Resilient Observability

Your data. Your cloud. Your AI. Our observability.

The economics of observability made sense once. You sent your telemetry to a vendor, they stored and indexed it, you paid a predictable bill and got a working platform in return. The tradeoff was real but manageable. Then AI arrived, and the bill stopped being predictable.

Every agent loop generates telemetry. Every prompt and response produces signals. Every autonomous deployment creates an observability surface that did not exist the week before. The platforms you are running were not designed for this volume. They were designed for a world where a human wrote every line of code and telemetry grew at a pace someone had modelled in a spreadsheet.

That world is gone. The pricing model is still here.

What breaks first

AThe first thing that breaks is cost. Ingestion-based pricing was calibrated for human-scale development cycles. AI-scale data volumes were not in the model. Your observability bill grows faster than your AI budget, which defeats the purpose of both.

The second thing that breaks is coverage. Vendors facing their own infrastructure load respond the way they always have: they sample. They drop data at the edges, retain less, and index selectively. You do not always know when this is happening. You find out when an incident surfaces a gap in the record, or when an agent investigation returns incomplete context, or when a compliance audit asks for data that was never kept.

The third thing that breaks is confidence. A platform that is silently dropping data under load is not a platform you can build intelligent automation on. Causal analysis requires complete records. Agent-driven investigation requires unsampled telemetry. The moment your platform starts making coverage decisions on your behalf, the intelligence you build on top of it inherits those gaps.

What resilient actually means

Resilience is not a dashboard feature or a reliability SLA. It is an architectural property. A resilient observability platform does not degrade under load. It does not sample to protect its own margins. It does not get more expensive as your AI generates more data, because the infrastructure cost does not sit between you and your telemetry.

Tsuga deploys inside your cloud. The storage and query layer handles AI-scale data volumes without the infrastructure tax that traditional platforms pass directly to your bill. There is no ingestion markup, no retention cliff, no sampling policy you did not choose. You define what gets kept. You define how long. The platform does not make those decisions for you because it is not paying for the storage.

The result is an observability estate that scales with your AI rather than working against it. More agents, more environments, more telemetry — and coverage that holds, costs that do not compound, and a data record you can actually build on.

Three things that make it work

Complete data. No sampling, no silent gaps, no vendor-managed retention policies. Your telemetry is kept in full, in your environment, at a cost that does not grow with your AI.

Predictable economics. A single transparent rate per GB of consumption, with forward-deployed engineers actively working to reduce what you process and retain. Costs fall over time. They do not compound.

A foundation for intelligence. Causal analysis, automated root cause investigation, and agent-driven workflows all require complete, unsampled data to work reliably. Resilient observability is the infrastructure layer everything else is built on.

Who this is for

Engineering and platform teams whose observability costs have started growing faster than their infrastructure, who have started noticing gaps in their data coverage they cannot fully account for, and who have reached the conclusion that a platform built for the previous era is not going to hold up under the next one.