Full Stack Observability Services

Overview

Full Stack Observability brings infrastructure, applications, logs, and real user experience into a single platform with one shared data model — so every layer of your environment is connected, and a problem in one place can be traced through every layer it touches. Instead of separate tools reporting separate versions of the truth, teams see automatically mapped dependencies from the network up to the customer session, with AI-driven root cause analysis across the whole stack.

Avocado designs, implements, and consolidates full stack observability on Dynatrace and Splunk, including untangling environments where overlapping tools have built up over time.

Who is this for?

You'll recognise the situation

You're running several monitoring tools that each cover one layer, so incident calls start with every team proving the problem isn't theirs.

Tracking dependencies across distributed applications and microservices is slowing down root cause analysis.

You find out about performance problems from customer complaints, not from your monitoring.

New releases regularly introduce performance regressions that aren't caught until after they've shipped.

You're mid cloud transformation and need consistent visibility across legacy, hybrid, and cloud-native estates at the same time.

Licence costs across overlapping tools keep rising, or you've bought a full stack platform and only use a fraction of it.

What's included?

The application layer, in depth

Application performance monitoring (APM)

Profile your applications to identify poor resource utilisation, errors, and unexpected crashes for faster diagnosis.

Distributed transaction tracing

Automatically trace transactions across every tier of the application to pinpoint bottlenecks and code-level issues.

Real user and synthetic monitoring

Track real user interactions and simulate key journeys to catch issues before customers are affected.

Container and microservices monitoring

Granular insight into containerised applications and microservices in cloud-native environments.

CI/CD release validation

Validate releases against real performance baselines within your pipeline, catching regressions before they reach production.

Across the stack

Connected across every layer

See the whole picture

Every layer in one data model, so one incident is one problem — not dozens of disconnected alerts.

Our approach

Automatic discovery and dependency mapping — continuously map how hosts, processes, services, and applications connect, so the impact of a fault is visible across every layer without manual configuration.
AI-driven root cause analysis — group related anomalies into a single problem with a proposed root cause, followed from the user session through to the code, service, or infrastructure responsible.
Logs, metrics, and traces in context — bring the three core telemetry types together, so teams aren't switching tools to investigate a single incident.

Consolidate and adopt

Rationalise what you already run, and make sure the platform actually gets used.

Our approach

Infrastructure monitoring — hosts, Kubernetes, networks, and cloud, connected to the applications running on them (covered in depth on Infrastructure Observability).
Tool consolidation and migration — rationalise overlapping tools, migrate dashboards, alerts, and integrations, and retire what's no longer needed.
Adoption and enablement — dashboards, alerting, and training designed around how each team actually works, so the platform gets used rather than shelved.

How do we deliver it?

Assess, design, migrate, scale

Assess

Assess observability maturity, map your current tools, coverage gaps, and overlaps, and agree what full stack coverage needs to mean for your environment.

Target architecture

Design a single platform and data model, including what to consolidate, integrate, or retire.

Migrate & consolidate

Migrate from legacy APM and monitoring tools, move dashboards, alerts, and integrations, then decommission redundant tools.

Phased deployment

Roll out layer by layer or application by application, so value lands early and risk stays low.

Enablement and handover

Train your teams, set up governance, and transfer knowledge, with managed services available afterwards.

What tools and technology do we use?

Built on Dynatrace and Splunk

Dynatrace and Splunk as the core platforms, with certified engineers configuring legacy, cloud-native, containerised, and microservices-based applications. Instrumentation supports OpenTelemetry for vendor-neutral, portable data collection, alongside proprietary agents where full automatic topology discovery and distributed tracing depth are needed — often run side by side rather than as a strict either/or. Integrated with ServiceNow and other ITSM platforms, CI/CD pipelines, and native cloud tooling across AWS and Azure.

What outcomes can you expect?

What you walk away with

A single source of truth across the stack — infrastructure, applications, and user experience.

Reduced mean time to detect and resolve (MTTD/MTTR).

Faster root cause identification, because dependencies are already mapped.

Fewer tools and lower tooling cost and complexity through consolidation.

Clear service health for leadership and decision making.

How is this engagement structured?

Usually starts with an Initial Environment Discovery, followed by a phased implementation and consolidation program, with ongoing managed services available. Contact us to discuss the right approach.

What's the proof?

For Hollard Insurance, a platform had been stuck at a ceiling of 20 concurrent users for three years, with a 20-person team unable to find the root cause after 12 months of trying. Avocado resolved it in 4 weeks and scaled the platform to 1,000 concurrent users within 9 months — with Dynatrace data also tying call-centre downtime directly to lost business, putting a dollar figure on outages rather than tracking uptime alone.

For HBF Health, Avocado established a single pane of glass across infrastructure and application layers spanning more than 10,000 assets during a large-scale cloud transformation — saving the team over 30 hours a month previously spent piecing visibility together manually.

Common questions

What is full stack observability?

Full stack observability is unified visibility across every layer of a technology environment — infrastructure, applications, logs, and end-user experience — in one platform, with the dependencies between layers mapped automatically so problems can be traced end to end.

How is full stack observability different from observability in general?

Observability is the broader practice. Full stack observability is a specific way of implementing it, where every layer is covered by a single platform and data model rather than separate tools stitched together.

Is full stack observability the same as APM?

APM is part of it. Application performance monitoring focuses on the application layer. Full stack observability includes APM and connects it to infrastructure, logs, and real user experience, so you can see how a problem in one layer affects the others.

Does this cover microservices and containers?

Yes. Container and microservices monitoring gives granular insight into performance in dynamic, cloud-native environments, with dependencies mapped automatically as services change.

Can this integrate with our CI/CD pipeline?

Yes. Releases can be validated against real performance baselines within your pipeline, so regressions are caught before they reach production rather than discovered by customers afterwards.

Do we have to replace all our existing tools?

No. Consolidation happens where it genuinely reduces cost and complexity. Where an existing tool does its job well, it can be integrated rather than replaced.

How long does it take?

It depends on the size of your environment and how many tools are involved. Most programs are phased, so the first applications or layers are covered early while consolidation continues. An Initial Environment Discovery is the best way to scope it accurately.

Full Stack Observability