Executive Summary
As AI continues to establish itself within enterprise workflows, IT and business leaders increasingly seek out solutions to drive value and improve outcomes from their most data-intensive initiatives.
The Elasticsearch AI Platform offers a suite of solutions and services that combine the breadth of search with the precision of AI to help enterprises find meaningful answers from all available data sources. Elastic Observability, built on the Elasticsearch AI Platform, helps customers unify data to cost-efficiently operationalize full-stack observability. With key capabilities that include machine learning anomaly detection, AI-assisted investigation and triage, and economical data storage and retrieval, teams gain broader visibility into system behavior from applications, infrastructure, cloud services, and networks.
Elastic commissioned Forrester Consulting to conduct a Total Economic Impact™ (TEI) study and examine the potential return on investment (ROI) PepsiCo realized by deploying the Elasticsearch AI Platform.1 The purpose of this study is to provide readers with a framework to evaluate the potential financial impact of Elastic on their organizations.2
To better understand the benefits, costs, and risks associated with this investment, Forrester interviewed a senior business leader at PepsiCo, an enterprise that achieved full-stack observability by consolidating disparate tools and moving beyond reactive troubleshooting. By treating telemetry as a unified data layer using Elastic Observability on the Elasticsearch AI Platform, PepsiCo transformed its operational approach from fragmented reaction to proactive prevention.
Prior to the investment, PepsiCo faced challenges with its observability initiatives. Despite having invested in more than 50 tools to gain better and broader visibility into its systems, application data silos and platform data bottlenecks kept the mean time to resolve (MTTR) an incident at a frustrating 8 hours. PepsiCo’s leaders determined this was an unacceptable risk for the business to carry.
After deploying the Elasticsearch AI Platform, PepsiCo’s observability team transformed its operations, achieving significantly reduced MTTR while reducing costs. The team identified business-critical issues faster, reduced false alerts and noise, and prevented incidents before they impacted the business, reducing MTTR by more than 73%. Additionally, the team downsized its toolset to about 40%, while increasing its total monthly observable data to 13.7 TB. By unifying data with a single, scalable platform, PepsiCo enhanced visibility across its vast IT ecosystem while rationalizing infrastructure spend and preventing key business risks from becoming costly realities.
Key Findings
Quantified benefits. Three-year, risk-adjusted present value (PV) quantified benefits for the interviewee’s organization include:
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Cost savings of $4.9 million from reducing the number of observability tools. PepsiCo reduces its observability toolset from roughly 55 tools to about 20 by standardizing on the Elasticsearch AI Platform, eliminating overlapping licenses and integrations. Over three years, PepsiCo reduced its number of observability tools by more than 60%, reducing costs and providing full-stack visibility to teams.
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Up to 38% hardware cost savings related to observability infrastructure. Data tiering and compression allows PepsiCo to move less frequently accessed data to lower-cost tiers without compromising on performance, reducing node counts and infrastructure spend while sustaining telemetry growth. Over three years, PepsiCo reduces hardware spend by a three-year present value of almost $1.2 million.
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Up to 78% of false positives suppressed. Machine learning (ML)-based anomaly detection, correlation, and log categorization suppresses more than 25,000 low-value alerts annually, recapturing engineering time and reducing alert fatigue. Time savings from eliminating these false positives amount to a three-year present-value benefit of $227,000 for PepsiCo in productivity savings, which site reliability engineers (SREs) repurposed to focus on events that could have a material business impact.
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Shift from reactive to proactive identification of downtime events. Elastic’s AI-driven spike detection enabled PepsiCo to proactively identify about 50% of major incidents before business impact, avoiding extended outages, reducing firefighting effort, and improving customer experiences. The resulting engineering hours saved total a three-year present value of $470,000 focused on proactively mitigating risk.
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More than 73% reduction in MTTR. Centralized telemetry and AI-assisted investigation reduced MTTR from roughly 8 hours to just over 2 hours by Year 3, improving service availability and operational efficiency. These efficiencies total a three-year present value of $334,000 for PepsiCo, allowing additional time to be spent on more complex cases.
