Five headless CMS options sit in your browser tabs. All have 10k+ stars. All are actively maintained. All have Docker deployments and comprehensive documentation. Their feature matrices look equivalent. GitHub activity shows regular commits. Community engagement appears strong. Yet one of these will prove to be the right choice for your architecture, and four won’t. The difference costs months of migration work when discovered too late.
This is the fundamental challenge in open-source evaluation: popularity metrics and surface-level indicators don’t distinguish between genuinely good fits and technically competent projects that don’t meet your needs. Stars measure awareness, not suitability. Commit frequency indicates activity, not alignment with your architecture. The existence of documentation doesn’t guarantee it covers your use case. The question isn’t which project is objectively best—it’s which project is best for your specific context, constraints, and requirements.
The solution requires a systematic evaluation framework that combines quantitative filtering with qualitative assessment. This article presents a three-factor decision process: health score filtering (will it be maintained?); technical score evaluation (can you work with it?); and use case fit analysis (does it solve your problem?). Each factor serves a distinct purpose, narrowing the candidate pool from dozens to the optimal choice.
The Decision Process: Filter, Then Evaluate
The framework operates in two phases. First, quantitative filtering eliminates projects that won’t work regardless of feature fit. Second, qualitative evaluation ranks remaining candidates by how well they address your specific requirements. This approach prevents time spent evaluating features in projects that will be abandoned next year or in codebases you can’t modify when needed.
Phase 1: Quantitative Filtering
Apply health and technical thresholds to quickly eliminate unsuitable projects. For production systems, filter for health scores above 75 and technical scores above 70 if customization is likely. For internal tools or low-risk deployments, health above 60 suffices. This removes abandoned projects, declining communities, and codebases with excessive technical debt before you invest time in detailed evaluation.
Phase 2: Qualitative Evaluation
Among projects that pass filtering, evaluate architectural match, feature coverage, deployment complexity, and integration requirements. Build a comparison matrix and score each candidate against your specific criteria. Test the top 2-3 options with realistic workloads. Choose based on demonstrated fit, not abstract superiority.
The key insight: health and technical scores are binary filters (pass/fail), whereas use case fit is a continuous metric (degrees of match). Don’t spend time evaluating use case fit for projects that will fail health or technical thresholds.
Step-by-Step Evaluation Process
Step 1: Define requirements explicitly.
Document must-have features, expected scale (users, requests, data volume), team capabilities (languages, frameworks, devops experience), and deployment constraints (cloud, on-premise, air-gapped). Specify numbers: “supports 50,000 concurrent users” rather than “scalable,” “handles 10TB datasets” rather than “performant.” Vague requirements produce vague evaluation results.
Step 2: Generate candidates.
Search by category and filter for health scores above your threshold (75+ for production, 60+ for lower-risk scenarios). Read taglines to eliminate obvious mismatches. You should have 5-8 viable candidates after this initial filter. Don’t evaluate dozens—the goal is a manageable shortlist of plausible options.
Step 3: Apply technical threshold if relevant.
If your team will customize, extend, or contribute code, eliminate projects with a technical score below 60-70. If you’re using the tool strictly through its API or admin interface without modifying the code, skip this filter. Technical scores matter when you interact with the codebase, not when you interact with the product.
Step 4: Review the documentation for the remaining candidates.
Spend 5-10 minutes per project reading the README and scanning architecture docs. Verify feature coverage, review deployment models, and identify dealbreakers (e.g., incompatible database, missing integrations, incompatible licensing, architectural mismatches). This reduces the list to 2-4 serious contenders.
Step 5: Build a comparison matrix.
Create a table with candidates as rows and your specific requirements as columns. Score each project on your criteria using a consistent scale (0-10 or low/medium/high). This makes trade-offs explicit and prevents picking based on whichever feature you most recently evaluated. Weight scores by importance if some requirements matter more than others.
Step 6: Test top candidates.
Deploy the top 2-3 projects using Docker and run through your key use cases with realistic data. This reveals usability issues, performance characteristics, and integration friction that documentation doesn’t capture. Allocate 2-4 hours per project for meaningful testing. Does the admin UI match your workflow? Are APIs intuitive? Does it handle your data model cleanly?
