume the most resources. Confirm that response times remain acceptable while Metrics and Runtime Logs show the container operating within its limits. Testing only an idle page can hide a peak-memory or CPU-contention failure.
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The container restarts or logs show an out-of-memory exit
First compare the recorded peak memory with the hard memory limit. If the limit is below the application’s working set, raise it and redeploy, or reduce the application’s own memory demand before applying the cap again. Also check whether the memory-and-swap total is constraining the container sooner than expected and whether swap activity is causing instability.
Do not treat repeated restarts as a successful enforcement mechanism. The limit is working as configured, but the value is unsuitable for the application’s current workload. Re-test startup, ordinary traffic, and the largest expected operation after adjusting it.
Health checks time out after a CPU change
Review CPU utilization and health-check failures together. A low CPU limit can leave the process unable to complete a check before its timeout under load. Raise the CPU limit, adjust the CPU set if the process has been pinned too narrowly, or revisit the workload that causes the spike. Changing CPU weight alone will not help if the container is already hitting its hard CPU limit.
The form shows the new value but behavior does not change
Confirm that you selected Save changes and then redeployed or restarted the application. Coolify applies these settings when it recreates the container. Verify the new runtime behavior in Metrics rather than assuming the running container was modified in place.
A direct Docker change disappears
Coolify owns the lifecycle of its managed containers. A manual Docker change can be overwritten the next time Coolify recreates the container, so it is not a durable configuration method. Put lasting resource changes in Coolify’s resource settings or in the source Docker Compose file.
Understand the Coolify, Docker, and Compose boundary
Coolify translates the saved resource settings into Docker operations. If the application is defined with Docker Compose, the Compose file is another durable place for resource configuration, provided it remains the source that Coolify deploys. Keep the chosen source of truth consistent so a later deployment does not silently replace the values you changed elsewhere.
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Custom Docker Options is intended for supported runtime flags such as --shm-size and --ulimit. Use the dedicated CPU and memory fields when they provide the setting you need. Adding ad hoc options is not a substitute for a CPU limit, memory limit, reservation, or swap policy, and unsupported flags can prevent a deployment from starting.
Know when the host is the bottleneck
Limits cannot create resources that the server does not have. If measured demand is greater than the host can supply even after sensible per-container settings, Coolify’s scaling guidance is to resize the current server or move the resource to a larger server.
Compare the infrastructure choices using the workload’s actual constraints:
- Available CPU and RAM, including headroom after system and neighboring services are counted.
- Storage capacity and performance for databases, uploads, and logs.
- Network throughput and any provider-imposed limits.
- Geographic location relative to users and dependent services.
- Reliability characteristics and the recovery options you need.
- Total cost, including the recurring price of the larger or additional server.
After resizing or moving the resource, repeat the Metrics, Runtime Logs, and representative-traffic checks. A larger host changes the available capacity, but the application’s limits still need to be reviewed for the new placement.
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Practical configuration examples
Limit a small web service to half a CPU
Enter 0.5 in CPU limit, leave CPU set empty unless you have a placement reason, and keep CPU weight at its default unless contention testing shows that the service needs a different relative priority. Redeploy, then check whether health checks and response times remain stable during the busiest expected requests.
Allow two CPUs while pinning execution
Enter 2 for CPU limit and a CPU set such as 0-2 when the service should run only on those logical CPUs. Because the set contains three logical CPU numbers while the limit caps total CPU quantity at two, verify the result under load instead of assuming all selected CPUs will be used simultaneously.
Apply a one-gigabyte memory ceiling
Enter 1g as the memory limit only after measuring that the application’s peak working set fits below it with headroom. Set reservation and memory-and-swap values deliberately, then watch for out-of-memory exits, restarts, swap use, and latency in the post-deployment test.
Quick Recap
Final checklist
- Measured CPU, peak memory, swap behavior, health checks, latency, and host headroom before changing limits.
- Used valid CPU values, CPU-set syntax, and memory units.
- Distinguished CPU weight from reserved capacity and memory reservation from the hard limit.
- Saved the settings and redeployed or restarted the application.
- Checked Metrics, Runtime Logs, and representative traffic.
- Stored lasting changes in Coolify or the source Compose configuration rather than making one-off Docker edits.
- Resized or moved the resource when the host could not provide the required CPU or memory.
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