# Autoscaling
{% hint style="warning" %}
Autoscaling features are only available for realms using the **Consumption-Based model**.
If your realm uses this model, it is highly recommended to activate Autoscaling to benefit from automatic elasticity, which can reduce consumption costs.
{% endhint %}
## What is autoscaling
Autoscaling is the process of adjusting computational resources based on workload. Pipelines are horizontally scalable, meaning that replicas can be **dynamically created or removed** depending on the number of messages (invocations) in the queue.
The Digibee Integration Platform continuously monitors the queue and adjusts the number of replicas to ensure **optimal resource usage** and **efficient processing**, regardless of the trigger type that generated the messages.
## How autoscaling works
Every pipeline execution starts with a **trigger**. Different trigger types, such as [Scheduler](/documentation/connectors-and-triggers/triggers/scheduling/scheduler.md), [Event ](/documentation/connectors-and-triggers/triggers/messaging-and-events/event.md)or [HTTP](/documentation/connectors-and-triggers/triggers/web-protocols/http.md), have unique behaviors and activation criteria. These triggers determine whether a message is sent to the execution queue for further processing or whether it should be skipped or rejected altogether.
### Autoscaling algorithm
The autoscaling algorithm scales pipelines **horizontally**. Every **10 seconds**, the Platform monitors the system and can execute the following commands:
#### Activation (from 0 to 1)
* If the **minimum number of replicas is set to zero**, the pipeline can receive messages but has no replicas running.
* The first replica is deployed when the **first message arrives**, which can **add latency**.
* The minimum startup time is **20 seconds**.
* Once initialized, the replica processes the first and subsequent messages.
{% hint style="info" %}
The number and type of connectors in the pipeline can impact startup time.
{% endhint %}
#### Scale up (from 1 to N)
When the number of messages in the queue exceeds **70% of total concurrent executions**, the Platform deploys **additional replicas**.
#### Scale down (from N to 1)
The Platform **reduces replicas** when fewer resources are needed. The system calculates the required number of replicas using the formula below:
{% code fullWidth="true" %}
```
Replicas = [(Message rate per second * Response time in seconds) / Total concurrent executions)]
```
{% endcode %}
#### Scale-to-Zero (Cooldown)
If the **minimum replica is zero** and a single replica is idle for **more than 60 seconds**, the pipeline scales **down to zero**. New replicas are deployed only when new messages arrive.
* **Custom Cooldown:** You can configure a custom cooldown time between 60 and 300 seconds in the pipeline’s Advanced Settings. This defines how long the system waits before scaling back to zero after idle.
## Autoscaling on the Digibee Integration Platform
The autoscaling process on the Digibee Integration Platform follows these steps:
### Phase 1: Activation of the first replica
1. The system executes an **Activation command** to create and start the **first replica**.
2. This command triggers the processing of the **first message in the queue**.
3. Components for pipeline processing are initialized only when the replica is deployed, that is, when messages arrive.
{% hint style="info" %}
Since the last step can add latency to the first message’s total response time, it’s recommended to set **min replicas ≥ 1** for latency-sensitive pipelines.
{% endhint %}
### Phase 2: Processing of messages
1. After the first replica is deployed and initialized, the Platform processes the first set of messages according to the concurrency settings.
2. When **new messages arrive:**
* They are processed by available consumers if capacity allows.
* Otherwise, a [Scale Up command](#scale-up-from-1-to-n) starts a **new replica**.
{% hint style="info" %}
Scale Up impact is smaller on total response time because it triggers only when the queue reaches 70% of concurrent execution capacity.
{% endhint %}
**Total processing capacity formula:**
```
Total capacity = Replicas × Simultaneous executions per replica
```
### Phase 3: Scaling down
* If the queue has only a few messages, the Platform reduces the number of running replicas until only one remains.
* If the single replica remains idle for **more than 60 seconds**, a [Cooldown command](#scale-to-zero-cooldown) scales the pipeline down to zero replicas.
* New replicas are deployed only when the next set of messages arrives.
## Autoscaling strategies
There are two types of autoscaling deployment strategies on the Digibee Integration Platform: **Scale-to-Zero** and **Always On**.
### Scale-to-Zero
The pipeline is deployed with autoscaling enabled and a **minimum of zero replicas**. Pipelines can receive new requests but operate with “zero replicas”, incurring no cost until the first message is received.
#### Recommended usage scenario
This strategy is ideal for workloads that **don’t require immediate response** for the first message. For example, a pipeline that runs once a day can remain inactive most of the time.
See more scenarios where Scale-to-Zero is applicable:
* Scheduled pipelines using the [Scheduler trigger](/documentation/connectors-and-triggers/triggers/scheduling/scheduler.md).
* Low-criticality workloads where the first message can tolerate extra latency.
* Workloads aiming to maximize cost savings during idle periods.
### Always On
The pipeline is deployed with autoscaling enabled and **a minimum of 1 or more replicas**. At least one replica is always running, ready to process the first messages immediately.
#### Recommended usage scenario
This strategy ensures **low-latency response** even during low traffic periods. Always On is applicable on the following scenarios:
* [API-triggered pipelines](/documentation/connectors-and-triggers/triggers/web-protocols/api.md).
* High-criticality workloads where extra response time for the first message is not acceptable.
{% hint style="success" %}
**Optimization tip:** Replace a Large pipeline with 1 replica and no autoscaling with a Small pipeline with autoscaling enabled, using 1 to 4 replicas.
{% endhint %}
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