According to a recent LinkedIn post from Sift, the company is emphasizing performance limitations in common time-series databases such as InfluxDB and Prometheus when used with Grafana for large-scale hardware telemetry. The post suggests these tools can struggle when correlating thousands of data channels over long test periods, leading to slow dashboards and reduced reliance on data-driven decision-making.
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The post highlights a new Grafana plugin that retains the familiar user interface while substituting a backend that Sift presents as designed for high-volume hardware telemetry workloads. According to the post, this approach aims to support millions of channels, low-latency queries, in-browser analytics such as FFTs and derivatives, and to reduce the need for CSV exports to external tools.
As shared in the post, customers including K2 Space Corporation and Astranis Space Technologies are already described as operating with this architecture. For investors, references to these space-focused users may indicate early traction in aerospace and advanced hardware verticals, segments that typically demand high reliability and are willing to pay for specialized observability infrastructure.
If Sift’s plugin sees broader adoption among hardware-intensive enterprises facing similar telemetry scale issues, it could deepen the company’s role in mission-critical test and monitoring workflows. That positioning may support pricing power, stickier customer relationships, and opportunities for incremental services or usage-based revenue, although the post does not provide details on monetization, pricing, or overall market penetration.
More broadly, the focus on extending Grafana rather than replacing it may signal a strategy of integrating into existing observability stacks instead of competing head-on with entrenched tools. For the observability and test-data management market, this type of specialized backend could contribute to a trend toward domain-specific data infrastructure, potentially creating a niche for Sift in high-volume hardware telemetry where generic time-series databases encounter scaling constraints.