Installation Guidelines

This page will provide guidelines and recommendations for installing Octopus Deploy on your infrastructure. If you are using Octopus Cloud, please skip to the next section in the guide.

The size of your Octopus Deploy instance will be dependent on concurrent tasks (deployments, runbook runs, etc.) and users. A team of 10 people doing five deployments a day will not need the same resources as a division of 500 users doing 600 deployments a day.

This document will refer to tasks; a task can be:

  • Deployments
  • Runbook run
  • Retention Policies
  • Health Checks
  • Let’s Encrypt
  • Process triggers
  • Process subscriptions
  • Script console run
  • Sync built-in package repository
  • Sync community library step-templates
  • Tentacle upgrade
  • Upgrade calamari
  • Active Directory sync

Of that list, Deployments and Runbook Runs are the most common.

Octopus Deploy installation requirements are:

  • SQL Server 2016 or higher (AWS RDS SQL Server and Azure SQL are supported)
  • Windows Server 2012 R2 or later when hosting on Windows Server
  • Linux is supported when using the Octopus Deploy Linux Docker image

High availability functionality may or may not be included in your license. All Server licenses, or Professional/Enterprise licenses sold after 2022 include at least 2 nodes.

High availability works in Octopus Deploy by dropping tasks into a queue. Periodically, each high availability node will check the queue for work. The node will pick up any pending tasks. The nodes have a built-in load balancer algorithm to ensure one node doesn’t perform all the work.

Here are some items to consider when installing Octopus Deploy:

  • Cloud providers (GCP, AWS, Azure) will charge roughly the same for 2 VMs with 2 cores / 4 GB of RAM or 1 VM with 4 cores / 8 GB of RAM. The difference in cost is typically less than $10 USD per month.
  • The ideal number of concurrent tasks is 10-15 for every 2 cores / 4 GB of RAM.
  • Going over 40-60 tasks on a single server typically requires much more compute resources.
  • The maximum number of tasks a single server can process is between 80 - 120. That requires a significant amount of compute resources, appx 16 vCPUs / 64 GB RAM. With that many concurrent tasks, we start seeing underlying host limitations. There are so many network ports, concurrent threads, and other resources a server can have.
  • The maximum number of recommended High Availability nodes on a single cluster is 6 to 8.
  • It is much more performant when the Octopus Deploy service and SQL Server are separated.
  • The less latency between SQL Server and Octopus Deploy, the better.
  • Configuring high availability mode provides multiple benefits, the most important being removing a single point of failure.
  • It is trivial to add additional high availability nodes after the initial configuration.

Calculating concurrent Tasks

Except in extreme cases, you will be processing between 5-10 concurrent tasks for quite some time. There might be one or two times when you go over that limit, but those tasks will queue for a few minutes before being processed. In our experience, most deployments take between 20-30 minutes. With a task cap of 5 to 10, that enables:

  • 10 to 30 tasks per hour
  • 40 to 120 tasks for a 4 hour span
  • 80 to 240 tasks for a 8 hour span
  • 120 to 360 tasks for a 12 hour span

We recommend using a Production deployment window to determine how many tasks you’ll need. The eventual goal of CI/CD is to provide the capability to deploy to Production in the middle of the day. However, in practice, depending on the application and users, that may not be feasible nor desirable. The Production deployment window is when all deployments must complete.

You should consider:

  • The duration of the window
  • How many applications need to be deployed during the window
  • How long, on average, each deployment will take. If you are unsure, we recommend starting with 30 minutes.

Knowing those three items, you can calculate the number of “deployment minutes” required. Deploying 50 applications, each taking 30 minutes during the deployment window, will require 1,500 “deployment minutes.” Then we can divide that by the duration of the window, for example, two hours or 120 minutes. 1,500 divided by 120 is 12.5, which is the minimum task cap. That minimum task cap makes a number of assumptions regarding your deployment, namely everything goes perfectly and there are no failures or retries. In this example, a reasonable task cap would be 15 to 20.

A rule of thumb is: When you see more and more tasks being queued, then it’s time to add capacity.

Some reference points to consider:

  • One customer has ~10,000 deployment targets, 120 projects and performs 400-500 deployments a day. Their instance is configured to handle 160 concurrent tasks with a burst on Sundays to 320 tasks.
  • Another customer has ~1400 deployment targets, 800 projects and performs 600-700 deployments a day. Their instance is configured to handle 100 concurrent tasks.

