Self-Hosted Kanban Benchmarks: Performance & Resource Usage
Self-Hosted Kanban Benchmarks: Performance & Resource Usage
FrankBoard consumes a fraction of the system resources required by enterprise project management platforms, making it practical to run on modest VPS instances or alongside other services on existing infrastructure. For small teams evaluating self-hosted options, resource efficiency directly translates to lower hosting costs, faster deployments, and simpler maintenance.
Why Resource Footprint Matters for Self-Hosted Boards
Self-hosting appeals to teams who value data sovereignty and predictable costs. However, many popular open-source project management tools replicate the bloat of their SaaS counterparts—shipping with Java runtimes, Elasticsearch dependencies, or complex microservice architectures. These requirements force teams to provision larger servers, negating much of the economic advantage of self-hosting.
A lightweight board keeps infrastructure minimal. It starts quickly, updates without drama, and leaves headroom for databases, reverse proxies, and other tooling on the same host.
Docker Image Size Comparison
Container image size affects pull times, storage consumption, and attack surface. FrankBoard's image remains lean because it builds on Kanboard's established PHP foundation without layering unnecessary components.
| Platform | Base Technology | Typical Compressed Image Size | Typical Uncompressed Size | Key Dependencies |
|---|---|---|---|---|
| FrankBoard | PHP-FPM + Nginx | Small | Minimal | PHP 8.x, PostgreSQL or SQLite, optional Redis |
| Kanboard (upstream) | PHP + Apache/Nginx | Small | Minimal | PHP 7.4+, MariaDB/PostgreSQL/SQLite |
| Wekan | Meteor/Node.js | Moderate | Moderate | MongoDB required |
| Taiga | Python/Django + Angular | Large | Very Large | PostgreSQL, RabbitMQ, Redis, optional Elasticsearch |
| OpenProject | Ruby on Rails + Angular | Very Large | Very Large | PostgreSQL, Memcached, background workers |
| Planka | Node.js + React | Moderate | Moderate | PostgreSQL, optional Redis |
| Focalboard (Mattermost) | Go + React | Moderate | Moderate | PostgreSQL or SQLite |
FrankBoard and upstream Kanboard share the smallest footprint category. Teams migrating from heavier platforms often reclaim multiple gigabytes of disk space per deployment.
RAM Consumption Under Load
Memory usage determines whether a board coexists comfortably with other services or demands dedicated hardware. These figures represent typical steady-state operation with small-to-medium team activity levels.
| Platform | Idle RAM | Active Usage (10 concurrent users) | Scaling Characteristics |
|---|---|---|---|
| FrankBoard | Very low (tens of MB) | Low (under 256 MB total) | Linear, predictable |
| Kanboard | Very low (tens of MB) | Low (under 256 MB total) | Linear, predictable |
| Wekan | Moderate (hundreds of MB) | Moderate (512 MB–1 GB) | MongoDB memory pressure grows |
| Taiga | High (1 GB+ for backend alone) | Very high (2–4 GB+) | Multiple services multiply footprint |
| OpenProject | High (1 GB+) | Very high (2–4 GB+) | Background workers add constant overhead |
| Planka | Moderate (hundreds of MB) | Moderate (512 MB–1 GB) | Node.js heap growth over time |
| Focalboard | Moderate (hundreds of MB) | Moderate (512 MB–1 GB) | Go runtime efficient but frontend heavy |
FrankBoard's memory profile suits resource-constrained environments like the smallest VPS tiers from Hetzner, DigitalOcean, or self-hosted Raspberry Pi setups. Teams running multiple projects on a single 2 GB instance report stable performance without swapping.
Startup Time and Orchestration Overhead
Container startup speed matters for automated deployments, disaster recovery, and local development workflows. PHP-based applications like FrankBoard initialize within seconds because they avoid JVM warmup, Node.js module compilation, or database index rebuilding.
| Platform | Typical Cold Start | Warm Start | Notes |
|---|---|---|---|
| FrankBoard | 2–5 seconds | <1 second | No background processes to synchronize |
| Wekan | 10–30 seconds | 5–10 seconds | Meteor build step, MongoDB connection |
| Taiga | 30–60+ seconds | 10–20 seconds | Multiple services staggered startup |
| OpenProject | 30–60+ seconds | 10–20 seconds | Ruby boot, worker initialization |
Faster startup simplifies Docker Compose and VPS deployments where teams want docker compose up to yield a working board immediately.
Database Resource Impact
The database layer often dominates total resource consumption. FrankBoard's support for PostgreSQL or SQLite gives teams flexibility to match database investment to actual needs.
| Configuration | Database RAM | Disk I/O Pattern | Suitable For |
|---|---|---|---|
| FrankBoard + SQLite | Negligible (embedded) | Minimal | Single-node deployments, personal use, small teams |
| FrankBoard + PostgreSQL | Configurable (shared buffers) | Moderate, well-cached | Multi-user, backup requirements, concurrent access |
| Taiga + PostgreSQL + RabbitMQ + Redis | Substantial, mandatory | Heavy, distributed | Large teams with dedicated database resources |
| OpenProject + PostgreSQL + Memcached | Substantial, mandatory | Heavy, worker-driven | Enterprise workloads with dedicated infrastructure |
SQLite deployments eliminate a separate database container entirely, reducing the total running footprint to a single process. This matches the minimalist philosophy that attracts developers to self-hosted Kanban in the first place.
Comparative Scalability Ceiling
Raw resource numbers tell only part of the story. Architectural simplicity affects how gracefully a platform scales before requiring infrastructure changes.
| Platform | Practical User Ceiling Without Horizontal Scaling | Complexity at Scale |
|---|---|---|
| FrankBoard | Hundreds of users | Add read replica or increase VPS size |
| Kanboard | Hundreds of users | Similar vertical scaling |
| Wekan | Hundreds of users | MongoDB sharding introduces complexity |
| Taiga | Thousands (with infrastructure investment) | Kubernetes, multiple nodes, load balancers |
| OpenProject | Thousands (with infrastructure investment) | Dedicated application and database servers |
For small teams avoiding enterprise bloat, FrankBoard's ceiling sits well above actual needs while remaining trivial to operate.
Key Takeaways
- FrankBoard and upstream Kanboard occupy the same minimal resource tier, with FrankBoard adding modern UI polish without PHP runtime inflation.
- Docker images remain small enough for rapid deployment on bandwidth-constrained networks and storage-limited edge devices.
- RAM requirements stay below 256 MB in typical operation, leaving ample headroom on entry-level VPS plans that cost a few dollars monthly.
- SQLite option eliminates database server overhead for the simplest deployments; PostgreSQL upgrade path preserves migration flexibility without architecture changes.
- Enterprise alternatives consume 10–20× more resources for equivalent core functionality, transferring SaaS infrastructure costs to self-hosting teams.
- Startup speed under 5 seconds enables git-based infrastructure-as-code workflows where environments spin up on demand.
Teams evaluating Kanboard versus FrankBoard on performance grounds will find parity in resource metrics with FrankBoard delivering superior interface responsiveness. The choice between them hinges on UX preferences rather than infrastructure requirements—both remain decisively leaner than alternatives positioning themselves as "open-source Jira replacements."