> ## Documentation Index
> Fetch the complete documentation index at: https://cerebrium-fix-make-entrypoint-docs-explicit.mintlify.site/llms.txt
> Use this file to discover all available pages before exploring further.

# Scaling Apps

> Learn to optimise for cost and performance by scaling out apps

Cerebrium's scaling system automatically manages computing resources to match app demand. The system handles everything from a few simple requests, to the processing of multiple requests simultaneously, while optimizing for both performance and cost.

## How Autoscaling Works

The scaling system monitors two key metrics to make scaling decisions:

The **number of requests** currently waiting for processing in the queue indicates immediate demand. Additionally, the system tracks **how long each request has waited in the queue**. When either of these metrics exceeds their thresholds, new instances start within 3 seconds to handle the increased load.

<Info>
  Scaling is also configurable based on the expected traffic of an application.
  See below for more information.
</Info>

As traffic decreases, instances enter a cooldown period at reduced concurrency. If reduced concurrency is maintained for the cooldown duration, instances scale down to optimize resource usage. This automatic cycle ensures apps remain responsive while managing costs effectively.

## Scaling Configuration

The `cerebrium.toml` file controls scaling behavior through several key parameters:

```toml theme={null}
[scaling]
min_replicas = 0           # Minimum running instances
max_replicas = 3           # Maximum concurrent instances
cooldown = 60              # Cooldown period in seconds
replica_concurrency = 1    # The maximum number of requests each replica of an app can accept
```

### Minimum Instances

The `min_replicas` parameter defines how many instances remain active at all times. Setting this to 1 or higher maintains warm instances ready for immediate response, eliminating cold starts but increasing costs. This configuration suits apps that require consistent response times or need to meet specific SLA requirements.

### Maximum Instances

The `max_replicas` parameter sets an upper limit on concurrent instances, controlling costs and protecting backend systems. When traffic increases, new instances start automatically up to this configured maximum.

### Cooldown Period

The `cooldown` parameter specifies the time window (in seconds) that must pass at reduced concurrency before an instance scales down. This prevents premature scale-down during brief traffic dips that might be followed by more requests. A longer cooldown period helps handle bursty traffic patterns but increases instance running time and cost.

### Replica Concurrency

The number of requests an app instance can handle concurrently is dictated by the `replica_concurrency` parameter. This is a hard limit, and an individual replica will
not accept more than this limit at a time. By default, once this concurrency limit is reached on an instance and there are still requests to be processed in-flight,
the system will scale out by the number of new instances required to fulfil the in-flight requests. For example, if `replica_concurrency=1` and there are
*3* requests in flight with no replicas currently available, Cerebrium will scale out 3 instances of the application to meet that demand.

<Info>
  Typically most GPU applications will require that `replica_concurrency` is set
  to **1**. If the workload requires GPU but higher throughput is desired,
  `replica_concurrency` may be increased so long as access to GPU resources is
  controlled within the application through batching.
</Info>

## Processing Multiple Requests

Apps can process multiple requests simultaneously using Cerebrium's batching and concurrency features. The system offers native support for frameworks with built-in batching capabilities and enables custom implementations through the [custom runtime](cerebrium/container-images/defining-container-images#custom-runtimes) feature. For detailed information about handling multiple requests efficiently, see our [Batching & Concurrency Guide](/cerebrium/scaling/batching-concurrency).

## Instance Management

Cerebrium ensures reliability through automatic instance health management. The system restarts instances that encounter issues, quickly starts new instances to maintain processing capacity, and monitors instance health continuously.

Apps requiring maximum reliability often combine several scaling features:

```toml theme={null}
[scaling]
min_replicas = 2              # Maintain redundant instances
cooldown = 600                # Extended warm period
max_replicas = 10             # Room for traffic spikes
response_grace_period = 1200  # Maximum request lifespan ensuring graceful exit
```

The `response_grace_period` parameter stipulates how long in seconds a request would need at most to finish, and provides time for instances to complete active requests during normal operation and shutdown.
During normal replica operation, this simply corresponds to a request timeout value. During replica shutdown, the Cerebrium system sends a SIGTERM signal to the replica,
waits for the specified grace period, issues a SIGKILL command if the instance has not stopped, and kills any active requests with a GatewayTimeout error.

<Note>
  When using the cortex runtime (default), SIGTERM signals are automatically
  handled to allow graceful termination of requests. For custom runtimes, you'll
  need to implement SIGTERM handling yourself to ensure requests complete
  gracefully before termination. See our [Graceful Termination
  guide](/cerebrium/scaling/graceful-termination) for detailed implementation
  examples, including FastAPI patterns for tracking and completing in-flight
  requests during shutdown.
</Note>

Performance metrics available through the dashboard help monitor scaling behavior:

* Request processing times
* Active instance count
* Cold start frequency
* Resource usage patterns

The system status and platform-wide metrics remain accessible through our [status page](https://status.cerebrium.ai), where Cerebrium maintains 99.9% uptime.

