10 Ratios to Help Design Your Tech Team

Optimize Your Engineering Team Using Best Practice Org Design Ratios

👋 Hello Technocrats!

I wrote a version of this last year. This is a refined article and includes additional learning factoring in AI. The core concept is the same: tech leaders can design better organizations by using best-practice ratios 🎯.

Cheers & let’s dive in, 🦈

Bobby

We’ve all heard the phrase, “you ship your org structure” and it’s true.

The way you structure your team matters a ton 🏋️.

But designing an effective engineering org is harder than it looks, even for top-tier leaders.

And the larger the organization the more difficult it is to get right.

As tech execs rise organizational design becomes an increasingly important skill.

Today, we’ll develop that skill through understanding best practice organizational design ratios.

Ratios between teams, roles and other factors are a powerful tool for both evaluating and designing engineering organizations because they provide quantifiable benchmarks for how teams should be structured.

Most average technology leaders don’t use them though. So this is just for the A players out there.

Caveat

I’ve found a lot of technology leaders either don’t worry about nailing down the right organizational structure or just rely on their instincts.

Either way it’s a risky approach if you want high performance.

Top CTOs and technology leaders are constantly & deliberately optimizing their org design to maximize performance.

In fact, I’d go as far as saying that org design is always happening as tech leaders hire, fire, create roles, teams, and so forth.

So you might as well do it right!

10 Best Practice Ratios

Ratios add a layer of efficacy which enables you to define the best possible engineering team with a given set of resources.

Let’s dive into them:

1. Managers vs. Teams

• 2 to 5 teams per manager is ideal, depending on complexity and maturity.

• Established teams with strong leads need less oversight, allowing managers to handle more teams.

• If teams are tackling highly complex or critical projects, managers need more focus and fewer teams.

• Seasoned managers can handle more teams, while new managers might need a lighter load to focus on learning and building trust.

• Red flags: managers overwhelmed by administrative tasks who are unable to provide strategic guidance, and teams reporting low morale or lack of direction.

2. Team Leads vs. Individual Contributors

• 1 lead for every 5 to 9 ICs is a common sweet spot.

• If teams are too large, leads can’t effectively mentor, provide feedback, or oversee work quality. This can lead to disengaged ICs and missed deliverables.

• If teams grow beyond 10 ICs, split them into smaller groups and promote high-performing ICs into team lead roles.

• Equip team leads with tools and simplify processes to reduce administrative burdens and maximize their focus on team guidance.

3. Developers vs. QA’s

• Early-stage startups may have 0 QAs and that’s OK, depending on the goals of the business.

• More mature product lines may have 1 QA for every 7 developers and that’s OK too if things are leaning more towards maintenance mode.

• If your products are in a quality-focused industry like Health Care / Life Sciences then1:1 ratios for maximum quality is OK too.

• Imbalanced ratios can delay releases or lead to higher defect rates post-launch.

• QA plays a key role in scaling development velocity without sacrificing product stability.

• Too few QAs results in bug accumulation and over-reliance on developers for testing, while too many QAs may lead to underutilization and higher costs without ROI.

4. Project Managers vs. Projects

• It’s not uncommon to see 1 PM for every 3 to 5 projects; of course this will vary based on size and complexity of projects.

• However, I wouldn’t go past 4 or 5 projects that is just too much for almost anyone to manage. Even 5 is doing A LOT of project management.

• If you need additional PM bandwidth on a huge, cross-functional project consider additional PMs and vary them by seniority.

• Projects with heavy interdependencies between teams demand closer monitoring and coordination so this will also be a factor.

• Don’t stretch PMs too thin—this leads to delays, poorly communicated goals, and higher risk of scope creep.

• Use modern tools like Agile boards to help PMs stay organized and efficient.

5. Bug Tickets vs. Developers

• There’s no universal rule but if 1 developer is fixing more than 7 to 10 moderately difficult bugs a week you will see burn-out pretty quickly.

• If a developer is fixing 20 to 30 “easy” bugs every week that is reasonable. But remember that bug fixing is not something devs like doing forever.

• Too many bugs per developer signals mounting tech debt or unstable codebases, which can demotivate teams and delay product roadmaps.

