In this time of soaring telemetry data and observability bills, there’s constant pressure on development and engineering organizations to reduce the telemetry – metrics, logs and traces, being sent to their observability backends and tooling of choice. This can include engineering spikes or projects to optimize the telemetry being sent, taking time away from other critical roadmap items and business initiatives. And when an incident inevitably does occur, teams often find themselves missing key information they need to drive mitigation, root cause analysis and resolution. The cycle repeats.

This behavior also disincentives our development and engineering teams from instrumenting their code and business logic up front, in fear of “driving up the observability bill,” leading to poor potential observability insights and outcomes.

But what if we could have “our cake and eat it too?” What if we could let our development and engineering teams instrument key business applications freely as they need to, without fear of repercussion, by dynamically controlling that telemetry at runtime, ensuring the right people and observability tools always get the right information at the right time?

Observability can now go from “1-way” to “2-way,” with feedback loops and signals to ensure everyone gets the observability data they need, when they need it:

Rolling out a critical new feature or change?  Roll out a configuration with the push of a button to dial up the granularity of your logs, metrics and traces, using ControlTheory’s Elastic Telemetry Pipelines (™) or target them at a particular application or service.  If everything looks good, dial your telemetry back down on demand to keep observability costs low – all without any code changes.

In the middle of a critical incident and need more information?  Simply roll out a configuration change to your fleet to dial up the (targeted) information or granularity you need to facilitate faster root cause, and dial it back down when you’re done.  Forgot to change it back?  No worries – ControlTheory’s proactive monitoring and guardrails will let you know, and keep you in control.

Control, route and transform your telemetry on your schedule – directed a set of logs to “cold storage” like AWS S3 and need to get them back?  Rehydrate them into your observability backend of choice with the touch of a button.

With support for 200+ existing tools, destinations and transformations, you can bring adaptive control to your current observability stack. And since it’s built on open, you have the peace of mind that you’re not getting locked in … again.

Regain control of your telemetry data and put it to work for you. Leverage dynamic controls to consolidate your existing observability tools, or drive the right telemetry data flows so that you can evaluate and migrate to best-of-breed (and AI-based) solutions.

Moving to OpenTelemetry based instrumentation to avoid vendor lock-in? Track, analyze and facilitate your rollout with configurations that allow you to enrich, optimize and route your telemetry to the right destinations, on your timeline.

From agile to “OODA” loops, systems get better by measuring and feeding back so we can make the right decisions and changes. With ControlTheory’s MetaMetrics(™), you can enable your platform efforts, track your observability flows, and understand the sources, applications and services driving them, and their destinations along with the real-time cost impact. Get insights into how you can enrich, aggregate and transform your data to both cut costs, but also drive better signal (and less noise) into your current observability tooling and teams.

Observability is changing – from a “1-way” transmission of data and the ensuing balancing act of high observability bills and precious development resources to make code changes to reduce them, to a “2-way” system, that encourages liberal instrumentation of your applications, and leverages feedback loops to analyze, optimize and control your existing observability to reduce costs and enable better outcomes and agility in the face of change.