Robotics companies are deploying production systems at unprecedented scale. In 2024, factories worldwide installed a total of 542,000 industrial robots—more than double the volume from a decade ago. But this rapid scaling has exposed an infrastructure problem: many teams start with development frameworks suited for prototyping, planning to build production infrastructure later, but "later" never comes.
As costs mount and deployment pressures increase, companies get locked into building custom infrastructure instead of replacing their tools. The impact is direct. Engineering hours spent building custom infrastructure are hours not spent on the robotics problems that differentiate products.
While development frameworks may enable rapid prototyping, they also assume teams will build production infrastructure separately. A production platform provides that infrastructure—fleet management, testing frameworks, data services, deployment tools—as integrated capabilities from day one.
Development frameworks vs production platforms
What development frameworks provide (and what they don't)
Development frameworks like ROS excel at what they were designed for: enabling rapid prototyping through shared building blocks. They provide messaging infrastructure, extensive package ecosystems, and community-contributed components that let small research teams move quickly from concept to working prototype.
But frameworks make a fundamental and costly assumption: you'll build production infrastructure separately. They provide the communication layer, but not the operational layer. They give you the tools to connect components, but not the infrastructure to deploy, monitor, and maintain those components at scale. This design works perfectly for academic research and early-stage development. The problems emerge when teams try to use development frameworks as production platforms.
The hidden costs of using development frameworks in production
The infrastructure gaps in development frameworks aren't isolated issues—they represent systemic problems that compound as you scale:
- Early architectural decisions multiply over time. Testing infrastructure, for example, is much harder to build with 100 robots than with 1.
- Engineering time disappears into infrastructure work. Time spent building custom fleet management tools, debugging frameworks, or maintaining homegrown data pipelines is time not spent on the robotics problems that differentiate your product.
- Integration complexity grows exponentially. Production requires connecting your development framework to fleet management systems, data pipelines, testing infrastructure, and operational monitoring. Each connection point adds maintenance burden and tech debt.
The switching costs grow while production pressures mount, leaving teams committed to building infrastructure "on top of" their development tools to compensate.
What a production platform provides
A production platform inverts the framework assumption. Instead of providing development tools and expecting you to build operational infrastructure, they provide a complete infrastructure baseline: fleet management, testing frameworks, data services, observability systems, and deployment tools are integrated capabilities, not separate projects requiring custom development.
This integration matters because production requirements don't arrive sequentially. Platforms are built for the full product lifecycle—prototype through deployment through scale—with the understanding that operational capabilities can't be afterthoughts. The value proposition is straightforward: platforms reduce time spent on tooling and integration so engineering resources can focus on robotics problems rather than infrastructure maintenance.
The fundamental difference is about where your engineering team spends its time. Development frameworks assume you'll build infrastructure. Production platforms assume you'd rather build robots.
Robotics companies looking to scale from prototype to production need infrastructure that provides 7 foundational capabilities from day one.
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Read now7 capabilities your robotics infrastructure needs to scale from prototype to production
1. Iteration velocity
Software development relies on infrastructure and tooling that lets engineers move quickly, including percentage-based rollouts, CI/CD pipelines, A/B testing, and feature flagging. Robotics companies need similar tools in order to succeed. But iterating with hardware can be expensive and time-consuming—especially when the infrastructure is custom built. Companies that can test and pivot quickly when working with hardware are able to sidestep inefficiency and build better products faster.
2. Operational intelligence and observability
Robotics production requires an infrastructure that supports complex data pipelines. As your fleet grows, so will your operational data. That data is invaluable, being useful for everything from proving ROI to troubleshooting and debugging. However you use it, that data needs to be visible and actionable from day one. The most competitive robotics companies are the ones that use that data to improve their robots and reach their business goals faster.
3. Hardware flexibility
Building robots for production usually involves testing different hardware combinations—and companies rarely get this right on the first try. When prototyping, teams need to be able to swap hardware in and out, test different configurations, and evaluate robotic actuators. Manually integrating these separate pieces of hardware can slow down your development cycles precisely when your team needs to move the fastest. With a production-ready platform, you can switch hardware without affecting your application logic.
4. Fleet management at scale
Managing one robot is a systems problem. Managing 2,000+ robots around the country is an infrastructure problem. At that scale, you can't SSH into individual units to push updates or manually coordinate behavior across the fleet.
Production fleet management requires centralized capabilities: over-the-air software updates that can be rolled out progressively, fleet-wide monitoring dashboards that surface anomalies before customers notice them, and coordinated rollouts that catch issues at small scale before they impact your entire deployment. A production-ready infrastructure platform makes all of this possible.
5. AI data infrastructure
Building robotic systems that can perceive, reason, and act in the physical world requires that huge amounts of data be collected. With teleoperation maxing out at just 24 hours per robot per day—an amount that’s insufficient for training robust models—companies are scrambling for alternative data strategies: cameras on humans, mechanical exoskeletons, domain randomization in simulation. But in production, your deployed robots generate data continuously.
This operational data doesn't just inform debugging—it becomes the foundation for model improvement. Robotics companies need infrastructure that makes model iteration seamless: version control for models deployed across your fleet, automated pipelines that fine-tune models on new production data without manual intervention, and the ability to A/B test model versions in the field to validate improvements before fleet-wide rollout.
6. Robust testing infrastructure
Production robotics demands deliberate validation infrastructure. Some companies rely heavily on simulation environments, while others build hardware-in-the-loop testbeds, and many use staged rollouts with canary deployments to catch issues at small scale. There are merits to each, but the specific approach matters less than having a repeatable, automated process that validates changes before they reach customer sites. The point isn't to test in every possible way; it's to build testing infrastructure into your workflow from the start, making validation a standard step in your deployment pipeline. This is exactly what you can do with a production-ready infrastructure platform.
7. Phased implementation
Some call it “tech up now, upgrade later;” some call it “send it and de-jank it.” Either way, the principle is the same: robot deployments rarely begin with perfect hardware, optimal sensor suites, or complete feature sets. For fast-scaling companies, gradual automation with acceptable ROI at each stage is a better approach. It allows you to move forward with minimum viable capabilities and build a better system as you validate performance and prove value.
Production robotics requires supporting this growth curve. Start with basic pick-and-place operations before tackling complex manipulation. Deploy to a single site before scaling to dozens. Validate core functionality before adding advanced features. Each phase should deliver measurable value while laying groundwork for the next expansion.
Conclusion: Starting with the right foundation
Robotics companies need integrated production infrastructure, not separate tools to build on top of frameworks. Viam is a modular robotics platform that addresses these production requirements from day one.
Rather than forcing teams to choose between the flexibility of a development framework and the robust infrastructure of a production platform, Viam provides iteration velocity, operational intelligence, hardware abstraction, fleet management, data infrastructure, testing capabilities, and support for phased implementation as integrated platform capabilities.
Ready to see how Viam can help bring your robotics idea to market? Learn how you can scale from prototype to production on Viam.
