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Deploy Grafana Alloy

Alloy is a flexible, vendor-neutral telemetry collector. This flexibility means that Alloy doesn’t enforce a specific deployment topology but can work in multiple scenarios.

This page lists common topologies used for Alloy deployments, when to consider using each topology, issues you may run into, and scaling considerations.

As a centralized collection service

Deploying Alloy as a centralized service is recommended for collecting application telemetry. This topology allows you to use a smaller number of collectors to coordinate service discovery, collection, and remote writing.

Using this topology requires deploying Alloy on separate infrastructure, and making sure that they can discover and reach these applications over the network. The main predictor for the size of an Alloy deployment is the number of active metrics series it’s scraping. A rule of thumb is approximately 10 KB of memory for each series. We recommend you start looking towards horizontal scaling around the 1 million active series mark.

Using Kubernetes StatefulSets

Deploying Alloy as a StatefulSet is the recommended option for metrics collection. The persistent Pod identifiers make it possible to consistently match volumes with pods so that you can use them for the WAL directory.

You can also use a Kubernetes Deployment in cases where persistent storage isn’t required, such as a traces-only pipeline.

Pros

Straightforward scaling using clustering
Minimizes the “noisy neighbor” effect
Easy to meta-monitor

Cons

Requires running on separate infrastructure

Use for

Scalable telemetry collection

Don’t use for

Host-level metrics and logs

As a host daemon

Deploying one Alloy instance per machine is required for collecting machine-level metrics and logs, such as node_exporter hardware and network metrics or journald system logs.

Each Alloy instance requires you to open an outgoing connection for each remote endpoint it’s shipping data to. This can lead to NAT port exhaustion on the egress infrastructure. Each egress IP can support up to (65535 - 1024 = 64511) outgoing connections on different ports. So, if all Alloys are shipping metrics and log data, an egress IP can support up to 32,255 collectors.

Using Kubernetes DaemonSets

The simplest use case of the host daemon topology is a Kubernetes DaemonSet, and it’s required for node-level observability (for example cAdvisor metrics) and collecting Pod logs.

Pros

Doesn’t require running on separate infrastructure
Typically leads to smaller-sized collectors
Lower network latency to instrumented applications

Cons

Requires planning a process for provisioning Alloy on new machines, as well as keeping configuration up to date to avoid configuration drift
Not possible to scale independently when using Kubernetes DaemonSets
Scaling the topology can strain external APIs (like service discovery) and network infrastructure (like firewalls, proxy servers, and egress points)

Use for

Collecting machine-level metrics and logs (for example, node_exporter hardware metrics, Kubernetes Pod logs)

Don’t use for

Scenarios where Alloy grows so large it can become a noisy neighbor
Collecting an unpredictable amount of telemetry

As a container sidecar

Deploying Alloy as a container sidecar is only recommended for short-lived applications or specialized Alloy deployments.

Using Kubernetes Pod sidecars

In a Kubernetes environment, the sidecar model consists of deploying Alloy as an extra container on the Pod. The Pod’s controller, network configuration, enabled capabilities, and available resources are shared between the actual application and the sidecar Alloy.

Pros

Doesn’t require running on separate infrastructure
Straightforward networking with partner applications

Cons

Doesn’t scale separately
Makes resource consumption harder to monitor and predict
Each Alloy instance doesn’t have a life cycle of its own, making it harder to do things like recovering from network outages

Use for

Serverless services
Job/batch applications that work with a push model
Air-gapped applications that can’t be otherwise reached over the network

Don’t use for

Long-lived applications
Scenarios where the Alloy deployment size grows so large it can become a noisy neighbor

Processing different types of telemetry in different Alloy instances

If the load on Alloy is small, you can process all necessary telemetry signals in the same Alloy process. For example, a single Alloy deployment can process all of the incoming metrics, logs, traces, and profiles.

However, if the load on Alloy is big, it may be beneficial to process different telemetry signals in different deployments of Alloy.

This provides better stability due to the isolation between processes. For example, an overloaded Alloy instance processing traces won’t impact an Alloy instance processing metrics. Different types of signal collection require different methods for scaling:

“Pull” components such as prometheus.scrape and pyroscope.scrape are scaled using hashmod sharing or clustering.
“Push” components such as otelcol.receiver.otlp are scaled by placing a load balancer in front of the components.

Traces

Scaling Alloy instances for tracing is very similar to scaling OpenTelemetry Collector instances. This similarity is because most Alloy components used for tracing are based on components from the OTel Collector.

When to scale

To decide whether scaling is necessary, check metrics such as:

receiver_refused_spans_ratio_total from receivers such as otelcol.receiver.otlp.
processor_refused_spans_ratio_total from processors such as otelcol.processor.batch.
exporter_send_failed_spans_ratio_total from exporters such as otelcol.exporter.otlp and otelcol.exporter.loadbalancing.

Stateful and stateless components

In the context of tracing, a “stateful component” is a component that needs to aggregate certain spans to work correctly. A “stateless Alloy” is an Alloy instance which doesn’t contain stateful components.

Scaling stateful Alloy instances is more difficult, because spans must be forwarded to a specific Alloy instance according to a span property such as trace ID or a service.name attribute. You can forward spans with otelcol.exporter.loadbalancing.

Examples of stateful components:

otelcol.processor.tail_sampling
otelcol.connector.spanmetrics
otelcol.connector.servicegraph

A “stateless component” doesn’t need to aggregate specific spans to work correctly. It can work correctly even if it only has some of the spans of a trace.

A stateless Alloy instance can be scaled without using otelcol.exporter.loadbalancing. For example, you could use an off-the-shelf load balancer to do a round-robin load balancing.

Examples of stateless components: