10 Common monorepo problems and how your team can solve them

“Monorepos Seem Great… Until They’re Not”

You’ve probably heard the hype: if tech giants like Google, Facebook, and Uber are all using monorepos, shouldn’t your team jump on board too? I mean, it sounds like a no-brainer. These companies have mastered the art of managing massive, complex systems, and they’ve reaped some pretty sweet benefits, code sharing, unified workflows, and a single source of truth. Who wouldn’t want that?

At first glance, the idea of a monorepo is incredibly appealing. Imagine it, one repository to rule them all. All your projects, dependencies, and code, living together in perfect harmony. No more chasing down changes across multiple repos, no more wrestling with mismatched dependency versions. Everything’s in one place, neatly synced up. It’s like the developer’s version of a perfectly organized desk.

But then… reality hits.

You start to notice problems. Cloning the repository takes forever. Your CI pipelines become overly complicated. Dependency conflicts keep appearing no matter how many times you fix them. Suddenly, the idea of a smooth, well-organized system feels more like a constant struggle to maintain.

Here’s the thing, monorepos can be amazing. They promise simplicity, cohesion, and seamless collaboration, especially when you hear stories about how Google and Facebook have made them work at scale. But let’s be real, most of us aren’t working with the same engineering resources, custom tooling, or armies of infrastructure experts. Without careful planning and the right setup, a monorepo can quickly turn into quicksand, pulling your team deeper into complexity instead of lifting you out of it.

The truth is, a monorepo isn’t a silver bullet. Sure, it has its perks, but the tradeoffs can be huge. Poor planning can lead to bloated repositories, painfully slow builds, and developers who feel like they’re drowning in overhead. And ironically, the very cohesion you were hoping for can end up slipping through your fingers.

So, what’s the solution? In this article, we’ll dive into the 10 most common problems teams face when adopting monorepos, and, more importantly, how to solve them. Because while monorepos can be powerful, they demand respect, planning, and a solid strategy. Sometimes, what looks like simplicity on the surface can hide a world of complexity underneath. Ready to dig in? Let’s go.

What is monorepo?

Let’s break it down,  a monorepo (short for “monolithic repository”) is essentially a single, centralized repository that houses everything in your organization’s codebase. Instead of the modern approach where each project, microservice, or library gets its own separate repository, a monorepo brings it all together under one roof. Think of it as a giant, shared workspace for your code, where services, libraries, configuration files, and even datasets all live side by side.

At its best, a monorepo simplifies development by unifying code organization and workflows. Developers gain greater visibility into the entire codebase, fostering collaboration and shared ownership. Shared tooling and consistent coding practices emerge naturally, and managing dependencies or cross-project updates becomes more streamlined.

In practice, a monorepo often follows a single build pipeline, with components neatly organized into subfolders. This structure makes it easier to track changes, roll out updates, or manage releases across the entire codebase. For teams looking to reduce friction and keep everyone on the same page, a monorepo can feel like a breath of fresh air.

However, the centralized nature of a monorepo isn’t without its challenges. One lingering bug or misstep can potentially stall the pipeline, impacting unrelated projects. While this setup promotes cohesion, it demands careful management to prevent bottlenecks and maintain the balance between simplicity and scalability.

Monorepo vs. Multi-Repo

When it comes to managing codebases, the debate between monorepos and multi-repos is as old as modern version control systems themselves. Both approaches have their strengths and weaknesses, and understanding the differences is crucial to picking the right fit for your team or organization.

A monorepo consolidates all your projects, applications, services, libraries, and configurations, into a single, centralized repository. Think of it as a shared workspace where everything lives together. This setup fosters a sense of unity and collaboration among teams. You can trace changes across projects in one place, maintain consistent tooling and processes, and even simplify dependency management when multiple services share common code.

With every commit, the new code is immediately visible and usable by all developers, which helps avoid the painful merges that often plague large teams working across multiple repositories.

But with great unity comes great responsibility. Monorepos can quickly balloon in size, slowing down Git operations like cloning or fetching. Dependency conflicts may ripple across the entire repository, and managing CI/CD pipelines requires careful planning to avoid testing or building irrelevant parts of the codebase. Without the right tools and processes, what starts as a streamlined solution can quickly turn into a tangled mess.