Unquantified benefits. Benefits that are not quantified for this study include:
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Increased data visibility. The Elasticsearch AI Platform centralizes logs, metrics, events, and traces into a single, searchable platform without the need to physically relocate data, giving application, infrastructure, and command center teams a shared view of system health and business impact.
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Improved business continuity. Faster resolution of incidents and earlier intervention reduce the duration and severity of service disruptions, lowering the risk of lost transactions, delayed operations, and degraded customer and partner experiences.
Costs. Three-year, risk-adjusted PV costs for the interviewee’s organization include:
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Platform licensing. PepsiCo pays a subscription fee based on data ingestion volume and platform usage rather than per‑user licensing. These costs total a three-year present value of $2.9 million.
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Deployment and training. The organization incurs internal labor costs to support initial deployment, integration, and enablement across observability, application, and infrastructure teams. The labor investment totals a present value of $217,000.
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Optimization and administration. A portion of engineering resources is dedicated to ongoing management and optimization of the Elasticsearch AI Platform environment. This commitment entails a total present value of $109,000.
The financial analysis that is based on the interview found that PepsiCo experiences benefits of $7.1 million over three years versus costs of $3.3 million, adding up to a net present value (NPV) of $3.8 million and an ROI of 118%.
Key Statistics
118%
Return on investment (ROI)
$7.1M
Benefits PV
$3.8M
Net present value (NPV)
<6 months
Payback
Benefits (Three-Year)
The Elasticsearch AI Platform Customer Journey
Drivers leading to the Elasticsearch AI Platform investment
Interviewee’s Organization
Forrester interviewed a senior leader with experience using the Elasticsearch AI Platform at PepsiCo. PepsiCo is a global food and beverage company headquartered in Purchase, New York. It produces, markets, and distributes a wide range of well-known brands including Pepsi, Mountain Dew, Gatorade, Lay’s, Doritos, Quaker, and Tropicana. Operating in more than 200 countries and territories, PepsiCo is one of the largest consumer packaged goods companies in the world, with a portfolio that spans soft drinks, snacks, cereals, and convenience foods.
At the time of the interview, PepsiCo’s observability and command center functions supported more than 60 critical applications, ingesting approximately 13.7 TB of telemetry data per month across logs, metrics, events, and traces. The organization’s incident response model involves a centralized command center working in conjunction with application and infrastructure teams, with dozens of engineers participating in major incident resolution as needed.
Key Challenges
The interviewee noted that PepsiCo struggled with the following challenges prior to adopting the Elasticsearch AI Platform:
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Fragmented and siloed observability tools. Prior to implementing the Elasticsearch AI Platform, PepsiCo relied on approximately 55 different monitoring and observability tools across applications, infrastructure, and platforms. Telemetry data was distributed across multiple systems, making it difficult for teams to correlate logs, metrics, events, and traces during incidents. This fragmentation increased investigation time and limited end‑to‑end visibility into system behavior, increasing outage risk.
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Lack of scalability. As PepsiCo’s application footprint and telemetry volumes continued to grow, expanding observability coverage required adding more tools, infrastructure, and licenses. Consequently, spiraling costs and manageability constraints made it difficult to efficiently scale monitoring functions. Additionally, both failing to capture data and losing captured data became key risks of simply managing costs via vendor rationalization.
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Reactive incident management. PepsiCo’s teams primarily identified incidents after business impact had already occurred, resulting in a reactive, firefighting‑oriented response model. Major incidents often required large, cross‑functional bridge calls and took an average of about 8 hours to resolve. Limited correlation across tools made it difficult to quickly identify root causes and reduce the outage durations.
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Significant alert noise. The organization generated a high volume of alerts, many of which were false positives or low‑value signals that required manual review. Engineers spent significant time triaging alerts rather than focusing on prevention or optimization. Alert fatigue reduced the effectiveness of monitoring and increased the risk that critical signals would be missed.
Solution Requirements
PepsiCo searched for a solution that could:
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Unify logs, metrics, events, and traces in a single platform.
