Confluence vs Notion vs Slab: Best SaaS docs tools for engineering

Introduction

Engineering orgs rarely fail because they lacked documentation tools. They fail because the docs system becomes a junk drawer.

At 10 people, you can get away with tribal knowledge and a few pages. At 50, onboarding turns into a scavenger hunt. At 200, documentation becomes an operational risk. It slows incident response, creates architecture drift, and makes hiring harder than it needs to be.

This piece compares Confluence vs Notion vs Slab through a CTO lens. Not feature checklists. The stuff that hurts in production: scale, governance, security, and the cost of keeping docs alive.

What we will cover:

• How each tool behaves when the wiki grows from hundreds to tens of thousands of pages
• Where teams lose time (search, permissions, duplication, review cycles)
• What to measure so the decision is not based on vibes
• How to roll out documentation without breaking delivery velocity

Insight: Documentation is part of your engineering system. Treat it like infrastructure, not like a side project.

A quick note on examples. In our work at Apptension, we have built products where knowledge management was the bottleneck, not the code. The Mobegí project is one of those. We built a secure AI knowledge assistant in 12 weeks because internal teams were drowning in repetitive questions and scattered docs. The tool choice mattered, but the information architecture and governance mattered more.

Who this is for

This is for Heads of Engineering and CTOs who:

• Are scaling teams and need predictable onboarding
• Have multiple squads shipping in parallel and want fewer architectural surprises
• Need audit friendly access control and retention
• Have budget pressure and want a defensible ROI story

Why documentation tools break at scale

Most orgs do not have a documentation problem. They have a lifecycle problem.

Docs start as helpful notes. Then they become a dependency. Then they become a liability.

Common failure modes we see:

• Search returns 12 near duplicates and the newest one is wrong
• Permissions are either too open (risk) or too locked (workarounds)
• Pages do not have owners, so nobody updates them
• Decision records live in chat, then disappear
• Onboarding relies on a senior engineer doing the same walkthrough again

Key Stat (hypothesis): If a new engineer spends 30 minutes per day searching for “the right doc,” that is 10 hours per month per person. Multiply by headcount and you get a real budget line. Measure it.

Scalability is not just page count

When people say “our wiki is slow,” they usually mean one of these:

• Information architecture does not map to how teams work
• Search cannot rank what matters (recency, ownership, canonical sources)
• Permissions create fragmentation (private copies, shadow docs)
• Review workflows are missing, so staleness grows quietly

The talent angle: onboarding and retention

Documentation is a hiring tool even if you never show it to candidates.

• Strong docs reduce time to first meaningful PR
• Clear runbooks reduce on call anxiety
• Visible decision history reduces politics and rework

Here is what to track if you want to tie docs to team management:

• Time to first production deploy for new hires
• Number of onboarding questions per new hire per week (Slack, tickets)
• On call escalations due to missing runbooks
• Incidents where the root cause was “we did not know this dependency existed”

Confluence vs Notion vs Slab: the comparison that matters

If you only compare features, you will end up with the wrong tool.

The better question: what is your operating model?

• If you run in an Atlassian world (Jira, Opsgenie, Bitbucket), Confluence often wins on integration and governance.
• If you want a single flexible workspace where product and engineering co author docs, Notion is hard to ignore.
• If you want a knowledge base that stays opinionated and clean, Slab tends to produce less mess.

Below is a practical comparison. Your mileage will vary, so treat it as a starting point and validate with a pilot.

Insight: The tool does not create governance. It only makes your governance easier or harder to enforce.

How each tool behaves under real engineering load

Here is the blunt version.

Confluence tends to work when:

• You need strict spaces, permissions, and audit trails
• You can invest in templates and page ownership
• You want Jira links, incident pages, and change logs to be first class

Confluence tends to fail when:

• Nobody owns information architecture
• Teams create new spaces for every initiative and never clean them up
• Permissions are used as a substitute for clarity (private docs everywhere)

Notion tends to work when:

• Product, design, and engineering share one workspace
• You want docs plus lightweight tracking (decision logs, service catalogs, onboarding checklists)
• You can enforce conventions (naming, owners, review dates)

Notion tends to fail when:

• Every team invents their own layout and taxonomy
• You rely on databases for everything and lose readability
• You cannot control duplication and “personal copies”

Slab tends to work when:

• You want a knowledge base, not a second project management system
• You care about clean navigation and fewer degrees of freedom
• You want docs to feel curated, not dumped

Slab tends to fail when:

• You need deep enterprise workflows and complex permission models
• You expect it to replace a broader collaboration suite

Security and compliance at scale

This is where CTOs get burned, because the risk shows up late.

Consider:

• SSO and SCIM provisioning
• Granular access controls and audit logs
• Data residency requirements
• Retention policies and legal holds
• Vendor risk management and incident response posture

Key Stat (observation): The most common compliance failure is not “the vendor got hacked.” It is “an ex employee still had access” or “a contractor saw something they should not.” SCIM and deprovisioning discipline matter more than fancy features.