So, why might a monorepo be the right choice for your team? Here are a few compelling reasons:

• Single source of truth: A monorepo ensures that everyone is working from the same playbook, reducing confusion and misalignment.
• Code sharing and reuse: With all your code in one place, sharing and reusing components across projects becomes effortless.
• Dependency visibility: Need to know how a change in one service might impact another? A monorepo makes it easy to track dependencies and assess the ripple effects of your changes.
• Atomic changes: Want to make a change that spans multiple projects? A monorepo lets you do it in one go, without the headache of coordinating across repositories.
• Enhanced collaboration: By breaking down silos, a monorepo encourages teams to work together more effectively, fostering a culture of shared ownership.
• Large-scale refactoring: Planning a major code overhaul? A monorepo simplifies large-scale changes, making it easier to refactor code across the entire codebase.

On the flip side, multi-repos offer a more modular approach. Each project or service gets its own repository, which can make it easier to manage smaller, independent codebases. This setup can be ideal for teams that need clear boundaries between projects or want to enforce strict access controls. However, it can also lead to fragmentation, making it harder to share code, maintain consistent tooling, or track dependencies across projects. 

However, multi-repos have their own challenges. Coordinating changes across repositories can feel like herding cats, especially when updates span multiple services or libraries. Dependency management becomes a juggling act, requiring robust tools and communication to ensure consistency. And let’s not forget the overhead of maintaining separate CI/CD pipelines for each repository.

When to Use Mono vs. Multi-Repos?

So, which approach is better? It depends. A monorepo works well when you prioritize consistency, collaboration, and a tightly integrated codebase. It’s great for teams that rely on shared tooling or frequently modify code that spans multiple projects. On the other hand, a multi-repo is often the go-to choice for organizations that value independence, modularity, and team autonomy. 

Multi-repos provide the autonomy to use different programming languages or frameworks for different services, tailoring the technology stack to the specific needs of each project.

That said, managing dependencies and coordinating changes across repositories in a multi-repo setup can get tricky. Without strong communication and tooling, things might start feeling fragmented.

Common Problems of Working with a Monorep.

1. Large Repository Size

At first glance, managing a monorepo feels like a developer’s dream, everything in one place, no more hopping between repositories, and seamless code sharing. But as your monorepo grows, that dream can quickly turn into a nightmare. 

Over time, as more projects, services, and libraries are added, the repository can balloon to a size that overwhelms even the most robust version control systems. Suddenly, simple tasks like cloning, fetching, or even searching for files take forever, leaving developers twiddling their thumbs.

Take Google, for example. Their monorepo is legendary, reportedly housing billions of lines of code. To manage this, Google built custom tooling, including their proprietary version control system, Piper. Features like efficient file caching and partial checkouts help them navigate the challenges of their monolithic approach. 

But let’s be real, most organizations don’t have the resources to build such bespoke solutions. For the rest of us, a growing monorepo can feel like trying to run a marathon in quicksand.

If you’ve ever worked with a repository containing thousands of files, you know the pain. Basic Git commands like git status or git checkout, which usually take milliseconds in smaller repos, can grind to a halt. And if your work only involves a tiny slice of the codebase, it’s frustrating to deal with the overhead of a massive repository. You’re forced to interact with files you don’t need, which wastes time, drains focus, and kills productivity.

The challenges don’t stop there. Large repositories can slow down essential Git operations like cloning and fetching, creating bottlenecks that ripple through your workflow. Build times stretch out, and navigating the sprawling codebase becomes a chore. What started as a streamlined solution can quickly turn into a developer’s worst nightmare.

Fortunately, there are ways to mitigate the challenges of a growing monorepo:

• To address this issue, repository partitioning and modularization can help break the monorepo into logical subfolders or modules, reducing unnecessary complexity. Tools like Nx and Bazel are particularly effective at enforcing modular boundaries and optimizing builds for monorepos through features such as remote caching, dynamically allocating machines to distribute tasks, and fine-grained e2e test splitting.
• Why download the entire repository when you only need a small part of it? Git’s sparse checkout feature lets you clone only the files or directories you need, saving time and disk space. Here’s an example.
  

saving time and disk space. Here’s an example.

$ git clone --filter=blob:none --no-checkout https://github.com/isaactonyloi/sparse-checkout-example
Cloning into 'sparse-checkout-example'...
Receiving objects: 100% (373/373), 75.98 KiB | 2.71 MiB/s, done.
Resolving deltas: 100% (23/23), done.
 
