Services · RUN
See your system. All of it. In time.
Prometheus, Grafana, OpenTelemetry, Loki, Tempo, Datadog — the stack matters less than the discipline. We build the discipline.
How we deliver this: AI handles the routine analysis (audits, IaC drafts, runbook scaffolds, alert triage). A senior engineer reviews every change before it touches your production. Consultancy speed at consultancy quality.
Read more →When you need this
Dashboards that nobody trusts
When the dashboard says green and customers are tweeting red, the dashboard isn't observability. We rebuild signals around user-visible outcomes, not box health.
Tracing that ends at the load balancer
Distributed traces that don't follow a request through every hop are a partial answer at best. We instrument the gaps — async jobs, third-party calls, ML inference — until "where did the time go?" has a one-click answer.
Log spend that grew faster than the company
Datadog and Splunk bills doubling year over year is fixable. Sampling, tiering, structured-log discipline, and selective indexing typically cut spend 50–70% without losing investigative power.
How it works
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Phase 01
Signal audit
Two-week deep dive into your current dashboards, alerts, and trace coverage. We document what users actually feel vs. what your monitoring measures.
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Phase 02
SLI / SLO design
Service-by-service SLI selection (availability, latency, correctness) and SLO targets that drive decisions, with error budgets that ship to product and engineering jointly.
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Phase 03
Instrumentation uplift
OpenTelemetry-first auto-instrumentation, structured logs with consistent fields, RED/USE method dashboards, and trace propagation through every async hop.
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Phase 04
Alert hygiene + runbooks
Every page comes with a runbook. Alerts on symptoms, not causes. Page volume drops 50–80% in the first month after the cleanup pass.
What you get
- → SLI/SLO definitions for top services (with error-budget burn dashboards)
- → Golden-signal dashboards per service (Grafana or Datadog)
- → OpenTelemetry instrumentation rollout plan
- → Alert library with linked runbooks
- → Log/metric/trace cost report with prioritized savings
What changes for you
Quieter pagers
Most clients see a 50–80% reduction in page volume in month one — without missing real incidents.
Investigation in minutes, not hours
Traces that follow requests end-to-end and dashboards that answer the next question turn 4-hour incident reviews into 20-minute ones.
Observability cost under control
Sampling discipline, tiered storage, and aggressive log structuring usually cut spend 50–70% — money you can put into reliability work.
Vendor-flexible architecture
We build OpenTelemetry-first so you're not locked into a single vendor. Switching from Datadog to Grafana Cloud (or back) becomes a config change, not a project.
SLO conversations product can join
Error budgets give product, engineering, and SRE a shared language for trading off velocity vs. reliability.
Faster incident commander training
Standardized dashboards mean any engineer in rotation can drive an incident response, not just the senior on-call.
What clients say
"CloudWizz rebuilt our delivery pipeline in eight weeks. Deploys went from a Friday-night ritual to a non-event we ship four times a day."
Director of Engineering
Fintech, Series C · 2025-11
"They turned a CFO emergency into a board-ready story in 12 weeks. The dashboards alone changed how engineering thinks about cost."
VP Engineering
Series B SaaS · 2026-01
Frequently asked questions
Datadog, Grafana, New Relic — does it matter which? +
Less than vendors say. The discipline matters more than the toolset. We work with what you have unless there's a clear gap.
How do you handle high-cardinality metrics? +
Aggressive cardinality budgets, selective recording rules, and where appropriate, separating hot/cold metric storage. The expensive habit is unbounded labels — we kill those first.
Can you migrate us off Splunk / Datadog to OSS? +
Yes. Common migrations are Splunk → Grafana Loki, Datadog → Grafana Cloud or Prometheus + Tempo. We design the migration, run it in parallel, and validate parity before cutover.
How do you handle observability for AI/LLM workloads? +
Standard signals (latency, throughput, error rate) plus LLM-specific ones — token-cost-per-request, cache hit rate, eval scores. Langfuse or Phoenix sit alongside the standard stack.
Do you cover synthetic monitoring? +
Yes — k6, Datadog Synthetics, or Checkly depending on your stack. Synthetic checks are part of the SLO definition, not a separate concern.
How long is a typical engagement? +
8–12 weeks for the assessment + first wave of instrumentation. Many clients retain a one-or-two-day-a-week advisory after.
Do you write runbooks for us? +
We provide templates plus the first 5–10 written together with your engineers. Long-term, runbooks belong with the team that owns the service.
Can you help reduce alert fatigue? +
Yes — alert hygiene is a standard part of every observability engagement. The data tells you which alerts page humans without producing actionable work.
What about chaos engineering? +
Useful — once SLOs and observability are in place. Chaos before observability is just breaking things.
How do you measure success? +
Page volume, MTTR, error-budget consumption, and observability spend. Each becomes an SLI on the engagement itself.