Host Octopus on Windows Server or as a Linux Container

Our recommendation is to host Octopus Deploy Windows Server over the Octopus Server Linux Container unless you are okay with all these conditions:

  • You plan on using LDAP, Okta, Azure AD, Google Auth, or the built-in username and password to authenticate users. The current version of the Octopus Server Linux Container only supports Active Directory authentication via LDAP.
  • You are okay running at least one worker to handle tasks typically done by the Octopus Server. The Octopus Server Linux Container doesn’t include PowerShell Core or Python.
  • You are familiar with Docker concepts, specifically around debugging containers, volume mounting, and networking.
  • You are comfortable with one of the underlying hosting technologies for Docker containers; Kubernetes, ACS, ECS, AKS, EKS, or Docker Swarm.
  • You understand Octopus Deploy is a stateful, not a stateless application, requiring additional monitoring.

Due to how Octopus stores the paths to various BLOB data (task logs, artifacts, packages, imports, event exports etc.), you cannot run a mix of both Windows Servers, and Octopus Linux containers connected to the same Octopus Deploy instance. A single instance should only be hosted using one method.

We are confident in the Octopus Server Linux Container’s reliability and performance. After all, Octopus Cloud runs on the Octopus Linux container in AKS clusters in Azure. But to use the Octopus Server Linux Container in Octopus Cloud, we made some design decisions and create custom workflows due to the above limitations. We restrict the authentication options to Okta, AzureAD, OctopusID, Google Auth and the built-in username and password. Octopus Cloud disables the built-in worker and uses dynamic workers. Finally, we have a process that injects a custom logging configuration to output the server logs to our Seq instance so we can debug any issues.

Below is the default configuration for Octopus Cloud. We’ve found this provides the resources necessary for 10 concurrent tasks. Anything more, and we have to either increase the container or database resources.

  • Docker images hosted on AKS clusters with the CPU set to 400m and a limit of 4000m, memory set to 400Mi with the limit set of 4Gi.
  • Azure SQL database with 50 DTUs.
  • Azure File Storage hosting all the BLOB data.

We are currently working with our existing customers on what best practices look like to self-host the Octopus Server Linux Container. If you’d like further recommendations please contact us.

Small-medium scale configuration

Our recommendation is to configure Octopus Deploy to run in high availability mode from the start, even if you only plan on running one node.

For the remainder of this document, the assumption is you will be using Windows Servers.

A high availability configuration will involve setting up:

  • 1 to 3 Windows servers, each with 2 cores / 4 GB of RAM with the task cap set to 15 for each server.
  • SQL Server to host Octopus Deploy database with 2 Cores / 8 GB of RAM or 50-100 DTUs
  • Load balancer for web traffic
  • 40 GB of File storage (NAS, SAN, Azure File Storage, AWS FSx, etc.)

small instance diagram

Depending on the number of nodes, that configuration will give you the capacity to process 15-45 concurrent tasks. If you need to scale up quickly, double the compute resources, for example, 4 CPUs / 8 GB of RAM, to get to 30-90 concurrent tasks. We don’t recommend going beyond 8 CPUs / 16 GB of RAM and instead recommend scaling horizontally.

Even with a single node, taking the time to configure a load balancer and the separate file storage will ensure your Octopus Deploy instance is more resilient. If the server hosting Octopus Deploy were ever to crash or stop responding, recovery time is measured in minutes, not hours. Having a two-node cluster will result in zero downtime in the event of crashes or regular restarts to update Windows.

Octopus Deploy Windows Server

Octopus Deploy is a Windows service that will run as Local System by default. If possible, run that service using a specific Active Directory or Azure Active Directory account. Use integrated security instead for the database instead of providing a username and password.


For the database, we will typically see customers who already have a very powerful production SQL Server 2016+ Server, Azure SQL Database, or AWS RDS SQL Server monitored by DBAs already running. If that server has plenty of capacity, then we recommend re-using that.