$ cd sparse-checkout-example/

$ git sparse-checkout set --cone
 
$ git checkout main
remote: Enumerating objects: 2, done.
remote: Counting objects: 100% (2/2), done.
remote: Compressing objects: 100% (2/2), done.
remote: Total 3 (delta 0), reused 1 (delta 0), pack-reused 1
Receiving objects: 100% (3/3), 1.41 KiB | 1.41 MiB/s, done.
Already on 'main'
Your branch is up to date with 'origin/main'.

$ git sparse-checkout set client/android
remote: Enumerating objects: 3, done.
remote: Counting objects: 100% (3/3), done.
remote: Compressing objects: 100% (3/3), done.
remote: Total 26 (delta 0), reused 1 (delta 0), pack-reused 23
Receiving objects: 100% (26/26), 985.91 KiB | 13.69 MiB/s, done.

• 3. Optimize Version Control Practices
  • Archive unused projects: If certain projects or files are no longer in active use, archive them to reduce the repository’s size.
  • Use Git LFS for Large Files: Store large binary files (like images or datasets) outside the main repository using Git Large File Storage (LFS).
    
    
  • Automate maintenance: Regularly clean up and optimize your codebase to keep it lean and efficient.
    
• Shallow clones: If you only need the latest commits, use shallow clones to fetch just the most recent history. This can significantly reduce clone times and disk usage.

git clone --depth 1 https://github.com/your-repo.git

2. Complex Dependency Management

Let’s talk about dependencies, the glue that holds your code together,  until it doesn’t. In a monorepo, dependency management isn’t just tricky, it’s high-stakes. Picture this, you tweak a shared library to fix a tiny bug, and suddenly, five different projects start throwing errors. Why? Because in a monorepo, everything’s connected. One small change can ripple across the entire codebase like a rogue wave, leaving version mismatches, breaking changes, and frustrated developers in its wake.

Without a solid strategy, managing dependencies in a monorepo can feel like trying to solve a Rubik’s Cube blindfolded. One wrong move and the whole thing falls apart.

So, How Do You Keep the Peace?

1. Lay down the law (for Dependencies)

Start with clear, enforced guidelines for versioning and updating shared libraries. Think of it as a “code constitution” that everyone agrees to follow. 

2. Use the right tools for the job

Modern package managers like npm workspaces, Yarn, or pnpm are your best friends here. They’re designed to handle monorepo chaos by:

• Linking related packages automatically (no more manual npm link nightmares).
• Resolving dependencies locally so projects don’t clash.
• Keeping node_modules lean and mean (looking at you, pnpm!).

3. Automate, automate, automate

Let bots do the grunt work. Tools like Dependabot or Renovate can:

• Scan your codebase for outdated dependencies.
• Open pull requests to update them.
• Flag potential conflicts before they hit production.
• This saves your team from playing “dependency whack-a-mole” every sprint.

4. Test like your code depends on It (Because It does)

Invest in conflict resolution tools and CI pipelines that run tests across projects. For example, if you update a shared utility, your CI should automatically check if it breaks any dependent services. Tools like Lerna or Rush can help orchestrate this.

3. Build Performance and Scalability Issues

Let’s face it, slow builds are the commute-from-hell of monorepos. At first, everything’s smooth, you make a change, hit build, and you’re off. But as your monorepo grows, it’s like rush hour traffic. Suddenly, every build involves the entire codebase, and your team is stuck idling, waiting for CI pipelines to crawl. Developers lose momentum, release cycles stretch out.

The problem? Monorepos love to punish you for their own success. The bigger they get, the more they demand. A tiny CSS tweak shouldn’t trigger a 30-minute build.

How to Escape the Build Time Nightmare

• Incremental builds & caching

Why rebuild everything when only one file changed? Tools like Bazel or Nx act like a GPS for your builds, they track dependencies and only rebuild what’s affected. Pair this with caching (think of it as a “build memory”) to skip redundant work. For example:

• Parallelize Everything

Modern build tools can split tasks across multiple threads or machines. It’s like adding lanes to a highway, suddenly, your builds aren’t stuck in single-file traffic. Gradle and Webpack (with plugins like thread-loader) excel here.

For instance, when using Gradle, build scans give you a visual timeline of task execution. In the following example build, you can see long-running tasks at the beginning and end of the build:

Tweaking the build configuration to run the two slow tasks early on and in parallel reduces the overall build time from 8 seconds to 5 seconds.

