01
Estate intake and sizing
Start with the shape of the customer estate. Hosts, clusters, pods, services, retention, and signal posture are converted into node size, replicas, storage, sampling, and cost-control defaults.
NewSignalCost Platform Pack is being prepared for guided AWS deployment.
Interested to deploy?Well-architected observability, packaged
A production observability platform, handcrafted into a deployable pack.
Built from real operating patterns: collect close to workloads, control telemetry before storage, keep incidents tied to evidence, and scale from the actual estate instead of a generic chart default.
1
Collect at the edge
Agents sit near workloads, normalize signals early, and keep noisy data from reaching expensive storage paths.
2
Control before storage
Filtering, sampling, redaction, routing, and enrichment happen before the backend is asked to retain anything.
3
Operate one platform
Logs, metrics, traces, incidents, runbooks, and RCA live in one deployed operating model, not a pile of disconnected charts.
4
Scale from the estate
Sizing starts from EC2s, clusters, pods, services, retention, and signal posture, then becomes deployment variables.
Architecture diagram
One signal path, from estate to action.
Functional view only: the implementation is packaged, but the operating model stays understandable.
01
Customer estate
- Kubernetes clusters
- VM and host fleet
- Cloud service logs
02
Collection layer
- Host and workload agents
- Application signal capture
- Cloud log receivers
03
Telemetry control
- Redaction
- Sampling
- Filtering
- Routing
04
Platform core
- Query and retention
- Dashboards
- Alert evaluation
- Incident records
05
Operations layer
- AIOps investigation
- RCA drafts
- Runbook context
- Guarded remediation
Functional architecture
The platform is wired as an operating system for observability.
The deployment pack is intentionally opinionated. Each layer has a job, a safety boundary, and a scaling knob.
02
Agent and collection layer
Kubernetes and VM agents collect logs, host data, application RED metrics, and traces. A first useful picture appears before every team has instrumented code.
03
Telemetry gateway
All signals pass through a gateway that batches, redacts, samples, enriches, and routes data. This is where cost discipline is enforced.
04
Query and retention backend
The backend is deployed with object storage, cache volumes, retention presets, and replicas sized to the estate rather than guessed from a default tutorial.
05
Alerts and incident flow
Starter alerts cover application errors, latency, cloud logs, crash loops, and telemetry pipeline health. Incidents carry runbook context and route to the right humans.
06
AIOps investigation and RCA
The AI investigation layer gathers context, checks recent changes, queries infrastructure, drafts RCA, prepares handoff notes, and keeps remediation behind guardrails.
Why it is different
It is not a list of tools. It is a deployment opinion.
Most teams can install components. The hard part is knowing where to put the control points, which defaults avoid runaway cost, how incidents should inherit evidence, and what should scale first. That judgement is packaged here.
Day-one data flow from Kubernetes, VMs, and cloud logs
Cost controls before retention, not after the bill arrives
A sizing overlay that changes deployment scale from real estate inputs
Alert templates with incident context and runbook links
AI-assisted investigation with human-controlled remediation
A licensed pack with SignalCost metadata and update path
Deployment path
Share your starting point.
We only need enough context to size the first deployment conversation: estate shape, retention, current pain, and timing. The request is forwarded to [email protected].