But, if you need to stand up a new server, that is more than okay. Our recommendations are

  • Small teams/companies or customers doing a POC with 5-10 concurrent tasks: SQL Server Express or Standard with 2 Cores / 4 GB of RAM
  • Small-Medium companies or customers doing a pilot with 5-20 concurrent tasks: SQL Server Standard or Enterprise with 2 Cores / 8 GB of RAM or Azure SQL with 50-100 DTUs
  • Medium to Large companies doing 20+ concurrent tasks: SQL Server Standard or Enterprise with at least 4 cores / 16 GB of RAM or Azure SQL with 200+ DTUs.

If you are going to run SQL Server Standard or Enterprise, configure either a failover cluster instance or an availability group to ensure database resiliency.

Keep an eye on your database resources as you increase the number of concurrent tasks and users. You should perform routine SQL Server maintenance periodically to maintain performance, including rebuilding indexes, regenerating stats, and regular backups.

To ensure high performance, the SQL Server and the servers hosting Octopus Deploy must be in the same data center or region to keep latency to a minimum.

Configure task cap

By default, the number of concurrent tasks for each Octopus Deploy node is 5. Increase that to 10 using this guide. We don’t recommend going beyond 40-60, even if the node has the necessary compute resources.

As stated earlier, each node will pick up tasks until it reaches its task cap or it runs out of pending tasks to pick up. Octopus has a load balancing algorithm in place to ensure one node in an HA cluster doesn’t process all the pending tasks. The algorithm will do its best to keep the load even, but outside factors, such as duration of task, the kind of task, etc., could result in one node seemingly doing more work than others.

Setting the task cap to 0 will mean that node picks up no tasks. It will only host web requests for the Octopus Deploy UI.

Load Balancer

The Octopus Deploy UI is a stateless React single page application that leverages a RESTful API for its data. Any standard load balancer, be it F5, Netscaler, or provided via a cloud provider, will work. If you need a small load balancer, NGINX will provide all the functionality you’ll need.

The recommendations for load balancers are:

  • Start with round-robin or “least busy” mode.
  • SSL offloading for all traffic over port 443 is fine (unless you plan on using polling Tentacles over web sockets).
  • Use /api/octopusservernodes/ping to test service health.

Octopus Deploy will return the name of the node in the Octopus-Node response header.

If you plan on having external polling Tentacles connect to your instance through a load balancer / firewall you will need to configure passthrough ports to each node. Our high availability guides provide steps on how to do this.

File Storage

Octopus Deploy stores BLOB items such as task logs (generated during deployments), deployment artifacts, packages, project images, event exports on a file share instead of in the database.

The kind of file storage will depend on where you are hosting Octopus Deploy.

  • On-premise data center: Any SMB-based file storage technology will work. If running Octopus Deploy as a specific Active Directory account, limit permissions to the file share to that account and system administrators.
  • AWS Windows EC2 instance: Use AWS FSx
  • Azure: Use Azure File storage


You can use the same /api/octopusservernodes/ping to monitor service uptime. Any monitoring tool that allows you to make HTTP calls to test health will work. Internally we use the tool Better Uptime to track Octopus Deploy status and alert us when it is down.

Large-Scale configuration

The above recommendation is designed for people working in small to medium-sized companies or people working in large companies getting started with Octopus, perhaps during an initial pilot of 4-7 teams. The recommendation below is for a large Octopus Deploy configuration designed to handle close to 1000 deployments a day. If you follow the advice in the small-medium scale configuration section, it will be easy to scale up to this as all the necessary infrastructure; load balancer, file storage, and SQL Server, will be in place.

We don’t recommend starting with this unless you plan to onboard dozens of teams quickly or you have a lengthy approval process.

  • 4 Windows servers with 4 Cores / 8 GB of RAM, with each server having the task cap set to 30.
  • 2 Windows servers with 4 Cores / 8 GB of RAM, with each server having the task cap set to 0. These are UI-only nodes.
  • SQL Server 2016 Standard or higher (or Azure SQL) running on at least 4 Cores / 16 GB of RAM with either Failover Cluster Instance or Availability Groups configured.
  • 200 GB of file storage.
  • Load balancer to manage traffic to UI-only nodes.

large scale instance

The configuration above is a baseline. We recommend monitoring your resources as you add projects, users and do more deployments and runbook runs. The more data, the more Octopus UI and database have to process. Experiment with increasing compute resources for the SQL Server and the UI nodes. If you run into any performance concerns, please contact support.

