Turborepo vs Nx: Quick Verdict
Turborepo vs Nx is the canonical monorepo tooling debate for JavaScript/TypeScript teams — and it surfaces constantly when AI teams try to organize a codebase that spans a Next.js frontend, a Node.js inference backend, Python evaluation scripts, shared prompt libraries, and deployment pipelines.
Choose Turborepo if you want minimal configuration, fast build caching with zero buy-in to an opinionated framework, and you're comfortable owning your own project conventions. It's the "just make npm run build fast" tool.
Choose Nx if you want a full-featured monorepo platform with code generation, dependency graph visualization, enforced module boundaries, and first-class support for multiple languages — including Python, which matters for AI teams.
Both tools solve the same core problem — making large monorepos fast and manageable — but they sit at different points on the convention vs. configuration spectrum.
Why AI Monorepos Have Unique Requirements
Before comparing tools, it's worth understanding why AI product monorepos have requirements traditional web application monorepos don't:
Polyglot codebases — Most AI teams write TypeScript for their API and frontend, Python for model fine-tuning, evaluation pipelines, and data processing scripts. Your monorepo tooling needs to understand — or at least not obstruct — both.
Shared prompt libraries — Prompt templates, system prompts, and few-shot examples are code. They need versioning, testing (via AI evaluation), and import resolution across packages.
Evaluation pipelines as first-class packages — An eval suite that runs against your staging environment before deployment is a build artifact. It needs to be part of your CI graph, not a separate repo.
Deployment heterogeneity — The frontend might deploy to Vercel, the inference backend to Kubernetes, evaluation scripts to GitHub Actions. Turborepo and Nx handle this differently.
Fast iteration cycles — When you're testing prompt changes against 200 test cases, slow builds kill velocity. Caching is not optional; it's the primary reason to adopt either tool.
How Each Tool Works
Turborepo is a build system that understands task dependencies and caches outputs aggressively. You define your task pipeline in turbo.json: which tasks depend on which, what inputs invalidate the cache, what outputs to cache. Turborepo then runs tasks in the optimal parallel order and restores cached outputs when inputs haven't changed. That's fundamentally it. No code generation, no module boundaries, no opinions about your package structure.
Nx is a full monorepo platform. It does everything Turborepo does (task runner + caching) but adds: code generators (nx generate), enforced module boundary rules (linting that prevents packages from importing packages they shouldn't), a visual dependency graph explorer, first-class plugin ecosystem for frameworks (Next.js, Vite, Playwright, FastAPI, etc.), and affected-command computation that scopes CI runs to only the packages that could be impacted by a change.
Feature Comparison
| Feature | Turborepo | Nx |
|---------|-----------|-----|
| Task runner | ✅ Yes | ✅ Yes |
| Remote caching | ✅ Vercel Remote Cache (free tier) | ✅ Nx Cloud (paid) / self-host |
| Local caching | ✅ Yes | ✅ Yes |
| Code generators | ❌ No | ✅ Yes (nx generate) |
| Module boundaries | ❌ No | ✅ Enforced via ESLint plugin |
| Dependency graph | Basic | ✅ Interactive graph (nx graph) |
| Python support | ❌ No native | ✅ Via @nxlabs/nx-python plugin |
| Affected commands | ❌ No | ✅ nx affected |
| Framework plugins | ❌ No | ✅ Next.js, Vite, Playwright, etc. |
| Migration tools | ❌ No | ✅ Automated migrations |
| Learning curve | Low | Medium-High |
| Config overhead | Low | Medium |
| License | MIT | MIT (core) |
Caching: The Core Value Proposition
Both tools offer aggressive local and remote caching. The mechanics are similar: inputs (source files, environment variables) are hashed, and if the hash matches a prior run, the cached output is restored instead of re-running the task.
Where they differ:
Turborepo's caching is entirely file-system based. It caches task outputs (the files your task produces) and restores them on cache hit. Configuration is straightforward: define inputs and outputs in turbo.json.
Nx's caching is more granular. It can cache at the level of individual project targets, cache test results per-project, and — critically for AI teams — supports nx affected which computes which projects in the graph could be affected by a given code change and runs only those tasks. For a large monorepo with 20+ packages, affected commands can reduce CI time by 80%+ compared to running all tasks on every commit.
For an AI monorepo where your eval suite takes 15 minutes to run against staging, nx affected scoped to "only run evals for packages that changed" is a meaningful CI time reduction.
Python Support: A Critical Differentiator for AI Teams
This is where Turborepo has a significant gap.
Turborepo understands JavaScript/TypeScript package graphs (via package.json workspaces). It has no native concept of Python packages, virtual environments, or Python task runners. You can run arbitrary shell commands as Turbo tasks, which means you can run pytest or python -m eval_suite, but Turbo won't understand Python dependency relationships, won't cache Python build artifacts intelligently, and won't include Python projects in task graph resolution.