• Optimize Your Configs

Bloated build configurations are the potholes of your pipeline. For example:

• Webpack: Use code splitting and tree-shaking to trim fat.
• Maven/Gradle: Cut unused plugins and enable incremental compilation. Small tweaks here can shave minutes off builds.

• Monorepo-Specific Tools Are Your Lifeline

Generic build tools buckle under monorepo scale. Swap them for Bazel or Nx, which are built for this chaos. They:

• Track dependencies at the file level (no more “rebuild the world”).
• Cache everything, locally and remotely.
• Split end-to-end tests into parallel jobs (because waiting 2 hours for tests is a crime).

How Digma Can Help Your Team

Tools like Digma’s Preemptive Observability Analysis offer an additional edge by identifying potential build bottlenecks and performance issues before they become critical. By analyzing code-level metrics and pipeline activity, Digma can provide actionable insights to optimize builds and maintain smooth workflows, especially in complex monorepos.

4. Lack of Clear Boundaries Between Projects

One of the biggest challenges in a monorepo is maintaining clarity around project boundaries. When multiple projects, services, and teams coexist in the same repository, the lines between them can blur. 

This can lead to confusion about module ownership, difficulty identifying which team is responsible for a specific section of code, and unintentional changes that ripple through unrelated projects.

Without clear boundaries, developers may inadvertently break functionality in other parts of the codebase, leading to merge conflicts, prolonged debugging, and even strained team dynamics. In a collaborative environment, lack of structure can quickly turn what should be a productive shared space into a chaotic free-for-all.

Establish Clear Project Boundaries and Ownership
Assign specific teams or individuals ownership over defined sections of the repository. This ensures accountability and clarity when issues arise. Owners should be responsible for reviewing changes, maintaining code quality, and approving updates within their domain. Using tools like CODEOWNERS files in Git can help automate this process by enforcing review requirements for specific files or directories.

1. Implement Code Organization Standards and Documentation
   Organize the monorepo into logical, well-documented folders or namespaces that reflect the structure of the projects it contains. For example, grouping code into directories like services/, libraries/, and configs/ can make it clear where specific functionalities reside. Complement this structure with thorough documentation.

2. Use Namespace and Module Patterns
   Adopting consistent namespace or module naming conventions can help delineate boundaries between projects. For instance, prefixing modules or packages with identifiers like auth-service/ or payments-library/ creates a natural separation that developers can easily follow. This pattern not only clarifies ownership but also minimizes the risk of cross-project interference.

5. Tooling and Continuous Integration Challenges

Monorepos promise streamlined collaboration and code reuse, but they come with tooling challenges. Not all tools are designed to work with monorepos, which can limit your options and require custom solutions

This is where monorepo management tools come into play. These tools help manage builds, testing, and dependencies efficiently in a monorepo setup. Here’s a guide on some of the best monorepo tools available:

Digma: How Digma Helps with Monorepos

Monorepos centralize code but amplify risks like breaking changes, unintended side effects, and scaling complexity. Digma steps in as a preemptive observability tool, designed to tackle these challenges head-on by focusing on early issue detection, code-level insights, and scaling collaboration. Here’s how Digma can transform your monorepo workflow:

1. Preventing Breaking Changes

In a monorepo, a single change to a shared library can ripple across multiple projects, causing unexpected failures. Digma’s pre-production observability identifies these risks before they reach production. By analyzing runtime behavior and dependencies, Digma:

Flags potential breaking changes in shared libraries.

• Highlights which services or modules will be impacted by a change.
• Provides actionable insights during code reviews, reducing the risk of destabilizing dependent projects.

Example: A developer updates a utility function used by 10 services. Digma detects latency spikes in dependent APIs during testing and flags the change in the pull request, preventing a production outage and boosting team productivity.

2. Streamlining Code Reviews

Monorepos often involve multiple teams working on interconnected projects, making code reviews complex. Digma integrates with pull requests to:

• Surface cross-project impacts of changes.
• Track performance regressions (e.g., latency spikes, resource overloads).
• Identify usage patterns of deprecated functions or libraries.

This turns code reviews into data-driven conversations, helping reviewers focus on high-risk areas rather than sifting through raw code diffs.

3. Validating Changes Beyond Unit Tests

Unit tests alone can’t catch all issues in a monorepo, especially when changes affect multiple services. Digma monitors real-world usage of code across the repository, enabling teams to:

• Detect hidden dependencies that might not break tests but cause issues later.
• Validate component interactions under realistic conditions.
• Increase test coverage by identifying gaps and creating more complete test cases.

