According to a recent LinkedIn post from Sift, the company is emphasizing performance limits in traditional time-series databases such as InfluxDB and Prometheus when supporting large-scale hardware telemetry in Grafana. The post suggests that these systems may struggle with thousands of channels over long test periods, potentially causing slow dashboards and less data-driven engineering decisions.
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The post highlights Sift’s new Grafana plugin, which is presented as retaining Grafana’s familiar interface while replacing the underlying data engine with infrastructure designed for high-volume telemetry analytics. According to the post, this includes support for millions of channels, low-latency queries, in-browser FFTs and derivatives, and reduced reliance on CSV export to external tools.
Sift’s post also notes that aerospace-focused firms K2 Space Corporation and Astranis Space Technologies are already using this approach for their workflows. For investors, this suggests Sift may be gaining traction in high-value, data-intensive hardware and space sectors, potentially expanding its addressable market and reinforcing its positioning as a specialized analytics provider for complex telemetry use cases.
If adoption broadens beyond early users in space technology, Sift could benefit from recurring software revenue tied to critical engineering infrastructure and observability stacks. The focus on integrating with widely used Grafana tooling may also lower switching costs for customers, which could support faster go-to-market execution and strengthen the company’s competitive stance against generic time-series database solutions.