Nx has the @nxlabs/nx-python plugin (community-maintained) that adds Python project support: pyproject.toml integration, virtual environment management, poetry support, and proper inclusion of Python projects in the Nx dependency graph. This means nx affected can correctly identify that a change to your shared embeddings library affects both your TypeScript API (which imports it via a REST call to a Python service) and your Python eval scripts (which import it directly).
For AI teams, Python support is often the deciding factor. If your team writes any Python — for evals, fine-tuning, data processing — Nx's ecosystem is meaningfully more complete.
Monorepo Structure for AI Products
A well-structured AI product monorepo, regardless of tooling:
/apps
/web # Next.js frontend
/api # Node.js inference backend
/eval-runner # Eval pipeline service
/packages
/ui # Shared UI components
/prompts # Prompt templates and schemas
/llm-client # Shared LLM API wrapper
/types # Shared TypeScript types
/tools
/eval # Python eval scripts
/fine-tune # Python fine-tuning scripts
/data # Data processing utilities
With Turborepo, the /tools Python directory is second-class — you manage it separately and hook it into Turbo via shell scripts.
With Nx, you can declare Python projects in /tools as proper Nx projects, give them targets (lint, test, run), and include them in the dependency graph. A change to /packages/prompts can trigger nx affected to re-run both the TypeScript tests that depend on it and the Python eval suite that validates prompt quality.
Remote Caching and CI Cost
Both tools offer remote caching that allows CI cache hits to be served from a central store, dramatically reducing build times for PRs that touch only a small part of the codebase.
Turborepo: Remote caching is built-in and free for teams using Vercel hosting. For self-hosted remote cache (via Turborepo's HTTP protocol), you can run your own cache server or use a community-built S3-backed implementation. This works well and is straightforward to set up.
Nx: Remote caching is via Nx Cloud, which has a free tier (limited cache size and monthly minutes) and a paid plan. Nx Cloud also adds distributed task execution (DTE), which parallelizes tasks across multiple CI agents — especially valuable for large test suites. For AI teams with expensive eval runs, DTE can parallelize eval execution across agents and cut CI time further.
Developer Experience
Turborepo's DX is genuinely excellent for what it does. Add turbo.json, define your pipeline, run turbo build. The learning curve is measured in minutes, not days. For small-to-medium teams that want their npm run build to be fast without adopting a new mental model, it delivers.
Nx's DX requires more investment. The concepts (projects, targets, generators, executors) take time to internalize. The payoff is a consistent development experience across all packages — nx run api:serve, nx run eval:test, nx run web:build all work the same way regardless of the underlying tech. For teams that grow past 3–4 engineers, this consistency reduces onboarding time and prevents the "I don't know how to run the eval package" problem.
Recommendation by Team Profile
Choose Turborepo if:
- Your team is TypeScript-only (or Python is truly a sidecar, not core)
- You want to move fast with minimal config
- You're already on Vercel and want free remote caching
- Your monorepo has fewer than 10 packages
Choose Nx if:
- Your team writes both TypeScript and Python
- You want
nx affectedto scope CI runs to changed packages - You need enforced module boundaries as the codebase grows
- You want code generators to maintain consistency across packages
- Your monorepo is growing beyond 10 packages
For most AI product teams building applications with a distinct Python evaluation and data layer, Nx's polyglot support and affected-command computation make it the stronger long-term choice, even if Turborepo is the faster start.
The Verdict
Turborepo wins on simplicity. Nx wins on capability. The crossover point — where Nx's overhead pays for itself — is roughly when your monorepo has Python code that needs to participate in the build graph, or when your CI costs from running all tasks on every PR become significant.
Start with Turborepo if you're early. Migrate to Nx when you hit its limits. Both support migration paths without requiring you to restructure your packages.
Related: Build vs Buy Your AI MVP · From MVP to Scale: Growing Your AI Product · What is AI Evaluation?
Need help setting up the right monorepo architecture for your AI product? Our team has opinions. →
Related Resources
Related articles:
Our solution: AI MVP Sprint — ship in 3 weeks
Browse all comparisons: Compare
Related Articles
- How We Ship AI MVPs in 3 Weeks (Without Cutting Corners) — Inside look at our sprint process from scoping to production deploy
- AI Development Cost Breakdown: What to Expect — Realistic cost breakdown for building AI features at startup speed
- Why Startups Choose an AI Agency Over Hiring — Build vs hire analysis for early-stage companies moving fast
- The $4,999 MVP Development Sprint: How It Works — Full walkthrough of our 3-week sprint model and what you get
- 7 AI MVP Mistakes Founders Make — Common pitfalls that slow down AI MVPs and how to avoid them
- 5 AI Agent Architecture Patterns That Work — Proven patterns for building reliable multi-agent AI systems