Observability & Performance · Paris & France

Regain control over your production with complete observability

Your teams navigating production blind? Incidents detected by customers, firefighting-mode debugging, uncontrolled cloud cost growth. We build your observability stack - logs, metrics, traces - so you shift from reactive to proactive.

Discuss your project See the methodology
They trust us

The challenge

Why observability has become critical for your business

Without visibility into production, every deployment is a gamble. Symptoms accumulate:

  1. 01 Incidents detected by customers before your technical teams
  2. 02 Production debugging that takes hours due to lack of distributed tracing
  3. 03 Invisible performance degradation: rising latency, declining conversion
  4. 04 Core Web Vitals in the red, SEO and user experience impact
  5. 05 Cloud costs growing +30% per year without per-service visibility
  6. 06 No SLOs defined: impossible to know if service quality is being met
  7. 07 Noisy and non-actionable alerting - widespread alert fatigue
  8. 08 No correlation between technical performance and business impact
Architecture

Technical overview

Observabilité par parcours e-commerce

Instrumentation bout-en-bout du parcours utilisateur avec corrélation front-to-back

Parcours utilisateur
Stockage & services tiers
Observabilité
Utilisateur
FrontWeb / App
CDN / WAF
API / BFF
ServicesMicroservices
Base de données
RechercheElasticsearch, Algolia
Paiement (PSP)Stripe, Adyen
RUM / Web VitalsPerformance front
Logs structurésJSON, corrélation
Traces distribuéesOpenTelemetry
Metrics & SLOSLI, error budgets
Source
Traitement
Service
Stockage
Couche

Solution comparison

Which observability stack to choose?

The choice depends on your infrastructure, budget, and desired level of autonomy. We recommend the most suitable solution.

Datadog

Datadog

Strengths
  • 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
Limitations
  • 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
Ideal for: Scale-ups and enterprises seeking a turnkey solution with dedicated budget
Grafana Stack (Prometheus / Loki / Tempo)

Grafana Stack (Prometheus / Loki / Tempo)

Strengths
  • 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
Limitations
  • 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
Ideal for: Mature DevOps teams, constrained budgets, desire for total control
New Relic

New Relic

Strengths
  • 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
Limitations
  • 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
Ideal for: Mid-size teams, fast observability start, controlled budget
AWS CloudWatch + X-Ray

AWS CloudWatch + X-Ray

Strengths
  • 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
Limitations
  • Limited for cross-cloud or hybrid monitoring
  • Basic dashboards compared to alternatives
  • Strong coupling with the AWS ecosystem
  • Less advanced alerting features
Ideal for: 100% AWS infrastructures, lean teams, zero-overhead start
No technology dogma. We recommend the solution best suited to your context, constraints and ambitions. Every choice is documented and justified.

Our methodology

How we deliver

Architects with twenty years behind them, and a delivery process instrumented by AI: scoping, code review, quality and security tooled at every phase. Each step produces concrete deliverables, and we stay through the run.

01
1 to 2 weeks

Existing observability audit

Assess the maturity of your current observability. Identify blind spots, untapped data sources, and real costs of your monitoring stack.
Deliverables
  • 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
02
2 to 3 weeks

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.
Deliverables
  • 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)
03
3 to 6 weeks

Implementation & instrumentation

Deploy the observability stack and instrument your applications. Set up structured log collection, custom metrics, and distributed tracing.
Deliverables
  • 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
04
2 to 3 weeks

Dashboards, alerting & SLO

Create operational and business dashboards, configure intelligent alerting, and set up SLO tracking with error budgets.
Deliverables
  • 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
05
Ongoing

Performance optimization & FinOps

Continuously optimize application performance and infrastructure costs. Leverage observability data to drive technical and business decisions.
Deliverables
  • 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

Business value

What you concretely gain

Expected results

Proactive incident detection

MTTR reduced by 60 to 80%

Continuously optimized performance

  1. 01

    Proactive incident detection

    Identify issues before they impact your users. Intelligent alerting based on anomalies, not static thresholds.

  2. 02

    MTTR reduced by 60 to 80%

    Distributed tracing, correlated logs, contextual dashboards - your teams find the root cause in minutes, not hours.

  3. 03

    Continuously optimized performance

    Green Core Web Vitals, controlled P99 latency, monitored conversion tunnels - every millisecond gained translates to revenue.

  4. 04

    Total visibility on cloud costs

    FinOps dashboard per service, per environment. Identify oversized resources and optimize your cloud spending by 20 to 40%.

  5. 05

    Guaranteed SLO/SLA compliance

    SLI/SLO defined per service, error budgets tracked in real time, burn rate alerts - meet your commitments with reliable data.

  6. 06

    Data-driven decisions

    Technical performance / business impact correlation. Prioritize your optimizations on the journeys that generate the most value.

Client references

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.

Frequently asked questions

Your questions, our answers

01 What is the difference between monitoring and observability?
Monitoring tells you "something is wrong" via alerts on predefined thresholds. Observability goes further: it lets you understand "why" through the correlation of three pillars - logs, metrics and traces. With good observability, you can diagnose problems you hadn't anticipated.
02 How long does it take to set up a complete observability stack?
8 to 14 weeks for a full implementation (audit + architecture + deployment + dashboards). First results are visible by week 3-4 with agent deployment and initial dashboards. Continuous optimization follows over the long term.
03 Do I need to instrument all my code to benefit from observability?
No. OpenTelemetry auto-instrumentation covers 70-80% of needs without modifying your code. We then add targeted manual instrumentation on critical journeys (checkout, payment, search) to obtain relevant business metrics.
04 How to control the costs of an observability solution?
Three main levers: 1) Intelligent trace sampling (tail-based sampling), 2) Adapted retention policy by data type (hot/warm/cold), 3) Source-level filtering to collect only useful data. We size the solution for your budget, not the other way around.
05 What is an SLO and why do I need one?
An SLO (Service Level Objective) is an internal service quality target - for example "99.9% availability" or "P95 latency < 200ms". Unlike an SLA (contractual commitment), the SLO serves as a steering tool: thanks to the error budget, you know exactly when to prioritize reliability over new features.
06 Can I migrate from an existing monitoring solution without interruption?
Yes. We set up the new stack in parallel with the existing one, with a double-run period to validate coverage and reliability. The switchover happens progressively, service by service, without any production monitoring interruption.

Ready to gain clarity on your production?

Thirty minutes with an architect - the person who will scope your project, and ship it. We assess your monitoring maturity and identify quick wins - no commitment.

Schedule a call See all our solutions