When DIY still makes sense
- You are actively researching SLAM algorithms and need total control.
- You already have the platform team to own the full runtime.
- You need a deeply custom output path that no shared product can expose yet.
Comparison
LidarFlow vs. rolling your own LiDAR SLAM pipeline
There is nothing wrong with building the stack yourself if that is the product. But if what you actually need is a reliable rosbag or MCAP to map workflow, it helps to be honest about where the engineering time really goes.
| Category | LidarFlow | DIY: GLIM + FlexCloud + orchestration |
|---|---|---|
| Setup time | Open the browser workflow and upload a recording. | Build and wire together GLIM, FlexCloud, storage, workers, and artifact delivery. |
| Maintenance cost | One product surface for upload, runs, preview, and downloads. | You own the mapping stack, the queues, the storage path, and the operational drift. |
| Reproducibility | Run settings and artifacts live together in one place. | Depends on how disciplined the team is with scripts, configs, and notebooks. |
| Team onboarding | Share the browser workflow and the downloaded artifacts. | New engineers inherit Dockerfiles, scripts, and environment setup before they can review a run. |
| Hardware needed | No ROS install or local GPU setup on every operator machine. | Every serious user eventually needs a maintained local or self-hosted runtime. |
| Best use case | Teams who need a rosbag or MCAP to map workflow without rebuilding the stack every time. | Teams doing deep engine research or maintaining a custom mapping platform anyway. |
When the managed path wins
- You mostly need the maps, not another quarter of platform work.
- You want reproducible runs and easy artifact review across the team.
- You want onboarding to start with a browser link instead of a Docker README.
Next step