## Using Scaling Metrics

Cerebrium provides a variety of scaling criteria which may be used to scale apps according to different metrics. As mentioned above,
by default this is determined by an application's `replica_concurrency`. However, this strategy may be insufficient for some use cases
and so Cerebrium currently provides four scaling metrics to choose from:

* `concurrency_utilization`
* `requests_per_second`
* `cpu_utilization`
* `memory_utilization`

These can be added to the `scaling` section as such, by specifying one of these metrics and a target:

```toml theme={null}
[scaling]
min_replicas = 0
cooldown = 600
max_replicas = 10
response_grace_period = 120
replica_concurrency = 1
scaling_metric = "concurrency_utilization"
scaling_target = 100
```

### Concurrency Utilization

`concurrency_utilization` is the default scaling metric, and defaults to a target of *100%* if not set explicitly.
This scaling metric works by maintaining a maximum percentage of your `replica_concurrency` averaged across every instance of
the app. For example, if an application has `replica_concurrency=1` and `scaling_target=70`, Cerebrium will attempt
to maintain *0.7* requests per instance across your entire deployed service. This strategy would always ensure an extra
30% capacity is provisioned.

As a different example, say an app has `replica_concurrency=200` and `scaling_target=80`. In this case, Cerebrium will
maintain *160* requests per instance, and will begin to scale out once that target has exceeded.

### Requests per Second

`requests_per_second` is straightforward criterion which aims to maintain a maximum application throughput
measured in requests per second averaged over every application instance. This can be a more effective scale metric than `concurrency_utilization`
if appropriate performance evaluation has been done on the application to determine the throughput. This criterion
is not recommended for most GPU applications, since this scaling metric does not enforce concurrency limits. For example,
if `scaling_target=5`, Cerebrium will attempt to maintain a 5 requests/s average across all app instances.

### CPU Utilization

`cpu_utilization` uses a maximum CPU percentage utilization averaged over all instances of an application to scale out, relative to the
`hardware.cpu` value. For example, if an application has `cpu=2` and `scaling_target=80`, Cerebrium will attempt
to maintain *80%* CPU utilization (1.6 CPUs) per instance across your entire deployed service. Since there is no notion of
scaling relative to 0 CPU units, it is required that `min_replicas=1` if using this metric.

### Memory Utilization

`memory_utilization` uses a maximum memory percentage utilization averaged over all instances of an application to scale out, relative to the
`hardware.memory` value. Note this refers to RAM, **not** GPU VRAM utilization. For example, if an application has `memory=10` and `scaling_target=80`, Cerebrium will attempt
to maintain *80%* Memory utilization (8GB) per instance across your entire deployed service. Since there is no notion of
scaling relative to 0GB of memory, it is required that `min_replicas=1` if using this metric.

## Keeping a Scaling Buffer

In certain use-cases where app startup time or total request time is long and traffic is predictable, a consistent excess capacity
which is dynamically added to the app's current required capacity may be needed to meet adequate request throughput.
Cerebrium provides a replica buffer mechanism to cater for this, whereby the scaling system aims to keep a specific excess replica capacity
available at all times above what the scaling metric suggests. This is done through the `scaling_buffer` option in the config. Currently, this
is only available when using the following scaling metrics:

* `concurrency_utilization`
* `requests_per_second`

The buffer can be added to the `scaling` section as such, by specifying `scaling_buffer`:

```toml theme={null}
[scaling]
min_replicas = 1
cooldown = 600
max_replicas = 10
response_grace_period = 120
replica_concurrency = 1
scaling_metric = "concurrency_utilization"
scaling_target = 100
scaling_buffer = 3
```

To illustrate how this works using the above config, if this application receives no traffic, it will buffer out to **1 replica** of capacity as a baseline - the buffer scales based on requests.
Since the config has specified `100` as a target for `concurrency_utilization` and `replica_concurrency=1`, if the application now receives 1 request
the autoscaler will suggest a value of 1 replica for scale out. Since however, we have `scale_buffer=3`, the application will actually scale one more replica to **(1+3)=4**.
In other words, the scale buffer will simply add a static amount of replicas to the number of replicas the autoscaler suggests using the scale target.
Once this request has completed, the usual `cooldown` period will apply, and the app replica count will scale down back to the baseline of **1 replica**.

