Your engineers were hired to build hardware. Run your telemetry on Sift, and one platform carries the database, the dashboards, the analysis, and the test results, built to grow from your first prototype to a fleet in operation. Keep your team on the product, and let the platform handle every channel, test, and vehicle you add.
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Move to one platform and the everyday work gets faster, costs less to run, and opens up to more of the team.
Every team that assembles its own stack puts at least one engineer on to keep it running, tune the database, build dashboards, and fix scripts. Move that work onto one platform, and those hours go back to the hardware your customers actually pay for.
Open-source tools start free, and the cost arrives later in the engineer maintaining the pipeline, the cloud storage that climbs as telemetry grows, and the specialist who keeps the database healthy at scale. One platform folds ingestion, storage, visualization, analysis, and test results into a single contract that runs an order of magnitude more efficiently, so the bill grows with your program in a way you can plan for.
Start on a single test bench and stay on the same platform through production and into fleet operations, adding new programs, vehicles, and test stands as you grow. The same data model keeps pace the whole way (assets are devices, runs are tests, channels are sensors), so scaling up means adding telemetry while the tooling stays exactly where your team already learned it.
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Bringing Sift in from day one, K2 Space kept a small engineering team focused on spacecraft while telemetry scaled from 10 megabytes over a summer to tens of terabytes a week, with tens of thousands of channels monitored in real time at sub-second speed. Investigations that once took four to eight hours of scripting now finish in under one.
Moving fast on a platform that worked out of the box, CX2 compressed field-test analysis from days to hours. After a desert test turned up a platform fault, the team pushed telemetry to Sift, opened one shared timeline, and isolated the issue the same afternoon, on a workflow ready the day they needed it.
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Count the full cost and the picture changes. The tools are free, and the engineers maintaining them are on salary: tuning databases, building dashboards, fixing scripts, and managing storage that grows every quarter. One platform folds that work and that spend into a single contract, and it often comes in below what a team already pays once every hour and every cloud bill is counted. See how Sift is built
Keep them. The Python client returns DataFrames straight into your existing notebooks, so your analysis scripts stay exactly as they are, now reading from one shared source. Connect your dashboards through the plugin, and the panels your team trusts keep working on a backend built for high-rate telemetry. Connect your dashboards to Sift
Start in parallel. Add one line to your ingestion so telemetry flows to Sift alongside your current stack, validate the two side by side, and move over when the results give you confidence. The migration runs at your pace, with your existing workflow live the whole way. Set up your first telemetry workflow
Your telemetry keeps running. Backed by a commercially supported platform with active development, your team knows how telemetry flows within the product and where it remains searchable for whoever picks it up next. Every exploration, rule, and annotation persists in one place, ready for the next engineer to open and extend. See how Sift is built
Yes. Run Sift on-prem, in a private cloud or secure VPC, in GovCloud, or fully air-gapped for classified and low-bandwidth environments, so telemetry stays inside your network. Access controls and audit logs meet CMMC2, NIST, and SOC-2 requirements. How Sift deploys for defense programs