This configuration will provide 120 concurrent deployments, with the capacity to quickly increase to 160. The two UI-only nodes will enable users to interact with Octopus Deploy without consuming compute resources needed to orchestrate deployments. If you need to process more than 160 concurrent tasks, first increase the compute resources on the task-only nodes and database server. Once you reach 16 Cores / 64 GB of RAM it is time to start adding nodes.

Managing Nodes

High availability allows you to add multiple nodes to your Octopus Deploy instance. That, in turn, opens up additional questions about how to manage those nodes.

Each node should update the OctopusServerNodes table in the database once a minute. This tells the other nodes it is still alive and can continue to process tasks.

Adding High Availability Nodes

Once high availability is configured, the steps to add a new Windows server node are:

  1. Create a Windows server and mount the file shares.
  2. Install Octopus Deploy and point it to an existing database.
  3. Configure task cap.
  4. (Optional) add the new node into the load balancer (if the node is hosting the Octopus UI).

To add a new Linux Docker image node, the steps are:

  1. Spin up a new Docker image with the arguments pointing to an existing database and mount the volumes.
  2. Configure the task cap.
  3. (Optional) add the new node into the load balancer (if the node is hosting the Octopus UI).

Assuming the node is configured to process tasks, it should start picking up tasks to process within a few minutes.

Removing High Availability Nodes

Occasionally, you’ll want to delete a node. Perhaps you added two, three, or four nodes in anticipation of a large project involving hundreds of deployments, and now it is time to scale back down.

To do that, you’ll want to follow these steps:

  1. Configure the node to drain. This will finish all tasks and prevent any new ones from being picked up.
  2. Wait until any executing tasks on that node are complete.
  3. Remove the node from any load balancers.
  4. Delete server or Docker image.
  5. Remove the node from the nodes UI by clicking on ... next to the node name and selecting Delete.

Any task associated with the node will fail if you don’t wait for the node to finish draining and wrapping up any active tasks.

Auto Scaling High Availability Nodes

It is possible to use auto-scaling technology to add/remove high availability nodes. Adding a node is a lot easier than removing a node; assuming all the file shares are mounted, and the node can see the database, the node will come online and pick up work.

Removing the node will require a bit more planning. When the node is deleted by the auto-scaling technology, any tasks in process will fail. If you are in AWS or Azure, you can use a Lambda or Azure Function to:

  1. Call the Octopus API to drain the node and cancel any tasks. You’ll want to cancel the tasks as you’ll have a short time-frame to wait.
  2. Use the Octopus API to find any active tasks running on that node and cancel them.
  3. Use the Octopus API to remove the node.
  4. Resubmit any canceled deployments and runbook runs so a different node can pick them up.

Restarting The Server Host

If you are hosting Octopus Deploy on a Windows server, you will need to install regular Windows patches. To do that, follow these steps:

  1. Configure the node to drain. This will finish all tasks and prevent any new ones from being picked up.
  2. Wait until any executing tasks on that node are complete.
  3. Restart the server and wait for it to come back online.
  4. Remove the drain mode from the node.

Create a single production instance

One question we get asked a lot is “should we have a single instance to deploy to all environments or have an Octopus Deploy instance per environment?” Unless there is a business requirement, our recommendation is to have a single instance to deploy to all environments and use Octopus Deploy’s RBAC controls to manage permissions. We recommend this to avoid the maintenance overhead involved with having an instance per environment.

Of the customers who opt for an instance per environment, we see them have an instance for development and test environments with another instance for staging and production environments.

If you chose this instance configuration, you would need a process to:

  • Clone all the library variables and project variables, and notify you when a new scoped variable is added.
  • Sync the deployment and runbook processes, but skip over steps assigned to development and test.
  • Update any user step templates to the latest version.
  • Ensure the same lifecycle names exist on both instances but not have the same phases.
  • Copy releases but not deployments.
  • Clone all the project channels.
  • And more.

Using the Octopus Deploy API, all of that is possible; however, it will require diligence and maintenance on your part. Unless there is a specific business requirement, such as security team requirements or regulatory requirements, we don’t recommend taking that on.

Further reading

For further reading on installation requirements and guidelines for Octopus Deploy please see:

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Page updated on Wednesday, October 4, 2023