A decision framework that survives budget reviews

If the decision needs CFO approval, you need a model. Not a preference.

Start with a simple ROI hypothesis. Then measure.

What to measure in a 30 day pilot

Pick one team. Pick one doc category. Run a controlled rollout.

Measure:

• Median time to find a runbook during an incident simulation
• New hire time to complete onboarding checklist
• Number of repeated questions in Slack for the same topic
• Percentage of pages with an owner and review date
• Search success rate (did the first result solve the task?)

Here is a lightweight way to capture search success in a form:

Question: Were you able to complete the task with the first doc you opened? Options: Yes / No If No: What did you do next? (ask in Slack, open another doc, guess)

Insight: If you cannot measure whether docs help, you will end up measuring page count. That metric will lie to you.

Budget constraints: where costs actually come from

License cost is the easy part. The hidden cost is admin and cleanup time.

Common cost drivers:

• Time spent maintaining templates, permissions, and spaces
• Time lost to duplication and outdated docs
• Migration costs when you switch tools later
• Security reviews and compliance work

If you want a number to take to finance, use this structure:

1. Estimate hours lost per engineer per week to doc friction (search, missing runbooks, rework)
2. Multiply by fully loaded hourly cost
3. Compare against license plus admin time

Call it a hypothesis until you measure it. But put it on paper.

Architecture decisions: docs as part of the platform

At scale, documentation is coupled to your architecture.

• Microservices and event driven systems increase the need for service ownership docs, contracts, and dependency maps
• Zero trust security increases the need for clear access patterns, secrets handling, and runbooks
• Hybrid cloud increases the need for environment specific operational docs

This is the part many teams miss: the more modular your system gets, the more modular your documentation must be.

A simple mapping from org shape to tool choice

• Strong governance needs, regulated environment, Atlassian stack: Confluence is often the least risky default.
• Fast moving product org, lots of cross functional docs, preference for flexible structure: Notion can work, but only with rules.
• Engineering org that wants a clean knowledge base and fewer knobs: Slab is worth serious consideration.

Implementation strategies that keep docs alive

Tool selection is step zero. Adoption is the hard part.

A rollout plan that does not stall delivery

Here is a rollout sequence we have seen work without derailing roadmaps:

1. Define doc types and owners (runbooks, ADRs, onboarding, service docs)
2. Create 5 to 10 templates and ban free form pages for core categories
3. Migrate only what is actively used (based on page views or team votes)
4. Add review dates and ownership to every canonical page
5. Tie docs to workflows (PR templates, incident retros, RFC process)
6. Run a monthly cleanup hour per team (delete, merge, mark canonical)

What not to do:

• Do not migrate everything “just in case.” You will import the mess.
• Do not rely on a single documentation champion. That person will quit or burn out.

Example: In the Miraflora Wagyu project, the team was spread from Hawaii to Germany. Async communication was not optional. Clear written decisions and lightweight handoffs were the difference between shipping in 4 weeks and slipping. The tooling mattered less than the habit.

Guardrails that reduce entropy

Pick a few rules and enforce them.

• Every page has an owner (a person, not a team)
• Every operational doc has a last verified date
• Every system has a “start here” page
• ADRs are required for irreversible decisions
• Runbooks are required for any service that can page someone

Using AI responsibly: retrieval beats guessing

Many teams want an AI assistant on top of docs. Reasonable. But the failure mode is ugly: confident wrong answers.

In our experience building Mobegí, a secure AI driven chatbot for internal knowledge management in 12 weeks, the hard problems were:

• Getting answers grounded in the right source docs, not generic completions
• Handling permissions so the assistant never leaks restricted information
• Keeping the knowledge base fresh so answers do not drift

Mitigations that actually help:

• Use retrieval with citations and show the source snippet
• Enforce permission checks at retrieval time
• Track unanswered questions and turn them into documentation work

Insight: The fastest way to lose trust in your knowledge base is an assistant that answers quickly and incorrectly. Make it cite sources or do not ship it.

Conclusion

Confluence vs Notion vs Slab is not a religion. It is an operating decision.

If you need governance, auditability, and tight integration with Jira, Confluence is usually the safe bet. If you need flexible cross functional collaboration, Notion can work, but only with strong conventions. If you want a focused knowledge base that stays readable, Slab is often easier to keep clean.

Next steps you can take this week:

• Pick one doc category to standardize first (runbooks usually pay back fastest)
• Write 5 templates and enforce them
• Run a 30 day pilot with measurable outcomes
• Decide based on metrics, not page count

A final checklist to sanity check your choice:

• Can a new hire find the onboarding path in under 2 minutes?
• Can an on call engineer find the right runbook in under 30 seconds?
• Can you deprovision access in under 24 hours with SCIM?
• Do you have a way to detect stale docs before they hurt you?

Insight: The best documentation tool is the one your org can govern without heroics.