Dokploy vs Coolify vs CapRover in 2026: Which Self-Hosted PaaS Fits Your Stack?

The self-hosted PaaS space has matured significantly. Dokploy, Coolify, and CapRover each offer a path away from expensive managed platforms like Heroku, Render, and Railway — deploy your applications on your own infrastructure with a UI that handles builds, routing, SSL, and database management. But the three tools make different trade-offs, and most comparison articles skim over the details that actually affect your day-to-day experience.

This comparison is structured around the technical dimensions that matter when you're running real workloads: resource consumption, Docker Compose support, multi-server scaling, database management, monitoring, and community health. After each section, there's a note on what the comparison means for your server infrastructure, because the PaaS you choose directly affects how much hardware you need underneath it.

Quick Overview

Dokploy is the newest of the three, but it has grown rapidly — over 26,000 GitHub stars and an active release cadence. It's lightweight, Docker-native, and consumes roughly 0.8% idle CPU with around 350MB of RAM. Its architecture is built on Docker Swarm for orchestration and Traefik for reverse proxying. The UI is clean and functional without being overwhelming.

Coolify is the most feature-rich option, with a polished interface that feels closest to a commercial product. It supports more source integrations out of the box, has a built-in S3-compatible storage integration, and offers more granular deployment configuration. The trade-off is resource overhead: idle CPU consumption runs around 5-6%, and RAM usage starts higher at roughly 500-700MB before deploying any applications.

CapRover is the most mature, having been around since 2017 (originally as CaptainDuckDuck). It has a large library of one-click app templates and a proven track record. However, its Docker Compose support is limited, the UI is functional but dated, and the development pace has slowed compared to Dokploy and Coolify.

Installation and Resource Consumption

All three tools install via a single command or script, and all three require Docker. The installation experience is comparable — you'll be looking at a running dashboard within 5 minutes on any of them.

The difference is what happens after installation, before you deploy anything:

What this means for your server: Coolify's higher baseline consumption isn't a problem on a well-provisioned server, but it narrows your headroom on smaller instances. On a 2GB RAM server, Coolify at idle leaves you around 1.3-1.5GB for applications. Dokploy and CapRover leave closer to 1.6-1.7GB. The difference becomes material when you're running tight on RAM and need every megabyte for your database. With MassiveGRID's independent RAM scaling, you can add 2GB specifically for the PaaS overhead without touching your CPU allocation — but starting lighter means delaying that upgrade.

Docker Compose Support

This is where the three tools diverge most significantly, and it's the feature that most affects real-world usability.

Dokploy has native Docker Compose support. You can paste or reference a docker-compose.yml directly, and Dokploy manages the entire stack — multiple services, volumes, networks, and dependencies. This is critical if your application stack is already defined in Compose files, which is the case for most modern development workflows.

Coolify also supports Docker Compose, with similar capability. You can define multi-service stacks and Coolify handles the orchestration. The Compose support has matured considerably in recent versions and is broadly comparable to Dokploy's implementation.

CapRover has limited Docker Compose support. It primarily works with single-container deployments and one-click app templates. If your application consists of multiple services (app + database + cache + worker), you either deploy each as a separate CapRover app (losing the networking and dependency management of Compose) or maintain a custom solution outside CapRover's management. This is the tool's most significant limitation in 2026.

What this means for your server: Docker Compose stacks use shared networks, which means inter-service communication happens over internal DNS without port exposure. This is both more secure and more efficient than CapRover's approach of deploying services independently. From an infrastructure perspective, Compose-based deployments produce fewer exposed ports and less iptables complexity on your server.

Multi-Server and Scaling

Outgrowing a single server is the natural progression for any self-hosted setup. Here's how each tool handles it:

Dokploy uses Docker Swarm for multi-node deployments. You add worker nodes to the Swarm, and Dokploy can distribute services across them. Swarm handles service scheduling, rolling updates, and failure recovery. The implementation is straightforward — join a node to the Swarm, and it appears in Dokploy's server management UI. Swarm's gossip protocol requires low-latency connections between nodes, so keep all nodes in the same datacenter.

Coolify supports managing multiple remote servers from a single dashboard. Each server is independent — you deploy specific applications to specific servers. This is more of a multi-server management approach than a clustering approach. You decide where each application runs, rather than letting an orchestrator distribute workloads.

CapRover supports Docker Swarm clustering similar to Dokploy, with the ability to add worker nodes and distribute services. This has been a feature since CapRover's early days and is well-tested.

What this means for your server: Docker Swarm clustering (Dokploy/CapRover) works best when all nodes have consistent, predictable performance. A Swarm where one node is a shared VPS with variable CPU and another is a dedicated server creates uneven service performance. If you're planning multi-node, use MassiveGRID's Dedicated VPS (VDS) or Cloud Dedicated Servers for all nodes to ensure the scheduler distributes work across equally capable machines.

Database Management

All three tools can deploy databases as managed services alongside your applications:

Dokploy supports PostgreSQL, MySQL, MariaDB, MongoDB, and Redis as first-class database services with UI-based management, backup scheduling, and connection string generation. Databases run as Docker containers with persistent volumes.