## Evaluation Interval

<Warning>Requires CLI version 2.1.5 or higher.</Warning>

The `evaluation_interval` parameter controls the time window (in seconds) over which the autoscaler evaluates metrics before making scaling decisions. The default is 30 seconds, with a valid range of 6-300 seconds.

```toml theme={null}
[scaling]
evaluation_interval = 30  # Evaluate metrics over 30-second windows
```

A shorter interval makes the autoscaler more responsive to traffic spikes but may cause more frequent scaling events. A longer interval smooths out transient spikes but may delay scaling responses.

<Info>
  For bursty workloads, a shorter `evaluation_interval` (e.g., 10-15 seconds)
  helps the system respond quickly to demand. For steady workloads, a longer
  interval (e.g., 60 seconds) reduces unnecessary scaling churn.
</Info>

## Load Balancing

<Warning>Requires CLI version 2.1.5 or higher.</Warning>

The `load_balancing` parameter controls how incoming requests are distributed across your replicas. When not specified, the system automatically selects the best algorithm based on your `replica_concurrency` setting.

```toml theme={null}
[scaling]
load_balancing = "min-connections"  # Explicitly set load balancing algorithm
```

**Default behavior**: When `load_balancing` is not set, the system uses `first-available` for `replica_concurrency <= 3` (typical for GPU workloads) and `round-robin` for higher concurrency.

### Available Algorithms

#### round-robin

Cycles through replicas starting from the last successful target. Each replica's concurrency limit is respected - if a replica is at capacity, the algorithm proceeds to the next one in rotation.

| Characteristic       | Value                                                                   |
| -------------------- | ----------------------------------------------------------------------- |
| Selection complexity | O(1) typical, O(N) worst case when scanning for available capacity      |
| Latency profile      | Consistent p50, good p90 under uniform load                             |
| Strategy             | Stateful index rotation with mutex synchronization; skips full replicas |

**Best for**: Workloads with predictable request times where you want even distribution across replicas over time.

#### first-available

Scans replicas from the start of the list and selects the first one with available capacity.

| Characteristic       | Value                                                                         |
| -------------------- | ----------------------------------------------------------------------------- |
| Selection complexity | O(1) typical, O(N) worst case                                                 |
| Latency profile      | Optimal p50 when load is light, may degrade p90 under high load               |
| Strategy             | Linear scan from list start; returns first replica that accepts via Reserve() |

**Best for**: GPU workloads with low concurrency (`replica_concurrency <= 3`). Maximizes utilization of warm replicas before spreading load, reducing cold starts and keeping models in VRAM.

**Tradeoff**: Earlier replicas in the list handle more traffic. This is desirable for GPU workloads but may cause uneven distribution for CPU workloads.

#### min-connections

Linear scan to find the replica with the fewest in-flight requests, then attempts to reserve it. If that replica cannot accept (at capacity), falls back to trying other replicas in iteration order.

| Characteristic       | Value                                                              |
| -------------------- | ------------------------------------------------------------------ |
| Selection complexity | Θ(N) - always scans all replicas to find minimum                   |
| Latency profile      | Best p90/p99 tail latency                                          |
| Strategy             | Single pass to find minimum in-flight; fallback in iteration order |

**Best for**: Workloads with variable request times (e.g., LLM inference where output length varies). Routes new requests to the least busy replica, preventing fast requests from queuing behind slow ones.

#### random-choice-2

Implements the "Power of Two Choices" algorithm: randomly samples two replicas and routes to the one with lower weight (based on active request tracking). Ties are broken randomly.

| Characteristic       | Value                                                       |
| -------------------- | ----------------------------------------------------------- |
| Selection complexity | Θ(1) - constant time regardless of replica count            |
| Latency profile      | Good balance of p50 and p90                                 |
| Strategy             | Sample 2 random replicas, compare weights, pick lighter one |

**Best for**: High-throughput scenarios with many replicas where selection overhead matters. Research shows this achieves exponentially better load distribution than pure random selection.

**Note**: Uses weight-based tracking rather than reservation-based concurrency limiting, making it suitable for unlimited concurrency scenarios.

### Choosing an Algorithm

| Scenario                                 | Recommended                 | Reason                                             |
| ---------------------------------------- | --------------------------- | -------------------------------------------------- |
| GPU inference, `replica_concurrency=1`   | `first-available` (default) | Maximizes GPU utilization, keeps models warm       |
| LLMs with variable output lengths        | `min-connections`           | Prevents head-of-line blocking, best tail latency  |
| High-throughput, many replicas           | `random-choice-2`           | Θ(1) selection with near-optimal distribution      |
| Uniform request times, even distribution | `round-robin`               | Predictable rotation, no hot spots over time       |
| Latency-sensitive with variable load     | `min-connections`           | Minimizes p90/p99 by routing to least busy replica |