Torn between whether to build or buy the hottest new edge IoT data plane?
You’re in luck! It’s finally possible to gain full control over IoT insights WITHOUT a big-budget annual license fee. For the first time, data-driven enterprises are quickly spinning up their own custom architecture to harvest high volumes of data on-demand — even in the most complex IoT environments.
Rather than choosing to build or buy, leaders in the industry are doing what they’ve always done behind the scenes for the past decade. The untold advantage for most large companies — like our clients at Netflix, Universal Studios, and the Mayo Clinic — is to incubate transparent architecture solutions in-house with DFHeinz. In just a few weeks, we coach developer teams on how to combine and customize common and not-so-common open source projects to achieve complex business goals fast. Because business needs change, we equip teams with the tools they need to quickly reconfigure the architecture and accommodate critical-now needs.
What’s the difference between purchasing a black box vendor product and building your own transparent data solution? Millions in time, money, effort, and quality.
Need a solution that works immediately? You’re in luck. We’ve made it easy to spin up and customize microservices across the entire enterprise with our highly customizable Edge IoT Data Plane. Our data plane drastically reduces the amount of time, money, and effort that it takes to drive near-immediate value back to the business. It’s generic enough to be deployed in a week, but flexible enough to be rapidly adapted and extended when the time comes.
|TIME (avg)||MONEY (avg)||QUALITY (avg)|
|VENDOR PRODUCT||18 months||$250,000 annual subscription|
Fee-based adaptability and extensibility
Limited sources, modes, domains, persistence, formats, scenarios
High volume, high latency
|DFHeinz IoT Data Plane||1-3 months||$120,000 annual subscription|
No-cost adaptability and extensibility unlimited formats
Unlimited sources, modes, domains, persistence. formats, scenarios
High volume, low latency
Low risk of latency per ML-based feedback