ChartlessOps
Use cases

Four shapes of real teams.

Four common stack shapes our customers run. Each comes with a recipe (which signals to watch, which integrations to wire, which SLOs to define).

For e-commerce

The checkout funnel page.

For e-commerce teams who know exactly which 6 services matter during an incident. Browse, search, cart, checkout, payment, fulfilment.

  • Per-service signals tuned to checkout conversion impact
  • Payment provider failure-rate signal pulled from Stripe/Adyen webhooks
  • SLO budget calibrated to revenue tolerance
  • Black-Friday-readiness mode: stricter thresholds for the week
// trasselsudd, e-commerce. 6 services, 17 dashboards replaced, MTTI 8 min → 30s.
trasselsudd / checkout
all ok · 12s
Browse
p95 84ms
within
Search
p95 142ms
within
Cart
0.02% err
within
Checkout
0.01% err
within
Payments
99.98% ok
within
Fulfilment
2.1k/h
within
harbour.io / production
1 degraded · 8s
API
p99 84ms
within
Auth
p50 12ms
within
Webhooks
99.95%
within
DB primary
p99r 4ms
within
Workers
2.4k bk
burning
CDN
4.2 Gb/s
within
For SaaS

The customer-facing-API page.

For SaaS teams whose product is the API. Latency, error rate, webhook delivery, auth availability. The signal is whatever’s in the SLA.

  • p99 latency + error rate per public endpoint
  • Webhook delivery success rate to customer endpoints
  • Auth service availability (the single point of failure)
  • Worker queue depth (signal that customer integrations are catching up)
// harbour.io, B2B SaaS. SLA in customer contracts; ChartlessOps page is the source of truth.
For fintech / payments

The payment-rail page.

For payments and fintech teams. Per-rail success rates, per-provider error patterns, fraud-rule fire rates, ledger consistency.

  • Per-payment-rail success rate (cards, ACH, SEPA, faster payments)
  • Per-provider error patterns (Stripe, Adyen, GoCardless …)
  • Fraud rule fire rates — flag spikes that indicate attack
  • Ledger consistency checks running every 5 min
  • Audit log of every alert + ack + resolution
// pinion labs, fintech. Audit-ready alerting; signals tied to FCA reportable incident definitions.
pinion / payment rails
all ok · 4s
Cards (Stripe)
99.98%
within
Cards (Adyen)
99.96%
within
ACH (Modern Treasury)
99.99%
within
SEPA Instant
99.91%
within
Faster Payments
99.94%
within
Ledger consistency
0 drift
within
stackseven / platform
all ok · 11s
api-eu · FRA
p99 78ms
within
api-us · IAD
p99 112ms
within
api-apac · SIN
p99 134ms
within
db-replica · FRA
lag 4ms
within
db-replica · IAD
lag 12ms
within
k8s clusters (12)
100%
within
For multi-region B2B platforms

The multi-region rollup.

For platforms running in 3+ regions where the right signal is “is the worst region still OK?”. Rollup-by-default, drill-in on demand.

  • Per-service rollups across all regions (worst region drives the status)
  • Per-region drill-in when you click into a service
  • Replication-lag signal for cross-region replicas
  • Kubernetes cluster health rolled into one platform signal
  • Multi-region SLO budgets (per-region + aggregate)
// stack/seven, B2B platform. 3 regions, 28 services rolled into 12 signals.

Recognise the shape?

Each starter template above maps directly to one of these stack shapes. Pick one when you sign up and you’ll get a pre-wired panel in about 8 minutes.

Start the trial Data sources