At real scale, most telemetry is never looked at, but it all gets stored and paid for anyway. SignalControl is Dash0’s ingestion pipeline: it filters, samples, and aggregates telemetry the moment it arrives, before anything is stored, so you keep the signal and stop storing the noise. It is included with every account from today. There is nothing to buy or enable, and until you create a rule, everything passes through untouched.
What it does
Spam filters. Drop known noise at ingestion, for every signal type. Each filter charts exactly what it catches over time, so you always know which rules are earning their keep.
Distributed tail sampling. You choose what to keep and what to sample out, and the decision is made only after the whole trace is visible. What makes Dash0’s version different is that it works no matter where the trace’s spans land: different collectors, even different clusters. Tail sampling normally requires every span of a trace to pass through the same collector so it can be judged as a whole, which means re-plumbing your pipeline and makes that collector the bottleneck at exactly the scale where sampling matters most. Dash0 separates the sampling decision from the collectors themselves, so a whole fleet reaches one consistent keep-or-drop decision per trace, whatever your topology. The typical setup keeps every error, outlier, and slow request and samples out routine successes, but the rules are yours, and changes apply live with no collector restart.
RED metrics before sampling. Rate, error, and duration metrics are computed from 100% of spans before sampling drops anything, whether sampling runs at the Edge or in Dash0's cloud. If metrics came only from the traces you keep, dashboards would show the exceptions, not the truth. Sampling changes which traces you keep, never what your dashboards say.
Signal to metrics. Convert chosen attributes of spans and logs into metric series: filter the signals you care about, pick the attributes to carry through, and get latency histograms from spans and counters from logs. Computed before sampling, so the metrics reflect all traffic.
Time series aggregation. Aggregate metrics temporally (downsample 15-second scrapes to the resolution you actually chart) or spatially (collapse per-pod series into one per-service series), with the aggregate staying exact. Runs in Dash0’s cloud after ingestion, where metrics from every source converge.
Everything SignalControl drops is visible as kept versus dropped, per rule, so reduction is something you verify rather than trust.
Two ways to run it
SignalControl Managed runs in Dash0’s cloud. Send telemetry as you do today; nothing to deploy.
SignalControl Edge runs in your own Kubernetes cluster, as a gateway behind your existing central collector. Telemetry is enriched, filtered, measured, and sampled before it leaves your network, which also cuts egress. Nothing upstream of it changes, and your existing destinations keep receiving exactly what they receive today.
Either way, rules are managed in Dash0, in the UI or as code, and changes apply live with no restarts.
With the new ingestion and storage pricing, signals pay a small ingestion fee and storage prices apply only to what you keep, so what SignalControl drops never hits the storage line of your bill. Existing accounts move to the new pricing at the start of a billing period, never mid-period; see the pricing page for rates.