Find out what broke before your customers do
Monitoring tells you something is wrong; observability lets you ask why without shipping new code. Together they replace the worst way to learn about an outage — a customer email — and the second worst: an alert channel so noisy that everyone muted it months ago.
Willowark builds observability from the questions backwards. What does healthy mean for this system? What would you need to see at 3 a.m. to fix it fast? We instrument to answer those questions — typically with OpenTelemetry feeding Prometheus, Grafana, and a log pipeline — rather than collecting everything and hoping dashboards emerge.
Cloud & InfrastructureHow the work gets done
The same way every time: scope, build, hand over.
Implementation covers the three signals: metrics with meaningful labels and recording rules, structured logs with consistent fields and sane retention tiers, and distributed traces that follow a request across service boundaries. On top sit SLOs — explicit targets for availability and latency — and alerting built on burn rates, so pages fire on threatened objectives instead of every CPU blip. For industrial clients, the same stack extends to machine and sensor data, where a gap in the data is itself a signal worth alerting on.
Success has a sharp test: during the next incident, does the on-call engineer go from page to probable cause in minutes using the dashboards alone? We also track alert quality over time — every page should be actionable, and anything that is not gets tuned or deleted. A quiet pager and a fast mean time to diagnosis are the point; dashboards are just how you get there.
Scoping starts with the last few incidents. We reconstruct how each was detected, how long diagnosis took, and what information would have shortened it — that gap defines the first instrumentation to add. From there we weigh the practical trade-offs: how much cardinality the metrics can carry before cost balloons, how long logs need to be searchable versus merely retained, what sampling rate keeps traces useful without storing every request. We also decide what goes into the initial SLOs; a small number of objectives that the business actually recognizes beats a comprehensive list nobody reads. Retention tiers and data volume get costed before anything is deployed.
Observability rots when nobody owns it: dashboards multiply, alerts are added after every incident and never removed, and instrumentation drifts as services change. We design against this by keeping dashboards and alert rules in version control alongside the code, tying every alert to a runbook entry, and scheduling a recurring review that prunes what did not earn its place. Handover means your on-call engineers have used the dashboards in a simulated incident and found them sufficient, and your developers know how to add instrumentation to a new service without asking us. Success is confirmed at the next real incident, not at the demo.
Scope it in writing
What we agree before work starts
- Instrumentation with OpenTelemetry across services
- Metrics, logging, and tracing stack (Prometheus/Grafana or managed equivalent)
Build with checkpoints
Working results, not slide decks
- SLO definitions with burn-rate alerting
- Runbook-linked dashboards built for on-call use
Hand over something you own
Documentation, source, and training
- Alert audit that eliminates non-actionable pages
- On-call training session with a simulated incident run against the new dashboards and alerts
Sound familiar?
Where monitoring & observability earns its keep.
An alert channel with 200 daily messages that everyone has muted
Outages that customers report before any internal system does
A microservices app where finding the slow service takes half a day
A plant data pipeline that fails silently and gets noticed at month-end reporting
Ask about Monitoring & Observability
Describe the problem. Get a straight answer.
One line is enough. An engineer replies within a business day.
Related work
AI operating software for training operations
Components:
- Schedulers (training ops): The people running the training operation.
- Operating software (AI assistance): The operating software, with AI assistance built in.
- Cloud services (distributed): Distributed cloud services behind the application.
- Training game (learning): The training game the software connects to.
Connections:
- Schedulers to Operating software
- Operating software to Cloud services
- Operating software to Training game
A global automotive manufacturer's training operation · Automotive
AI operating software for training operations
AI operating software for the manufacturer's training schedulers — removing manual scheduling labor and logistics tracking, saving hundreds of hours per year — plus a training video game built to help trainers perform better. Used across the manufacturer's training organization.
Read the case study →Common questions
Asked before every monitoring & observability project.
We already have dashboards. What are we missing?
Usually the connection between dashboards and action. The common gaps are alerts on causes rather than symptoms, no tracing to follow a request across services, and no definition of what performance is acceptable. An observability review maps what you would actually need in the next incident against what exists.
Managed observability platform or self-hosted?
It is a cost-shape question. Self-hosted Prometheus and Grafana are excellent and cheap at moderate scale but cost engineering time; platforms like Datadog or Grafana Cloud trade money for that time and get expensive with high-cardinality data. We model both against your data volume before recommending.
How do you stop alert fatigue from coming back?
By making it structural. Alerts tie to SLOs, every page requires a documented action, and a recurring review retires alerts that fired without being useful. Fatigue returns when alerts are free to create and nobody owns pruning them — so we make pruning part of the process.
Will adding instrumentation slow down our application?
Done properly, the overhead is small enough that it is not a factor in most systems. OpenTelemetry SDKs are designed for production use, traces are sampled rather than captured in full, and metrics are aggregated in memory before export. The exceptions are very high-throughput hot paths, where we measure the cost explicitly and reduce or sample more aggressively. We would rather instrument a little less than have the tooling itself become a performance question.
Can you monitor industrial equipment and plant data as well as software?
Yes — the same stack handles it well. Sensor and machine data from PLCs, gateways, or a historian can be brought into Prometheus or a time-series database and displayed in Grafana alongside application metrics, which gives operations and engineering one place to look. The alerting logic differs: for plant data, a missing reading or a flat line often matters more than a threshold breach, so we build detection for absence and staleness explicitly.
Where this sits
Monitoring & Observability, inside a cloud & infrastructure system.
The lit component is the part of the system this service delivers; the rest is what it has to work with.
Hover or focus a component to see what it is and what it talks to. Arrow keys move between them.
Plant systems reach the cloud over an encrypted tunnel; services run in containers over managed data stores, and monitoring alerts a person before a customer notices.
Components:
- Plant network: OT network, segmented from IT.
- Tunnel (VPN / private link): Encrypted, allow-listed, no inbound ports on the plant side.
- Services (containers): Deployed from CI, rolled back in one command.
- Data stores (DB, objects): Managed database and object storage with backups tested, not assumed.
- Monitoring (logs, metrics): Health, latency, error rate, cost.
- On-call: Someone is paged before a customer emails.
Connections:
- Plant network to Tunnel over VPN
- Tunnel to Services over MQTT
- Services to Data stores over SQL
- Services to Monitoring over events
- Monitoring to On-call over push
Strategy. Software. Systems.
Have a system that should exist?
Tell us what your operation is doing manually, what isn't connected, or what you're trying to build. We'll tell you plainly whether and how we can help.