If you are a new reader, my name is Danar Mustafa. I write about product management focusing on AI, tech, business and agile management. You can visit my website here or visit my Linkedin here. I am based in Sweden and founder of AImognad.se – the leading AI maturity Model Matrix. Get your free assessment here.

Want to Learn from OpenAI’s Engineering Team? Here’s the Secret Sauce Behind Their Success

Curious how OpenAI ships cutting-edge AI products at scale? Whether you’re an engineer looking to grow, a tech lead scaling your team, or exploring your next big career move — this post breaks down the real engineering practices behind OpenAI’s success.

From monorepos and FastAPI to team autonomy and CI challenges, here’s what makes their engineering culture work — and what we can all learn (and apply) from it.

OpenAI’s Engineering Approach

1. Monorepo Strategy – A unified codebase (mainly Python) supports visibility and reuse but demands strong internal standards to maintain quality.
2. Modern API Stack – Using FastAPI and Pydantic enables rapid development of robust, type-safe APIs, reducing friction between teams.
3. Cloud Native at Scale – Running fully on Azure (AKS, CosmosDB, BlobStore) shows the effectiveness of managed services for speed and scalability.
4. Talent Advantage – Hiring engineers from infrastructure-heavy companies like Meta accelerates adoption of proven, battle-tested practices.
5. Customized Core Systems – Rebuilding critical infrastructure (like internal auth systems) offers control and alignment with company needs.
6. Use-Case-Driven Architecture – Designing systems around core user experiences (e.g., chat) leads to focused, scalable product delivery.
7. Decentralized Decision-Making – Empowering teams to own decisions boosts velocity but requires clear communication and alignment.
8. Bias for Action – Fast iteration is a cultural cornerstone, supporting rapid progress at the cost of occasional technical debt.
9. CI/CD and Tooling Gaps – Rapid growth outpaced test infrastructure, highlighting the need to invest early in developer productivity tooling.

OpenAI balances autonomy, action, and alignment — a model that encourages innovation, but only works with strong foundations in tooling, hiring, and product-driven engineering.

Let’s dive into each section.

🗂 Monorepo & Python

What:
OpenAI uses a giant monorepo that’s mostly Python, although there’s some Rust and Go sprinkled in for specific use cases.

Takeaway:
A monorepo offers visibility and shared tooling, but without enforced style guides, things can get messy. If we move in this direction, we need strong code conventions.

⚙️ API and Validation

What:
Most services are built with FastAPI for APIs and Pydantic for validation.

Takeaway:
FastAPI + Pydantic is a powerful combo for building well-structured and type-safe APIs. Worth considering for internal and external services.

☁️ Everything Runs on Azure

What:
OpenAI runs entirely on Azure, especially relying on:

• Azure Kubernetes Service (AKS)
• CosmosDB
• BlobStore

Takeaway:
Even large-scale companies use managed cloud services. Picking the right ones can significantly simplify infrastructure and operations.

🔁 Meta → OpenAI Pipeline

What:
There’s a strong talent pipeline from Meta to OpenAI, especially on the infrastructure side.

Takeaway:
Hiring experienced engineers from companies with similar scale and systems can bring major advantages in terms of best practices and velocity.

🧱 In-House TAO Implementation

What:
OpenAI built its own internal version of TAO, Meta’s identity/auth system.

Takeaway:
When identity and access are core to your platform, building a tailored solution might be worth the investment.

💬 Chat-Centric Codebase

What:
Much of the architecture is centered around the concept of chat messages and conversations (e.g., ChatGPT).

Takeaway:
Designing systems around your primary use case leads to more coherent, optimized solutions.

🛠 Decentralized Code Decisions

What:
No central architecture or planning committees — teams that plan to do the work make the decisions.

Takeaway:
Empowers teams and speeds up delivery — but can result in duplicated or inconsistent code unless offset by shared understanding and documentation.

⚡ Bias for Action

What:
OpenAI encourages bias toward action — teams move fast and build things quickly.

Takeaway:
Great for speed and innovation, but can lead to tech debt if not balanced with code quality and refactoring time.

🧪 CI and Tooling Challenges

What:
Rapid team growth + weak tooling led to:

• CI breaking often on master
• Slow test suites (~30 mins on GPU)
• “Dumping ground” backend repos

Takeaway:
Invest early in reliable CI/CD and developer tooling, especially during team expansion. Poor tooling can bottleneck productivity.

💡 Summary – Key Lessons

Area	Key Insight
Monorepo	Works well with strong conventions
APIs	FastAPI + Pydantic = fast, safe APIs
Cloud Infra	Pick reliable managed services
Hiring	Bring in talent with large-scale experience
Code Ownership	Empower teams but coordinate standards
Test Infra	Robust CI/CD is not optional
Engineering Culture	Bias for action = speed, but manage tech debt

Reflection from an AI Tech Lead’s Perspective:

Reading through OpenAI’s engineering choices isn’t just interesting — it’s grounding. It reinforces a few things I’ve come to believe as a tech lead, but also surfaces blind spots we all need to watch out for.

1. Monorepos Work — If Your Team Does

The monorepo strategy only works because OpenAI engineers trust each other to follow (implicit) conventions. That kind of discipline is earned, not assumed. For our team, it’s a reminder that scaling code means scaling habits — not just tools.

2. FastAPI + Pydantic = Power Without Complexity

It’s validating to see OpenAI lean on tools many of us use. Sometimes we over-engineer; OpenAI shows you can ship state-of-the-art systems with the same open-source stack everyone else has — if your team is sharp enough.

3. Managed Cloud Is Not a Shortcut — It’s a Strategic Choice

They didn’t invent their own infra stack — they used Azure, and went deep. That’s a huge lesson: focusing on your differentiator (the model, the experience) means letting go of unnecessary complexity. Not every problem needs a custom solution.

4. Engineering Culture > Engineering Committees

I’m struck by their decentralized decision-making. It’s bold. It means trusting teams to build what they need. But that only works with strong culture and clear guardrails. For us, it’s a call to level up our internal documentation, not our approval chains.

5. Bias for Action Must Be Matched with Tooling Maturity

They move fast — but they paid the price in broken CI and slow tests. That’s the trade-off. For my team, it’s a reminder: shipping fast is great, but the long-term cost of neglected infra is real and non-trivial.

6. Your Architecture Reflects Your Product

The fact that their system is built around “chat” concepts is brilliant. It’s not just API endpoints — it’s a domain model rooted in how users interact. It reminds me that architectural clarity starts with product empathy.

OpenAI’s setup isn’t magic. It’s a series of disciplined choices, made with deep product alignment and cultural intent. As a tech lead, I walk away reminded that our real leverage isn’t in tools or languages — it’s in clarity, culture, and keeping engineers close to the problem we’re solving.

Source:

This content started with this blog post. https://x.com/deedydas/status/1945366936893972710/. I have since then made some research and added some of my own ideas and thoughts.