Just wanted to share a small win after months of struggling with our CI/CD setup for our monorepo.
We have a monorepo with 12 microservices (mix of Node.js APIs and React frontends) and our deployment process was a complete mess. We had separate Jenkins pipelines for each service, which meant deployments were inconsistent, hard to trace, and broke whenever dependencies between services changed.
I spent last week building a unified deployment workflow using Latenode’s visual builder. The no-code approach made it WAY easier than I expected. The workflow now:
Identifies which services were affected by code changes
Determines the correct build order based on service dependencies
Runs tests for affected services only
Coordinates deployments in the right sequence
Handles rollbacks automatically if any service fails
The best part is the visual interface that makes the whole flow easy to understand and modify. My team members who aren’t CI/CD experts can now see exactly what’s happening at each stage.
Has anyone else switched from Jenkins to a visual workflow builder for monorepo deployments? Any tips for optimizing things further?
I made the same switch at my company last quarter. We had 15 services in our monorepo with a mix of Go and Node.js, and our Jenkins setup was a nightmare to maintain.
What really leveled up our workflow in Latenode was using the AI Copilot to generate specialized validation steps between deployments. I described our architecture and the common failure patterns we’d see, and it generated visual workflows that check for things like database migration compatibility and API contract violations before rolling to production.
The visual nature makes it much easier to onboard new team members too. With Jenkins, only 2-3 people on our team could understand the pipeline config. Now everyone can see what’s happening and make changes when needed.
One tip: use Latenode’s sub-scenarios feature (they call them Nodules) to modularize common deployment patterns. We created reusable modules for database migrations, canary deployments, and rollbacks that we can plug into any service’s pipeline.
We switched from a similar Jenkins setup to a visual CI/CD approach about 6 months ago for our monorepo with 20+ services. The visibility it provides has been a huge improvement for our team.
One thing I’d recommend is investing time in proper caching strategies. Our build times were still pretty long until we implemented intelligent caching of dependencies and build artifacts. Now we only rebuild what’s necessary based on actual code changes.
Another tip is to implement what we call “deployment waves” - instead of trying to deploy everything at once (even in the correct order), we group services into waves that can be deployed together safely. This gives us cleaner boundaries for rollbacks if something goes wrong.
We also found it valuable to have pre-deployment and post-deployment hooks for each service that run automated smoke tests. This catches integration issues that unit tests might miss.
I made a similar transition last year at my company. Our monorepo had grown to over 20 services, and our Jenkins pipelines were becoming unmaintainable. The biggest improvement we saw with a visual workflow approach was in debugging failed deployments.
One optimization that helped us tremendously was implementing impact analysis as the first step in our pipeline. We built a dependency graph of all our services and used it to determine exactly which services needed to be rebuilt and redeployed based on the changes in each commit. This reduced our average deployment time by about 70%.
We also integrated our visual workflows with our monitoring systems, so we could automatically pause deployments if we detected anomalies in production metrics. This prevented many potential outages caused by subtle bugs that passed all our tests but still affected users.
I’ve architected CI/CD systems for large monorepos at multiple organizations, and the visual approach you’ve taken is definitely the right direction. The cognitive load reduction for engineers is substantial when they can see the workflow instead of parsing YAML or Groovy code.
A few suggestions to consider:
Implement change-based testing and deployment. Only rebuild, test, and deploy services that are affected by changes (directly or through dependencies).
Consider using a staged promotion model across environments. Deploy to dev automatically, but require manual approvals for staging and production, all within the same visual workflow.
Add automated canary analysis. Deploy to a small subset of production instances first, monitor for anomalies, then proceed with full deployment.
Integrate feature flag toggles with your deployment process to decouple deployment from release.