Coolify offers the same database engines with a more polished configuration interface. It also includes built-in S3-compatible backup destinations, making it slightly easier to configure off-site backups without additional tooling.

CapRover handles databases through its one-click app template system. You can deploy PostgreSQL, MySQL, MongoDB, and others, but they're treated as regular applications rather than a distinct service category. Backup configuration is manual — you set up cron jobs and scripts yourself.

What this means for your server: Databases are the most storage-intensive and RAM-hungry services you'll run. PostgreSQL alone can consume 256MB-1GB depending on your shared_buffers configuration. The PaaS layer's overhead on top of this matters: Coolify's higher idle consumption means less RAM available for database buffer pools. Dokploy's lighter footprint leaves more headroom for PostgreSQL to cache query results. On a constrained server, this difference translates directly to query performance.

Monitoring and API/CLI

Dokploy includes built-in container monitoring (CPU, RAM, network) visible from the dashboard. It also provides a REST API for programmatic management and a CLI tool for interacting with your Dokploy instance from the terminal. The API makes it possible to integrate Dokploy into CI/CD pipelines beyond simple webhook-triggered deploys.

Coolify offers comprehensive monitoring through its UI, including container-level metrics and server-level resource graphs. The API is extensive and well-documented, supporting most operations available through the web interface. Coolify also integrates with external notification channels (Slack, Discord, email) for deployment and alert notifications.

CapRover has basic container monitoring and a CLI tool (caprover) for managing deployments from the command line. The API exists but is less documented than the other two. Notification integrations are limited compared to Coolify.

Community Size and Update Frequency

The health of an open-source project's community determines how quickly bugs are fixed, features are added, and security patches are released:

Coolify has the largest community and the most active development. Dokploy is growing fastest in terms of star velocity and contributor activity. CapRover is stable but development has slowed, which can be interpreted positively (mature, fewer bugs) or negatively (fewer new features, slower security response), depending on your needs.

The Honest Summary

Each tool has a clear strength:

• Dokploy is the best fit if you value lightweight resource consumption, native Docker Compose workflows, and Docker Swarm multi-node scaling. It's the most infrastructure-efficient option — you spend fewer server resources running the PaaS itself, leaving more for your applications. If you're cost-conscious or running on smaller servers, Dokploy's lower overhead translates to real savings.
• Coolify is the best fit if you want the most polished interface, the broadest feature set, and don't mind allocating additional resources to the platform layer. Its S3 backup integration, notification system, and multi-server management make it the most "batteries included" option. If your server has 8GB+ RAM and you value UI polish, Coolify is excellent.
• CapRover is the best fit if you need one-click app templates and have simple, single-container deployments. Its maturity means edge cases are well-understood, and the community has documented solutions for most common problems. If Docker Compose support isn't critical to your workflow, CapRover remains a solid choice.

None of these tools is objectively "the best." The right choice depends on your workload, your resource budget, and whether you value lightweight efficiency (Dokploy), feature richness (Coolify), or template-driven simplicity (CapRover).

The Infrastructure Underneath Matters More Than the PaaS

Here's the part most comparison articles leave out: a well-chosen PaaS on poorly matched infrastructure will underperform a mediocre PaaS on properly provisioned servers. All three tools are running Docker containers under the hood. The speed of your builds depends on CPU. The stability of your applications depends on RAM. The safety of your data depends on storage reliability. The availability of your services depends on the hardware failover capabilities of your host.

Regardless of which PaaS you choose, the infrastructure recommendations are the same:

• Start with a Cloud VPS for development, evaluation, and early production. MassiveGRID's independent resource scaling lets you start small (2 vCPU, 4GB RAM, 50GB SSD) and scale only what becomes constrained. Dokploy's lower overhead means you can run more on the same hardware, but all three tools work well at this tier.
• Graduate to a Dedicated VPS (VDS) when build consistency and runtime predictability matter. Dedicated CPU cores eliminate the noisy-neighbor problem that makes builds slow on shared infrastructure. This is the production-ready tier for single-server deployments with any PaaS.
• Move to Cloud Dedicated Servers when downtime is unacceptable. Automatic hardware failover, Ceph distributed storage with 3x replication, and a 100% uptime SLA. If you're running Docker Swarm multi-node (Dokploy or CapRover), this is the tier that ensures every node in your cluster has consistent, HA-backed infrastructure.

Getting Started

If you've decided on Dokploy, follow our step-by-step installation guide to go from a fresh server to a running Dokploy instance with your first deployed application. The guide covers server provisioning, firewall configuration, DNS, SSL, and first deployment.

If you're still evaluating infrastructure options, our breakdown of the best VPS for Dokploy covers CPU, RAM, storage, and reliability considerations with concrete numbers for each workload size. And for a deeper look at the shared vs. dedicated resource question, see our analysis of shared vs. dedicated resources for Dokploy workloads.

Whichever PaaS you choose, the pattern is the same: start with infrastructure that matches your current workload, scale the specific resources that become bottlenecks, and upgrade tiers only when you need dedicated cores or hardware-level failover. The PaaS layer is the deployment abstraction. The infrastructure beneath it is what determines reliability, performance, and cost.