DigitalOcean Droplets are Linux-based virtual machines (VMs) that run on top of virtualized hardware. Each Droplet you create is a new server you can use, either standalone or as part of a larger, cloud-based infrastructure.
Some Droplet network traffic is restricted to help prevent malicious actions, like reflected DDoS attacks. We know these restrictions also prevent functionality like configuring direct server return and using Droplets as routers and site-to-site VPN gateways. Future changes to our network may support this functionality. Until then, some workarounds include using a VPN mesh network or overlay network.
The following types of traffic are restricted:
Multicast traffic.
Traffic not matching a Droplet’s IP address/MAC address.
SMTP via Reserved IPs and IPv6.
Droplets with Premium CPUs have a network throughput limit of 10 Gbps. All other Droplets have a maximum network throughput limit of 2 Gbps.
You can’t create more than 10 Droplets at the same time using the control panel or the API.
SMTP port 25 is blocked on all Droplets for new accounts. As an alternative, we recommend using a dedicated email deliverability platform, like SendGrid, and generally recommend against running your own mail server.
/proc/cpuinfo shows your Droplet plan, either DO-Premium or DO-Regular. You can see which processors each plan uses in Choosing the Right Droplet Plan.
Root password resets are not available for operating systems with internally-managed passwords, including Fedora.
Droplets cannot have more than one Reserved IP address assigned to them at a time.
You cannot resize from an 8 GPU Droplet to a single GPU Droplet.
Non-GPU Droplets do not support jumbo frames, so GPU and non-GPU Droplets can only communicate over the VPC using the standard 1,500 byte MTU size.
To create multi-node GPU Droplets, you need to contact support to enable that functionality for your team.
You cannot resize GPU Droplets from the control panel. Instead, use the API’s Droplet action endpoint or the CLI with doctl compute droplet-action resize
The control panel currently doesn’t display network transfer for GPU Droplets, but does display other plan details.
Powering GPU Droplets on and off from the control panel may be slow, especially for 8 GPU Droplets. We recommend using soft reboots from the command line (reboot or shutdown --reboot), the API’s reboot action, or doctl compute droplet-action reboot instead.
When using the AI/ML-ready image on 8 GPU Droplets, journald does not automatically start on boot. To enable journald logs, restart the service with sudo systemctl restart systemd-journald.