Elastic Observability Study Reports Lower Downtime and Infrastructure Costs

Consolidating observability tools can change more than how engineers find an outage: it can affect service reliability, developer time and the cost of storing telemetry. A Forrester Consulting study commissioned by Elastic estimates those effects for a modeled enterprise, drawing on interviews with five organizations that adopted Elastic Observability. The reported gains are substantial, though they describe a composite scenario rather than a guaranteed result for every customer.

The study interviewed decision-makers in fintech, financial services, telecommunications, media and consumer manufacturing. Forrester combined their experiences into a hypothetical enterprise with $10 billion in revenue and 25 million customers. That distinction matters: the figures below are modeled outcomes over three years, not a direct measurement of one named organization’s results.

Why teams changed their monitoring setup

Before adoption, the interviewed organizations described fragmented telemetry and incident response that often began only after a problem reached users. As application environments grew through business expansion, acquisitions and AI workloads, teams added tools to cover new systems. The result, according to the study, was six or more overlapping platforms in some cases, with data scattered across systems and storage bills rising.

That fragmentation can slow a routine investigation. Engineers need to identify which system holds the relevant logs, connect them with metrics or traces, and determine whether the signal points to an application fault or an underlying service issue. If organizations limit the data they ingest to keep costs under control, the investigation may start with gaps in visibility. If they retain everything, the expense can become difficult to manage.

A senior director of application operations at a fintech company described the business consequences: “We lost a couple of customers because of outages we had, and how long it took to resolve them, and the fact that they happened again.” The director said the company’s monitoring and telemetry had matured with Elastic, reducing mean time to resolution across its services.

The teams were seeking a shared view of logs, metrics, events and traces; a way to expand coverage without runaway storage costs; and dashboards usable by people outside technical operations. They also wanted support for AI and agentic capabilities, along with earlier incident detection that could reduce reliance on firefighting.

What the modeled results say

In Forrester’s composite scenario, Elastic Observability brings together machine learning based anomaly detection, root cause analysis, application performance monitoring and an AI assistant. The intended operational change is straightforward: surface unusual behavior before it becomes a customer affecting outage, then give responders more connected evidence when investigation is needed.

Forrester’s modeled benefits include up to 75% less system downtime by year three. The study associates that reduction with $15.2 million in revenue at risk protected over three years. It also estimates a 0.6% improvement in customer retention by year three. Those figures connect reliability work to business outcomes, but the source does not provide a breakdown of how each factor contributes or how results vary by industry.

The report estimates that site reliability engineering teams spend 95% less time on monitoring and incident resolution, freeing engineering capacity valued at $3.5 million. Developers are modeled to spend 55% less time on testing, deployment and debugging by year three. Across three years, that amounts to 263,983 hours, which Forrester values at $6.6 million.

Infrastructure costs are modeled to fall by 70%. The study attributes this to data tiering, compression and reducing the tool count from six platforms to two. A telecommunications software engineering director pointed to a related choice: Elastic provides templates for analyzing logs, helping teams decide which data to index. That can reduce reliance on proprietary log storage and other costly providers, the director said.

Where the value depends on implementation

These outcomes are linked. A shared telemetry platform can reduce the time spent switching between tools, while decisions about indexing and data tiers can shape storage costs. Faster troubleshooting may also reduce the amount of engineering time diverted from planned work. But the modeled savings depend on consolidating systems and using the platform in ways that actually improve coverage and response processes.

That makes migration and operating practices part of the business case. Teams evaluating a similar move will need to account for the effort of connecting existing services, deciding what data to retain, and building dashboards that support both specialists and other users. The study’s published summary does not quantify those migration costs or provide a detailed comparison of the composite organization’s starting and ending configurations beyond the reduction from six tools to two.

It also doesn’t establish that each organization will see the same reductions. The interviewed companies span several sectors, but Forrester’s results combine their experiences into one modeled enterprise. Buyers should treat the percentages as estimates to test against their own incident history, telemetry volume, current contracts and engineering workflows.

What to examine next

The study is most useful as a framework for asking practical questions: How long does it take to locate relevant telemetry today? Which data is missing when teams investigate incidents? How much time goes to monitoring and debugging, and what does storing the data cost? A business case built from those internal measures can show whether consolidation addresses the organization’s particular constraints.

Elastic commissioned the research, and the full Forrester study contains its methodology, composite data and customer accounts. Elastic also says that the release and timing of described features remain at its discretion, and that features not currently available may arrive late or not at all. For teams considering AI or agentic capabilities, availability and delivery timing therefore remain questions to verify.

The report’s central takeaway is that observability consolidation can connect operational visibility with measurable cost and productivity outcomes. The next step for a prospective buyer is to compare the modeled gains with its own baseline—and confirm which capabilities are available for the workloads it needs to monitor.

Elastic’s ML-driven anomaly detection, root cause analysis, application performance monitoring, and AI assistant are among the capabilities discussed in the study summary. Read the full Forrester TEI study for its methodology and composite data.