Step 7: Make the decision.
Choose the project that scores highest on your weighted criteria matrix, validated by hands-on testing. There is no universal “best”—only the best fit for your context. Document your decision reasoning for future reference when questions arise about why you chose this option.
When Health Scores Matter
Health scores measure maintenance consistency and community engagement. They answer a single question: Will this project exist and receive updates in 12-24 months? This matters for production deployments that require security patches, bug fixes, and compatibility updates. It matters less for internal tools, proofs of concept, or projects where you’re willing to fork and maintain them independently.
Consider Ghost at health 96 versus a hypothetical CMS at health 45. Both might have equivalent features today. But Ghost’s score reflects years of consistent releases, active issue triage, regular commits, and community growth. The 45-score project might have great code, but its maintenance pattern suggests risk: declining commit frequency, accumulating unanswered issues, or signs of maintainer burnout.
Health score components include:
- Community engagement: Stars, watchers, forks, contributor count, discussion activity
- Development consistency: Commit frequency, time since last commit, contributor diversity
- Maintenance responsiveness: Issue response time, PR merge rate, release cadence
- Project maturity: Age, version number, breaking change frequency
- Growth trajectory: Star velocity, contributor growth, community expansion
For production systems, the threshold is at 75+. Mattermost (96), Ghost (96), and Directus (92) all exceed this bar, indicating proven maintenance track records and resilient communities. Even if individual maintainers leave, these projects have enough organizational structure and contributor depth to continue.
For side projects, internal tools, or deployments where migration risk is acceptable, scores between 60 and 75 work. Plane at 87 provides solid maintenance without the maturity depth of decade-old projects. Below 60 requires investigation: is this newly launched with promise, or declining with abandonment risk? Check recent commit patterns to distinguish.
Health scores don’t evaluate features, architecture, or code quality. They strictly assess maintenance likelihood. This makes them effective filters but poor final decision criteria.
When Technical Scores Matter
Technical scores evaluate code quality, architecture, testing infrastructure, and documentation. They answer: Can your team work with this codebase if you need to extend functionality, fix bugs, contribute features, or debug production issues? This question matters a great deal if you plan to customize. It matters little whether you use the tool exclusively through its interface.
Consider Payload CMS at technical 80.8 versus a project at 55. Both might solve your immediate CMS needs. But Payload’s score indicates clean separation of concerns, comprehensive test coverage (100 code-quality subscore), well-documented APIs, and a maintainable architecture (95 architecture subscore). The 55-score project might have working features, but technical debt that makes modifications risky: poor test coverage, complex interdependencies, or undocumented architectural decisions.
Technical score components include:
- Code quality: Test coverage, linting configuration, CI pipeline maturity, code review practices
- Architecture: Modular structure, separation of concerns, design pattern consistency, complexity management
- Documentation: Inline comments, type annotations, API documentation, architecture diagrams
- Complexity: Codebase size, dependency count, file organization clarity, learning curve assessment
When technical scores matter most:
- Heavy customization planned: If you’re building custom features or modifying core behavior, prioritize 70+ technical scores. Directus (77 overall, 95 architecture, 91 code quality) and Strapi (76 overall, 81 architecture, 90 code quality) offer codebases you can modify with confidence.
- Contributing upstream: If you plan to contribute fixes or features back to the project, high technical scores reduce friction. Clean code with good tests makes contributions easier to write and more likely to be accepted.
- Long-term maintenance: If you might fork and maintain independently, technical scores indicate how painful that maintenance will be.
When technical scores matter less:
- Interface-only usage: If you interact exclusively through admin UIs, APIs, or configuration files without touching code, technical scores become secondary to feature completeness and API design. Ghost at 70.8 technical works fine for publishers who won’t modify internals.
- Vendor-supported deployments: If you’re using hosted/managed versions, vendor support handles code-level issues.
- Short-term projects: If the deployment is temporary or experimental, codebase quality matters less than rapid feature validation.
The technical scores filter projects by whether your team can work with them. They don’t indicate which project best solves your problem, only whether you can modify it as needed.
Use Case Fit: The Actual Decision
Health and technical scores eliminate unsuitable projects. Use case fit determines the winner among viable options. This evaluation is specific to your architecture, workflow, team capabilities, and requirements. There is no universal ranking—only context-specific fit.
