Regain control over your production with complete observability
Why observability has become critical for your business
Without visibility into production, every deployment is a gamble. Symptoms accumulate:
Technical overview
Observabilité par parcours e-commerce
Instrumentation bout-en-bout du parcours utilisateur avec corrélation front-to-back
Which observability stack to choose?
The choice depends on your infrastructure, budget, and desired level of autonomy. We recommend the most suitable solution.
Datadog
- All-in-one platform: logs, metrics, traces, RUM, synthetics
- Exemplary UX, powerful and intuitive dashboards
- Extensive integrations (750+): AWS, GCP, Azure, K8s, etc.
- Native machine learning for anomaly detection
- High costs at scale (per host + ingestion)
- Strong vendor lock-in, difficult migration
- Complex and hard-to-predict pricing model
- Expensive data retention beyond 15 days
Grafana Stack (Prometheus / Loki / Tempo)
- Open-source, no license or vendor lock-in
- Total flexibility on architecture and retention
- Massive community, mature CNCF ecosystem
- Controlled cost: you only pay for infrastructure
- Significant operational overhead (deployment, scaling)
- Requires solid SRE/DevOps expertise
- Infrastructure to manage and monitor itself
- Less fluid log/metric/trace correlation than SaaS solutions
New Relic
- Unified platform with 30+ integrated capabilities
- AI-powered: anomaly detection and intelligent alerting
- Generous free tier (100 GB/month free ingestion)
- Powerful NRQL for data exploration
- Limited data retention on standard plans
- Per-user pricing that can climb rapidly
- Less customizable than open-source solutions
- Variable support depending on pricing tier
AWS CloudWatch + X-Ray
- Native integration with all AWS services
- No additional infrastructure to manage
- Pay-per-use model, no minimum commitment
- Service Lens for metrics/traces/logs correlation
- Limited for cross-cloud or hybrid monitoring
- Basic dashboards compared to alternatives
- Strong coupling with the AWS ecosystem
- Less advanced alerting features
No technology dogma. We recommend the solution best suited to your context, constraints and ambitions. Every choice is documented and justified.
End-to-end support, phase by phase
Each phase produces concrete deliverables. You maintain visibility and control at every step.
Existing observability audit
Assess the maturity of your current observability. Identify blind spots, untapped data sources, and real costs of your monitoring stack.
- Inventory of monitoring tools in place (APM, logs, infra)
- Data flow and metrics source mapping
- Existing instrumentation coverage analysis
- Current cost evaluation (licenses, storage, ingestion)
- Blind spot identification: unmonitored services
- Existing alert audit (noise, relevance, response time)
- Observability maturity benchmark (levels 1 to 5)
- Prioritized recommendations and quick wins identified
Target monitoring architecture — 3 pillars
Design the observability architecture around the 3 fundamental pillars: Logs (context), Metrics (trends) and Traces (flows). Define SLOs and alerting strategy.
- Target 3-pillar architecture: logs, metrics, distributed traces
- Technical stack selection and justification
- Data collection and ingestion strategy
- SLI/SLO definition per critical service
- Operational and business dashboard design
- Multi-level alerting strategy (P1 to P4)
- Retention plan and data storage policy
- Application instrumentation architecture (OpenTelemetry)
Implementation & instrumentation
Deploy the observability stack and instrument your applications. Set up structured log collection, custom metrics, and distributed tracing.
- Observability stack deployment (agents, collectors)
- OpenTelemetry application instrumentation (auto + manual)
- Exporter and data pipeline configuration
- Structured logging setup (JSON, levels, context)
- Cross-service distributed tracing deployment
- Infrastructure metrics configuration (CPU, RAM, network, I/O)
- Business metrics integration (orders, cart, conversion)
- End-to-end testing on staging environment
Dashboards, alerting & SLO
Create operational and business dashboards, configure intelligent alerting, and set up SLO tracking with error budgets.
- Operational dashboards per service and team
- Executive dashboard: SLO, availability, global performance
- Business dashboard: conversion, journey latency, Core Web Vitals
- Multi-channel alerting configuration (Slack, PagerDuty, email, SMS)
- SLO setup with error budgets and burn rate alerts
- Automated runbooks for recurring incidents
- FinOps dashboard: cloud costs per service and environment
- Team training on tools and on-call rituals
Performance optimization & FinOps
Continuously optimize application performance and infrastructure costs. Leverage observability data to drive technical and business decisions.
- Weekly performance review (Core Web Vitals, latency, errors)
- Continuous cloud cost optimization (right-sizing, reserved, spot)
- Proactive trend analysis and capacity forecasting
- Progressive alerting noise reduction (signal/noise ratio)
- Technical performance / business impact correlation (revenue)
- Monthly FinOps reports with optimization recommendations
- Continuous instrumentation evolution (new services, features)
- Knowledge transfer and operational documentation
What you concretely gain
Expected results
Proactive incident detection
MTTR reduced by 60 to 80%
Continuously optimized performance
Proactive incident detection
Identify issues before they impact your users. Intelligent alerting based on anomalies, not static thresholds.
MTTR reduced by 60 to 80%
Distributed tracing, correlated logs, contextual dashboards — your teams find the root cause in minutes, not hours.
Continuously optimized performance
Green Core Web Vitals, controlled P99 latency, monitored conversion tunnels — every millisecond gained translates to revenue.
Total visibility on cloud costs
FinOps dashboard per service, per environment. Identify oversized resources and optimize your cloud spending by 20 to 40%.
Guaranteed SLO/SLA compliance
SLI/SLO defined per service, error budgets tracked in real time, burn rate alerts — meet your commitments with reliable data.
Data-driven decisions
Technical performance / business impact correlation. Prioritize your optimizations on the journeys that generate the most value.
They trusted us with this type of engagement
Christian Louboutin
Complete monitoring stack implementation on Azure. Performance dashboards, multi-level alerting, e-commerce SLO tracking, cloud cost optimization.
Kering — Boucheron
Multi-zone observability (AWS + AliCloud) for APAC and WW e-commerce. Cross-region distributed tracing, Kubernetes operational dashboards, PagerDuty alerting.
Truffaut
AWS infrastructure monitoring for Magento + Mirakl e-commerce platform. Performance metrics, marketplace monitoring, FinOps dashboards and cost optimization.
Your questions, our answers
01 What is the difference between monitoring and observability?
02 How long does it take to set up a complete observability stack?
03 Do I need to instrument all my code to benefit from observability?
04 How to control the costs of an observability solution?
05 What is an SLO and why do I need one?
06 Can I migrate from an existing monitoring solution without interruption?
Ready to gain clarity on your production?
Free 30-minute observability diagnostic. We assess your monitoring maturity and identify quick wins — no commitment.