Example: Digma identifies that a shared authentication library is used by 3 services in ways the tests didn’t cover, prompting the team to add missing test scenarios.

4. Scaling with Confidence

As monorepos grow, so does the complexity of managing dependencies and ownership. Digma acts as a code GPS, helping teams:

• Map dependencies between services and libraries.
• Identify dead code or orphaned modules that can be safely removed.
• Track performance trends to prevent slow degradation over time.

By providing visibility into the codebase’s structure and behavior, Digma ensures that scaling doesn’t come at the cost of stability.

Why Digma Stands Out

Unlike traditional APMs (Application Performance Monitoring tools), Digma is designed specifically for pre-production environments. It:

• Works with fragmented telemetry data, reducing operational overhead.
• Provides proactive insights by predicting risks during testing.
• Links anomalies directly to specific lines of code, commits, or functions, making debugging faster and more efficient.

Tour the product: The Digma Sandbox lets you explore the use of Preemptive Observability during development, providing a clearer understanding of its benefits.

Other MonoRepo Tools

1. Bazel: Helps with scalable, incremental builds and tests for large, multi-language monorepos.

2. Turborepo: Helps with blazing-fast caching and parallel task execution for JavaScript/TypeScript codebases.

3. Nx: Helps with holistic task orchestration, distributed caching, and dependency tracking across projects.

4. Lerna: Helps with streamlining versioning and dependency management for npm-based monorepos.

5. Rush: Helps with managing complex dependencies and isolated builds in enterprise-scale monorepos.

6. Pants: Helps with fine-grained caching and polyglot support (Python, Go, Java, etc.) for mixed-language repos.

7. Gradle: Helps with flexible, plugin-driven builds optimized for JVM languages (Java, Kotlin).

8. Lage: Helps with task scheduling and topological execution for JavaScript monorepos

6. Steep Learning Curve for New Contributors

Monorepos, while powerful for code sharing and collaboration, can pose a significant hurdle for new contributors due to their sheer scale and intricacy. A sprawling repository housing dozens (or hundreds) of projects, libraries, and tools can feel like a labyrinth to newcomers. 

Without clear guidance, developers may struggle to locate relevant code, understand interdependencies, or follow team workflows, leading to frustration and delayed productivity. Let’s explore this challenge in depth and unpack proven solutions.

Solutions

• Structured documentation: Create detailed guides with repository maps, dependency diagrams, and step-by-step workflows (e.g., Microsoft’s TypeScript contributor guide).  
• Modular entry points: Break the monorepo into logical sections (e.g., `frontend/`, `shared-libs/`) to let contributors focus on smaller, manageable areas first.  
• Mentorship programs: Pair new hires with experienced developers for hands-on guidance.  
• Scaffolded starter tasks: Assign isolated, beginner-friendly tasks (e.g., updating a microservice) to build familiarity without overwhelm.  
• Tooling for clarity: Use tools like Nx or CodeSee to visualize dependencies and enforce bounded contexts between projects.

7. Technical Debt and Codebase Complexity.

As monorepos grow, they can become cluttered with outdated code, unused dependencies, and modules that no one maintains. This technical debt piles up quietly, making the codebase harder to navigate, update, or debug. Teams might avoid touching older parts of the code for fear of breaking dependencies, or leave unused code “just in case” it’s needed later. Over time, this slows down development and turns simple tasks into time-consuming fixes.

How to Keep Your Monorepo Clean

1. Regular code reviews + Incremental refactoring

• Focus on shared code and prioritize reviewing libraries, utilities, or APIs used across multiple projects. A small tweak here can impact many teams.
• Instead of overhauling everything at once, tackle one service or module per sprint. For example, modernize a legacy logging system or update a deprecated API in a single directory.
• Use IDE plugins: Tools like SonarLint or CodeClimate highlight code smells (e.g., duplicated code, unused variables) as you work, making it easier to fix issues early.

2. Track technical debt 

Document technical debt in your project management tools by creating clear, actionable tickets in Jira, Linear, or GitHub Issues. Examples include tasks like “Migrate outdated authentication module” or “Remove unused dependencies from /libs/utils.” Make sure to prioritize these tasks alongside new feature development to keep your codebase healthy.

Measure your progress with tools like SonarQube, which generate detailed reports on code quality trends, such as “Technical debt decreased by 15% this quarter.”