Architectural alignment matters most. Are you building API-first systems? Prioritize headless CMS architectures such as Strapi and Directus, which are designed for programmatic access. Need traditional publishing with themes? Ghost optimizes for content-first workflows. Want code-based configuration? Payload CMS uses TypeScript config files instead of admin UI schema builders. The architecture should match how you think about the problem.
Feature coverage follows architecture. Does the project handle your data model? Support required content types? Provide needed APIs? Include essential integrations? Build a checklist of your top 10 requirements and score each project against them. Missing features matter differently: core gaps are dealbreakers, peripheral gaps might be acceptable, and features you can build through extensions indicate customization opportunities.
Deployment complexity affects operational burden. Does the project support your infrastructure (Docker, Kubernetes, bare metal)? Require compatible databases (PostgreSQL, MySQL, MongoDB)? Integrate with your auth system? Scale to your traffic patterns? Some projects deploy in minutes with Docker Compose. Others require extensive configuration. Match deployment complexity to your team’s operational capacity.
Integration requirements determine effort. Does the tool work with your existing stack? Provide webhooks for event streaming? Support your authentication provider? Offer APIs that your other systems can consume? Integration friction compounds over time. A project with poor integration support repeatedly costs the developer hours.
Team familiarity influences productivity. Is the project written in languages your team knows? Use frameworks they’ve worked with? Follow patterns they understand? Alignment with existing expertise reduces ramp-up time and ongoing maintenance burden. A technically superior project in an unfamiliar language might deliver value more slowly than a good-enough project your team can immediately work with.
Evaluate use case fit through:
- Comparison matrix: Score each project 0-10 on your specific requirements, weight by importance, and calculate totals
- Hands-on testing: Deploy top candidates, run realistic workflows, measure actual performance
- Integration prototypes: Test critical integrations to verify compatibility and identify friction
- Team feedback: Have developers who’ll maintain the project evaluate codebase familiarity
The highest scoring project on use case fit, validated by hands-on testing, becomes your choice—assuming it passed health and technical thresholds.
Making the Decision
The three-factor framework structures decisions but doesn’t eliminate judgment. Two projects might score identically on quantitative metrics while differing significantly on qualitative fit. Three considerations guide final decisions when scores are close:
Reversibility: How difficult is migration if this proves wrong? Projects with a clear separation between content and presentation, standard data formats, and robust export tools reduce switching costs. This matters when choosing between closely matched options—pick the one that’s easier to migrate away from if needed.
Momentum: Which direction is the project heading? A score of 75 with improving metrics (rising commit frequency, growing community, increasing adoption) may be safer than 85 with declining trends. Look at trajectories, not just the current state.
Community fit: Does the project’s governance model, contribution process, and community culture align with how your team operates? Open, responsive communities that welcome contributions differ from projects with opaque decision-making. This affects your ability to influence the project’s direction when your needs diverge from maintainers’ priorities.
Document your decision with:
- Evaluation matrix: Scores for each candidate on each criterion
- Testing results: Performance data, integration outcomes, usability observations
- Key trade-offs: What you gained and what you gave up with this choice
- Review timeline: When to reassess if circumstances change
Six months later, when someone questions the choice, your documented reasoning explains the decision context and prevents revisiting debates based on information that wasn’t available or factors that weren’t prioritized.
Beyond the Numbers
Quantitative score filters are efficient, but qualitative judgment makes final decisions. The framework prevents common mistakes: picking based on stars alone, choosing familiar tools by default without evaluation, or selecting based on whichever factor you most recently considered. It doesn’t eliminate thinking—it structures it.
The best tool isn’t the highest-scoring project. It’s the project that passes your quality thresholds (health, technical) and best fits your specific context (architecture, requirements, team capabilities). That fit determines whether the project succeeds in production or creates months of friction when reality diverges from expectations.
Start with health filtering to eliminate maintenance risks. Apply technical filtering if you need to work with the code. Evaluate use case fit among remaining candidates with hands-on testing. Choose based on demonstrated fit to your requirements, not abstract superiority. And document why, because you’ll need to explain the reasoning when someone inevitably asks.