• But too few bugs isn’t necessarily a good thing either. It often signals gaps in your testing or user feedback…either your pipelines aren’t rigorous enough to catch real issues, or your customers simply aren’t exercising key features, leaving you blind to the bugs that really matter.

• Monitor this ratio regularly and adjust resources or priorities as needed.

• Schedule bug sprints or allocate dedicated “bug-fix days” to keep backlogs manageable.

6. CloudOps Engineers vs. Servers

• 1 CloudOps engineer for every 20 to 40 servers in moderately automated & complex environments is fairly good.

• In highly automated ecosystems this ratio can stretch to 1 to 75 or even 1 to 100.

• A well-balanced ratio ensures infrastructure uptime, scalability, and incident response efficiency.

• Red flags include frequent outages, missed SLAs, or delayed provisioning.

• Over-resourcing leads to idle staff and unnecessary costs.

• Invest in automation tools (e.g., Terraform, Kubernetes) to maximize the scalability of your CloudOps team as your business grows.

7. CloudOps Engineers vs. Users

• High-traffic environments can see 1 engineer for every 100K users, but it’s highly dependent on the platform & nature of the usage.

• Low-traffic platforms may be fine with 1 engineer for every 250K users.

• Rising user activity often brings unforeseen infrastructure challenges, such as latency, resource exhaustion, or scaling issues.

• Pair this ratio with monitoring tools like New Relic or Data Dog to proactively manage usage spikes.

• Plan ahead for scaling needs during seasonal or marketing-driven surges.

8. Help Desk Techs vs. Internal Staff

• 1 technician for every 100 staff members is ideal.

• Certain organizational cultures where staff is highly technically experienced & things are automated this number can go to 1:150 or 1:200.

• Use ticket management tools like Zendesk or Jira Service Desk to streamline workflows & help scale.

• Introduce self-service solutions, such as AI chatbot assistance, to reduce ticket volume.

9. Help Desk Techs vs. Staff Devices

• 1 technician for every 150 to 200 devices (laptops, etc) is fairly reasonable.

• With remote work a part of life, device management changes quite a bit.

• Each employee typically has multiple devices, doubling or tripling the potential device workload for Help Desk teams.

• Use device management platforms (e.g., Jamf, Intune) to automate updates and troubleshooting.

• Regularly audit device inventories to prevent resource bottlenecks.

10. AI Engineers vs. Data Scientists

• If you’re building ML models for deployment 1 AI Engineer : 2–4 Data Scientists is a solid starting point, balancing experimentation with production readiness.

• Early‐stage AI orgs often run 1:5 while prototyping; as you move models into production, tighten to 1:2–3 so pipelines don’t stall.

• If data scientists vastly outnumber AI engineers, you’ll accumulate un‐deployed prototypes; if reversed, engineers will starve for new models to build.

• Leverage MLOps platforms (e.g., MLflow, Kubeflow) and CI/CD for models to reduce per‐model overhead and let each AI engineer support more data scientists.

• Red flags: long gaps between prototype and deployment, high model‐drift incidents in production, or a backlog of “ready” models awaiting engineering support.

• Regularly review this ratio during hiring and project planning to keep your AI pipeline flowing smoothly.

Closing Thoughts

As CTOs, we’re constantly balancing priorities—scaling systems, delivering roadmaps, and leading teams.

Organizational design often takes a back seat, but it directly impacts your ability to execute & deliver.

If your managers are overburdened or your QA team is stretched too thin, you’re not just creating inefficiencies you’re limiting your team’s potential.

These ratios aren’t just best practices really, they’re tools to help you proactively shape your team to maximize impact.

Org design is a process that evolves with your business, of course.

Ratios provide a valuable baseline, but they’re not absolute. Your instincts, combined with the unique needs of your company will guide when to challenge or adapt them.

Ignoring ratios entirely, however, leaves too much to chance.

Ratios help you ask critical questions: Are developers well-supported? Is quality balanced with speed? Are leaders empowered or overwhelmed?

Ultimately, your org structure reflects your priorities as a leader.

Use these ratios as a lens to refine and strengthen your structure because, in the end, you really do ship your org structure.

Btw, email me at bobby@technocratic.io if you need help on this :)