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Cost‑effectively scale observability coverage.
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Suppress alert noise and improve signal quality.
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Reduce MTTR and enable proactive incident detection.
Analysis Of Benefits
Quantified benefit data as applied to the composite
Total Benefits
| Ref. | Benefit | Year 1 | Year 2 | Year 3 | Total | Present Value |
|---|---|---|---|---|---|---|
| Atr | Software cost savings from tool consolidation | $1,211,250 | $2,018,750 | $2,826,250 | $6,056,250 | $4,892,928 |
| Btr | Hardware cost savings from efficient storage | $380,000 | $475,000 | $570,000 | $1,425,000 | $1,166,266 |
| Ctr | Reduction in false-positive alerts | $42,753 | $104,623 | $135,200 | $282,577 | $226,910 |
| Dtr | Proactive discovery and remediation of major downtime events | $177,840 | $188,955 | $202,293 | $569,088 | $469,820 |
| Etr | Reduced MTTR | $87,552 | $142,443 | $181,602 | $411,597 | $333,754 |
| Total benefits (risk-adjusted) | $1,899,395 | $2,929,771 | $3,915,345 | $8,744,512 | $7,089,678 |
Software Cost Savings From Tool Consolidation
Evidence and data. Prior to investing in the Elasticsearch AI Platform, PepsiCo relied on a highly fragmented observability environment that included approximately 55 different monitoring and observability tools spanning applications, infrastructure, and operations.
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PepsiCo used multiple point solutions for logging, monitoring, alerting, and event analysis, which resulted in overlapping capabilities and duplicated licensing costs.
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Telemetry data was dispersed across these tools, requiring teams to switch systems to investigate issues and increasing operational complexity.
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As PepsiCo standardized on the Elasticsearch AI Platform, the organization consolidated its observability toolset to roughly 20 solutions, retiring many legacy and redundant products.
Modeling and assumptions. Based on the interview, Forrester assumes the following:
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Software cost savings are driven by avoided licensing and maintenance costs associated with decommissioned tools, rather than reductions in headcount.
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Average annual licensing costs per retired tool remain consistent throughout the analysis period.
Risks. The following risk may impact the magnitude of benefits experienced by other organizations:
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Incomplete tool retirement. Some specialized or legacy tools may need to be retained for niche use cases or contractual reasons, limiting total savings.
Results. To account for these risks, Forrester adjusted this benefit downward by 5%, yielding a three-year, risk-adjusted total PV (discounted at 10%) of $4.9 million.
60%
Reduction in observability tools
Software Cost Savings From Tool Consolidation
| Ref. | Metric | Source | Year 1 | Year 2 | Year 3 | |
|---|---|---|---|---|---|---|
| A1 | Total observability tools licensed prior to the Elasticsearch AI Platform | Interview | 55 | 55 | 55 | |
| A2 | Total observability tools licensed while using the Elasticsearch AI Platform | Interview | 40 | 30 | 20 | |
| A3 | Reduction in observability tools needed when using the Elasticsearch AI Platform | A1-A2 | 15 | 25 | 35 | |
| A4 | Average license costs saved per observability tool | Interview | $85,000 | $85,000 | $85,000 | |
| At | Software cost savings from tool consolidation | A3*A4 | $1,275,000 | $2,125,000 | $2,975,000 | |
| Risk adjustment | ↓5% | |||||
| Atr | Software cost savings from tool consolidation (risk-adjusted) | $1,211,250 | $2,018,750 | $2,826,250 | ||
| Three-year total: $6,056,250 | Three-year present value: $4,892,928 | |||||
Hardware Cost Savings From Efficient Storage
Evidence and data. PepsiCo reduced its infrastructure and storage costs by using Elastic’s capabilities for log compression, data tiering, and more efficient resource utilization. This optimization allowed the observability environment to scale without proportional increases in hardware or cloud infrastructure spend, even as application coverage expanded.
