Advanced observability with Datadog
From first dashboards to proactive, AI-driven operations, we help you get real value from Datadog.
Our partnership with Datadog
As a Datadog partner, Adaptavist is perfectly positioned to help you adopt and mature your observability practice. Whether you're migrating to the cloud, modernising your applications, or trying to cut incident response time, we combine deep platform expertise with a practical, outcome-focused approach.
Datadog brings together infrastructure monitoring, application performance management, log management, and real-user monitoring in a single unified platform. We help teams move beyond basic setup to deliver genuine business value from that investment, using industry best practices to guide you from your first dashboards through fully automated, proactive operations.
Our services span CI/CD pipeline observability, AI and LLM monitoring, FinOps, and Datadog setup and value optimisation, so wherever you are on your observability journey, we can help.
Why observability matters
Modern digital systems are complex, distributed, and constantly changing. Traditional monitoring tells you something is wrong, but not why. Observability changes that give your teams the metrics, logs, traces, and correlated insights they need to understand exactly what's happening across your entire technology stack in real time.
For organisations running cloud-native applications, observability is no longer optional. It accelerates digital transformation and cloud migration, breaks down silos between development, operations, security, and business teams, and gives you the deep insight into user behaviour and system performance you need to make confident decisions and stay ahead of your competitors.
Explore our Datadog solutions
We work with you across four core areas to help you unlock the full value of your Datadog investment.
Delivery
Make your delivery pipelines observable from end to end. Using Datadog CI Visibility, we instrument your CI/CD workflows so your teams can see pipeline health, pinpoint bottlenecks, and debug failures quickly, then connect build and deployment activity directly to application performance and user impact. We also help you track and improve DORA metrics, so engineering leaders have the data they need to drive faster, safer releases.
This solution is for you if: your CI/CD pipelines feel like black boxes when builds or deployments fail, or you lack standardised dashboards and metrics across teams.
AI
As AI-powered features move into production, the ability to observe them becomes critical. We instrument your AI and LLM workloads with Datadog so you can track latency, error rates, token usage, and cost across every model and provider, and correlate AI behaviour with downstream user experience and business outcomes. We also help you put the governance structures in place to safely scale AI features with confidence.
This solution is for you if: you're rolling out AI capabilities but lack visibility into reliability, latency, safety signals, or cost, especially across multiple models or providers.
Cost
Datadog is a powerful FinOps tool, but only if it's configured to connect your cloud and platform bills with real usage data. We help finance, platform, and engineering teams align on a shared view of cost versus value: tracking spend by team, service, and environment; identifying wasteful or low-value telemetry; and establishing guardrails and a governance model to prevent surprise bills.
This solution is for you if: your cloud or Datadog spend is growing quickly, cost ownership is unclear, or finance and engineering are working from different reports.
Platform
Already using Datadog but not sure you're getting the most from it? We assess your current observability footprint, align it to your business goals, and identify where you can reduce redundant spend without sacrificing critical insight. The result is a leaner, smarter Datadog implementation, with clear ROI reporting and an ongoing governance model to keep it that way.
This solution is for you if: you have an existing Datadog deployment and want to reduce waste, improve ROI, or get stakeholder alignment on what good observability looks like.
Our Datadog services
AI and LLM monitoring
AI and LLM monitoring
We start by reviewing your current and planned AI/LLM use cases, whether internal tools, customer-facing features, agents, or copilots, and assessing your observability architecture for gaps and risks. We then instrument your AI and LLM integrations with Datadog APM, Datadog LLM Observability, and custom metrics, capturing latency, error rates, timeouts, token usage and cost, and model or provider data.
We design dashboards for LLM performance and reliability, cost and usage by team and feature, and safety and quality signals. We configure alerts for degraded AI performance, cost anomalies, and provider issues, and define the tagging and data structures you'll need to support future AI governance, routing, and optimisation. Where applicable, we also help you integrate AI workflows with live Datadog observability signals and create feedback loops from production issues back into model and prompt refinement.
FinOps and cloud cost optimisation
FinOps and cloud cost optimisation
We assess your current use of Datadog and cloud or platform services from a FinOps perspective, analysing spend across providers, products, teams, and environments to understand cost per service. We correlate your telemetry and cost data, then design and recommend cost-efficient telemetry strategies across logs, metrics, traces, and synthetics.
We build shared cost and usage dashboards for finance, platform, and engineering, and define FinOps guardrails, budgets, and review cadences to embed cost ownership into engineering workflows. Where in scope, we implement agreed optimisations to reduce low-value or wasteful spend, and produce a playbook your teams can use to manage cloud and observability costs with Datadog on an ongoing basis.
Datadog setup and value optimisation
Datadog setup and value optimisation
We assess your existing Datadog implementation against your business goals, identifying where your observability investment is delivering maximum impact, where it can be redirected for greater returns, and where critical gaps limit visibility. We deliver a prioritised optimisation roadmap with ROI projections and clear owners, implement agreed changes, and establish ongoing governance with transparent value reporting so stakeholders see the measurable business impact.
CI/CD pipeline observability
We begin by assessing your current CI/CD tooling, workflows, and monitoring across build, test, and deploy stages. From there, we instrument your in-scope pipelines with Datadog CI Visibility, capturing key metrics including duration, success rates, queue time, test flakiness, and failure reasons.
We design and build dashboards for pipeline health, deployment frequency, and change failure rate, and configure alerts for failed or slow pipelines, increasing test failures, and risky deployments. We define tagging and naming conventions to enable full traceability from build to user impact, integrate CI/CD events into your incident response and release management workflows, and produce a practical runbook so your engineering and DevOps teams can maintain and evolve what we build together.
AI and LLM monitoring
We start by reviewing your current and planned AI/LLM use cases, whether internal tools, customer-facing features, agents, or copilots, and assessing your observability architecture for gaps and risks. We then instrument your AI and LLM integrations with Datadog APM, Datadog LLM Observability, and custom metrics, capturing latency, error rates, timeouts, token usage and cost, and model or provider data.
We design dashboards for LLM performance and reliability, cost and usage by team and feature, and safety and quality signals. We configure alerts for degraded AI performance, cost anomalies, and provider issues, and define the tagging and data structures you'll need to support future AI governance, routing, and optimisation. Where applicable, we also help you integrate AI workflows with live Datadog observability signals and create feedback loops from production issues back into model and prompt refinement.
FinOps and cloud cost optimisation
We assess your current use of Datadog and cloud or platform services from a FinOps perspective, analysing spend across providers, products, teams, and environments to understand cost per service. We correlate your telemetry and cost data, then design and recommend cost-efficient telemetry strategies across logs, metrics, traces, and synthetics.
We build shared cost and usage dashboards for finance, platform, and engineering, and define FinOps guardrails, budgets, and review cadences to embed cost ownership into engineering workflows. Where in scope, we implement agreed optimisations to reduce low-value or wasteful spend, and produce a playbook your teams can use to manage cloud and observability costs with Datadog on an ongoing basis.
Datadog setup and value optimisation
We assess your existing Datadog implementation against your business goals, identifying where your observability investment is delivering maximum impact, where it can be redirected for greater returns, and where critical gaps limit visibility. We deliver a prioritised optimisation roadmap with ROI projections and clear owners, implement agreed changes, and establish ongoing governance with transparent value reporting so stakeholders see the measurable business impact.
Datadog insights
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Ready to do more than monitor?
Whether you're extending observability into new parts of your stack, getting more from an existing Datadog investment, or trying to bring cost and operations into alignment, we're here to help.
Talk to one of our expert consultants today and find out how to move from reactive firefighting to proactive, AI-driven operations.