3. Automate Code Quality Checks

Enforce standards in CI: Set up GitHub Actions or GitLab CI to block PRs if new code introduces vulnerabilities, linting errors, or drops test coverage below a threshold.

Clean up dependencies: Use tools like depcheck to find unused npm packages or Renovate to auto-update outdated libraries.

# Scan for unused dependencies in a project  npx depcheck ./apps/frontend

1. bundle analyzer show which modules aren’t referenced.
   • Archive responsibly: Move old projects to a /legacy folder (with clear documentation) instead of deleting them outright.

8. Version Conflicts and Inconsistent Code Standards

In a monorepo, managing versioning across multiple projects and ensuring consistent coding practices can become a significant challenge. Version mismatches may arise when different teams update shared dependencies at different rates, leading to compatibility issues. Similarly, inconsistent coding styles across projects can result in maintainability problems and reduce the quality of the codebase.

Solutions

• Centralized versioning strategy: Implement a unified versioning approach to manage shared dependencies. Tools like Lerna or Yarn workspaces can help synchronize package versions and ensure consistency across projects.

• Linting and formatting tools: Use tools like ESLint, Prettier, or SonarQube to enforce consistent coding standards throughout the repository. Linters are configured with rulesets that define coding standards, including formatting conventions, naming conventions, and other stylistic guidelines.  
• You can automate these checks in CI pipelines to ensure adherence. In this example below, you can see the linter covering several executions across several tools:

• Maintain a living Monorepo Style Guide documenting patterns for dependencies, error handling, testing, and architecture.

9. Collaboration and Coordination Overhead

With multiple teams working in the same repository, coordinating changes and managing dependencies can become challenging. Poor communication and lack of visibility into each team’s progress can lead to duplicated efforts or unintended conflicts.

Solutions

• Clear communication channels: Use tools like Slack, Microsoft Teams, or project management platforms like Trello or Asana to keep teams aligned.
• Agile practices tailored for monorepos: Implement agile methodologies, such as sprint planning and daily stand-ups, to ensure cross-team collaboration.
• Dependency and impact tracking tools: Leverage project management tools with dependency tracking features, such as Jira Advanced Roadmaps or Linear, to map changes and their impacts across the repository.

10. Merge Conflicts and Integration Issues can Increase.

In a monorepo, multiple developers and teams contribute to a shared codebase, often modifying the same files, libraries, or configurations. This collaborative environment amplifies the risk of merge conflicts, especially in large organizations with hundreds of daily commits. For example:

A monorepo might contain shared utilities, configuration files, or UI components that many teams depend on. A change to a widely used file (e.g., a shared API client) can trigger conflicts if multiple developers edit it simultaneously.

• Simultaneous Changes: Long-lived feature branches increase the likelihood of divergent code, especially when multiple developers work on interdependent features.
• Shared Dependencies: Updates to a dependency version in one project might clash with another team’s work elsewhere in the repo.

Solutions to reduce merge conflicts

1. Smaller, frequent commits

Large, infrequent commits increase the chance of overlapping changes. Break work into smaller, atomic commits that address specific tasks. Encourage developers to merge changes into the main branch multiple times a day (e.g., via trunk-based development).

2. Feature flags

Long-running branches risk conflicting with upstream changes. Use feature flags (toggles) to hide incomplete features in the main branch. This allows developers to merge code incrementally without exposing unfinished work.

3. Pre-Commit hooks for code standards

Inconsistent formatting (e.g., tabs vs. spaces) creates unnecessary conflicts. Enforce code formatting and linting rules via pre-commit hooks. 

Tools like Husky (for Git hooks), Prettier, or ESLint automatically format code before commits.  Standardized formatting eliminates “noise” conflicts caused by stylistic differences.

Conclusion

In this article, we’ve explored 10 common problems that teams often encounter when working with a monorepo, along with practical solutions to help you tackle each one effectively.

Working with a monorepo comes with its fair share of challenges, some we’ve covered here, and others you might face as your codebase grows. Think slow CI pipelines, complex dependency management, scalability issues, and version conflicts. But here’s the good news, none of these are deal-breakers. With the right strategies, tools, and workflows in place, a monorepo can become one of your greatest assets. It can enhance code reuse, improve cross-team collaboration, and simplify dependency management. Ultimately, it’s all about finding the right approach and tools to make it work for you.

Requrest a demo with Digma to see how your team can solve similar issues.