Latifold helped SimpleCGT improve platform reliability, observability, and cost efficiency so the team could scale with confidence.
Historical cloud engineering engagement. The work, service arrangements and outcomes below describe this engagement; they are not current platform offers or performance guarantees.
In four weeks we unified tracing and logging with SLOs and error-budget alerting, introduced an incident severity and runbook model, right-sized costs with per-team dashboards, and embedded governance (weekly SLO reviews, monthly capacity planning, quarterly retros) — plus hands-on enablement.
Below is the full published narrative: situation, workstreams, deliverables, timeline, impact and ROI, and customer quote.
Amazon CloudWatch as baseline; distributed tracing and structured logging; SLOs on calculation, API, and submission paths; severity-based incident response; runbooks and post-incident reviews; compute and database right-sizing; per-team cost visibility — with a governance cadence the team can sustain.
See Measured outcomes and Impact & ROI for figures from the PDF.
Fewer P1/P2 incidents
Faster root-cause isolation
Infra cost saved
Uptime target met
SimpleCGT is a tax and compliance platform helping users calculate capital gains obligations. Growing traffic and regulatory complexity were outpacing the team’s ability to maintain uptime, diagnose issues, and control infrastructure spend.
No SLOs, alerting based on raw thresholds, frequent P1/P2 incidents during peak filing periods.
Basic CloudWatch metrics only — no distributed tracing, limited structured logging, slow root-cause isolation.
Ad-hoc response with no severity model, unclear escalation paths, and no post-incident learning process.
Over-provisioned compute and database instances, no per-team spend visibility, costs growing faster than traffic.
Deployed distributed tracing and structured logging across core services, then defined service-level objectives tied to real user-facing outcomes — calculation accuracy, API response time, and submission success rate. Rebuilt alerting around error budgets so the team gets a clear signal when user experience degrades, not when infrastructure metrics fluctuate.
Introduced severity criteria aligned to business impact, on-call escalation paths, and a post-incident review framework with corrective-action tracking. Reduced mean time to recovery by giving responders structured runbooks and clear ownership from detection through resolution.
Right-sized compute and database instances across environments, removed idle resources, and introduced per-team cost dashboards with monthly review cadences — cutting infrastructure spend while maintaining headroom for peak filing periods.
Embedded an operating cadence — weekly SLO reviews, monthly capacity planning, and quarterly reliability retrospectives — so the team could sustain the model without ongoing external support.
SLO framework — service-level objectives for calculation, API, and submission paths with error-budget tracking and alerting.
Observability platform — distributed tracing, structured logging, and application-level metrics across all core services.
Incident operations playbook — severity model, escalation paths, runbooks, and post-incident review framework with corrective-action tracking.
Cost & capacity dashboards — per-team infrastructure spend visibility with right-sizing recommendations and monthly review cadence.
Platform governance model — weekly SLO reviews, monthly capacity planning, and quarterly reliability retrospectives embedded into the team’s workflow.
Internal enablement workshops — hands-on sessions covering observability, incident response, and cost controls so the team could operate independently.
Four weeks from discovery to full operating-model handoff, with SLO alerting and observability improvements live by week two.
Service dependency mapping, failure-mode inventory, SLO target definition, and observability gap analysis.
Tracing and structured logging deployed, SLO alerting live, incident severity model and runbooks rolled out to on-call teams.
Compute and database right-sizing, idle-resource removal, per-team cost dashboards deployed, and incident playbooks tested.
Operating cadence embedded, enablement workshops delivered, and full knowledge transfer to the SimpleCGT team.
SimpleCGT hit its 99.9% uptime target through peak filing season — the period where downtime directly costs revenue. The 22% infrastructure cost reduction and 50% drop in P1/P2 incidents started paying back the engagement within weeks, and the governance model means the gains compound rather than erode. Hiring an SRE team, deploying observability, and building an incident response model internally would have taken months — and the platform would have been unprotected through at least one more filing season.
| Dimension | Typical internal path | With Latifold |
|---|---|---|
| Timeline | 3–5 months to hire SRE capacity, deploy tracing, define SLOs, and build incident response. | 4 weeks — SLO alerting live by week two, full model handed off by week four. |
| Engineering effort | 3–5 person-months (≈ 480–800 hours) of senior engineers diverted from product. | Engineering team stayed on product — reliability work ran in parallel. |
| Fully-loaded cost | Roughly €40k–€90k in engineering time, plus risk of another unprotected filing season. | Engagement paid for itself through 22% infra savings and avoided incident costs. |
Figures shown are typical ranges for comparable work and will vary by baseline maturity, constraints, and team size.
“Latifold helped us scale reliability and observability without slowing down. We now diagnose issues faster and plan capacity with confidence.”
Director of Platform, SimpleCGT
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