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Prior to implementing the Elasticsearch AI Platform, PepsiCo maintained higher-cost storage and larger node counts to support expanding telemetry ingestion, and data growth created pressure on compute and storage budgets.
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After shifting to Elastic, PepsiCo adopted warm and cold data tiers with Elastic Searchable Snapshots, which enabled the organization to move older or less frequently accessed data to lower‑cost storage while maintaining search performance.
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PepsiCo also used Elastic’s LogsDB compression capabilities to reduce the size of log data, creating meaningful storage efficiencies as monthly ingestion volumes reached 13.7 TB.
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The combined effect of data tiering, compression, and improved indexing efficiency enabled the organization to reduce node counts and contain infrastructure costs while improving overall performance.
Modeling and assumptions. Based on the interview, Forrester assumes the following:
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Savings represent avoided infrastructure and storage spending that would have otherwise been required to support expanded telemetry ingestion.
Risks. The following risk may impact the magnitude of benefits experienced by other organizations:
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Variability in data retention requirements. Regulatory or operational policies might require keeping more data in higher-cost tiers than planned.
Results. To account for these risks, Forrester adjusted this benefit downward by 5%, yielding a three-year, risk-adjusted total PV (discounted at 10%) of $1.2 million.
38%
Year 3 reduction in observability infrastructure costs
Hardware Cost Savings From Efficient Storage
| Ref. | Metric | Source | Year 1 | Year 2 | Year 3 | |
|---|---|---|---|---|---|---|
| B1 | Observability infrastructure costs driven by storage prior to the Elasticsearch AI Platform | Interview | $1,600,000 | $1,600,000 | $1,600,000 | |
| B2 | Reduction in observability infrastructure costs driven by efficient storage and data tiering | Interview | 25.00% | 31.25% | 37.50% | |
| Bt | Hardware cost savings from efficient storage | B1*B2 | $400,000 | $500,000 | $600,000 | |
| Risk adjustment | ↓5% | |||||
| Btr | Hardware cost savings from efficient storage (risk-adjusted) | $380,000 | $475,000 | $570,000 | ||
| Three-year total: $1,425,000 | Three-year present value: $1,166,266 | |||||
Reduction In False-Positive Alerts
Evidence and data. PepsiCo reduced engineering time spent triaging nonactionable alerts after implementing the Elasticsearch AI Platform’s correlation, categorization, and anomaly detection capabilities. This noise reduction decreased alert fatigue and freed engineers from low‑value work, enabling them to focus on prevention, improvement, and addressing real business-impacting issues.
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Prior to Elastic, PepsiCo generated a high volume of alerts, many of which were low value or false positives that required manual review. Engineers frequently spent time acknowledging, validating, and clearing alerts that did not indicate real issues.
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With Elastic’s ML‑driven log categorization and anomaly detection, the command center suppressed more than 25,000 false-positive alerts annually, reducing noise and alert fatigue and enabling the team to recoup time to focus on meaningful incidents.
Modeling and assumptions. Based on the interview, Forrester assumes the following:
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A blended fully burdened hourly rate of $40 is applied to engineering time saved.
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A 90% productivity recapture factor is used to reflect that not all saved time converts to net-new productive output.
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Suppression scales over the analysis period as the Elasticsearch AI Platform matures, ingestion expands, and PepsiCo tunes correlation and anomaly detection rules.
Risks. The following risk may impact the magnitude of benefits experienced by other organizations:
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Varied alert patterns. Alert volumes may vary depending on factors such as industry, seasonal workload spikes, new deployments, or operational changes.
Results. To account for these risks, Forrester adjusted this benefit downward by 5%, yielding a three-year, risk-adjusted total PV (discounted at 10%) of $227,000.
