Platform groups gain real impact when they shift from doing everything to delivering defined, developer-centric outcomes, with clear roadmaps, boundaries, and service levels, cutting noise and making scaling easier across product lines.
In the initial stages, platform teams operate much like a startup, where everyone is expected to wear different hats depending on the need, with the same person coding a feature required for a new prospect while also contributing to marketing and pre-sales.
This operational structure can work well for the first subset of customers – maybe 20-30 – but it can easily start to break down as the number of customers scales rapidly.
Ashok Chawla, co-chief delivery officer for R Systems, explains a product-oriented platform team is built around clear functional responsibilities.
“On the product side, product managers define the vision and execute the roadmap,” he explains.
Engineering includes product owners, platform architects, UX designers, data analysts or scientists, developers, quality engineers, and DevOps engineers–with a separate sustenance engineering team added as the product scales.
“These teams must be focused on customer centricity, scalability and reliability, continuous improvement, innovation, a fail-fast approach, and collaboration,” Chawla says.
He adds cloud operations and SRE groups consist of SRE engineers and customer support engineers who maintain reliability and support.
Meanwhile, customer success focuses on onboarding and retention, while functions such as marketing, finance, HR, and administration provide the operational foundation.
“As companies grow, product management plays a key role in keeping scope tight,” Chawla says.
The priority should be given to features that are more common in nature and able to be sold to multiple customers, rather than pursuing ad-hoc or one-off requests.
Ensuring Platform Team Alignment
Chawla adds that platform architects and product managers play a critical role in maintaining a focused technology footprint.
“The emphasis should be on delivering business functionality rather than on adopting new tools, open-source software, or expanding infrastructure unnecessarily,” he says.
He points out that end customers want tangible business benefits, not shiny or experimental tech.
“That said, platform security, scalability, reliability, and availability are essential considerations when making decisions about infrastructure and tooling,” he says.
Pavlo Baron, co-founder and CEO, Platform Engineering Labs, says when developers cannot break out into low-level details and too much access, they will be constrained by what they can request.
“Often, developers break out into alternative approaches and tools when the right side of the cycle is perceived as slow,” he says.
Baron advises leaders to educate everyone about the differences in jobs and responsibilities and clarify and follow through on responsibilities.
“Simply renaming ‘Ops’ into ‘Platform Team’ is not going to cut it,” he says.
KPIs, Centralized Ticketing Systems
To achieve high customer satisfaction, Chawla says several best practices are essential, with Service Level Agreements (SLAs) well-defined and communicated to customers.
“Internal teams should track KPIs and review them regularly,” he explains. “Engineering teams must provide clear documentation and training to customer support and sustenance engineering teams.”
Centralized ticketing systems can help manage workloads effectively, while continuous training ensures teams stay up to date.
“Lastly, mandatory customer surveys provide ongoing feedback to guide improvements and quality,” Chawla says.
Outcome Driven Platform Ops
To move from reactive, ticket-driven chaos to outcome-driven platform operations, several shifts are essential.
Teams should adopt a “shift-left” approach with best-in-class observability and monitoring frameworks.
Modern generative AI tools can be leveraged to debug issues agentically and automate resolutions wherever possible.
“Empowering teams to make independent decisions is critical, along with providing continuous feedback to engineering teams about recurring issues,” Chawla says.
He suggests implementing agile practices with a focus on continuous learning to help the organization adapt and improve.
“Finally, moving toward a customer self-service model using modern chatbot interfaces can reduce reactive workload and increase operational efficiency,” Chawla says.