78%
False positives suppressed
Reduction In False-Positive Alerts
| Ref. | Metric | Source | Year 1 | Year 2 | Year 3 | |
|---|---|---|---|---|---|---|
| C1 | False positives prior to the Elasticsearch AI Platform | Interview | 84,096 | 92,506 | 101,756 | |
| C2 | Percentage of false positives suppressed using the Elasticsearch AI Platform | Interview | 29.73% | 66.14% | 77.70% | |
| C3 | False positives suppressed | C1*C2 | 25,002 | 61,183 | 79,065 | |
| C4 | Average time spent per false positive alert (hours) | Interview | 0.05 | 0.05 | 0.05 | |
| C5 | Fully burdened hourly rate for an IT engineer | Composite | $40 | $40 | $40 | |
| C6 | Productivity recapture rate | TEI methodology | 90% | 90% | 90% | |
| Ct | Reduction in false-positive alerts | C3*C4*C5*C6 | $45,003 | $110,130 | $142,316 | |
| Risk adjustment | ↓5% | |||||
| Ctr | Reduction in false-positive alerts (risk-adjusted) | $42,753 | $104,623 | $135,200 | ||
| Three-year total: $282,577 | Three-year present value: $226,910 | |||||
Proactive Discovery And Remediation Of Major Downtime Events
Evidence and data. PepsiCo improved its ability to proactively identify and address major customer-facing incidents before they caused business and revenue disruption by using the Elasticsearch AI Platform’s anomaly detection and spike analysis capabilities. Earlier detection allowed engineers to mitigate issues before they escalated into full outages, reducing both the number and severity of business‑impacting downtime events.
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Prior to the Elasticsearch AI Platform, PepsiCo primarily detected incidents after they had already impacted users or downstream operations, resulting in reactive response and extended business disruption.
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With the Elasticsearch AI Platform in place, PepsiCo configured ML‑based anomaly detection and proactive alerting across critical systems, enabling the command center to detect emerging issues earlier in the lifecycle.
Modeling and assumptions. Based on the interview, Forrester assumes the following:
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A blended fully burdened engineering pay rate of $40 and a standard productivity recapture factor of 90% are applied to estimate the value of time saved.
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The proactive detection rate remains consistent over the analysis period as Elastic’s anomaly detection models and alerting configurations mature.
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With enough data, a quantifiable avoided loss of revenues can also be attributed to the reduction in customer-impacting incidents.
Risks. The following risk may impact the magnitude of benefits experienced by other organizations:
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Variance in downtime events. The number and severity of major downtime events may vary based on factors such as industry, business model, company size, and application portfolio.
Results. To account for these risks, Forrester adjusted this benefit downward by 5%, yielding a three-year, risk-adjusted total PV (discounted at 10%) of $470,000.
50%
Major downtime events discovered proactively
Proactive Discovery And Remediation Of Major Downtime Events
| Ref. | Metric | Source | Year 1 | Year 2 | Year 3 | |
|---|---|---|---|---|---|---|
| D1 | Major downtime events experienced annually without the Elasticsearch AI Platform | Interview | 160 | 170 | 181 | |
| D2 | Percentage of major downtime events discovered and addressed proactively | Interview | 50% | 50% | 50% | |
| D3 | Subtotal: Avoided major downtime events | D1*D2 | 80 | 85 | 91 | |
| D4 | Engineers deployed for major incident resolution (FTEs) | Interview | 10 | 10 | 10 | |
| D5 | Average MTTR prior to adoption (hours) | Interview | 8 | 8 | 8 | |
| D6 | Engineering time required to reactively resolve a major downtime event, after adoption (hours) | D4*D5 | 80 | 80 | 80 | |
| D7 | Engineering time required to proactively mitigate a major downtime event (hours) | Interview | 15 | 15 | 15 | |
| D8 | Subtotal: Engineering time saved by proactively identifying and mitigating a major downtime event with the Elasticsearch AI Platform(hours) | D4-D5 | 65 | 65 | 65 | |
| D9 | Fully burdened hourly rate for an IT engineer | C5 | $40 | $40 | $40 | |
| D10 | Productivity recapture rate | C6 | 90% | 90% | 90% | |
| Dt | Proactive discovery and remediation of major downtime events | D3*D6*D7*D8 | $187,200 | $198,900 | $212,940 | |
| Risk adjustment | ↓5% | |||||
| Dtr | Proactive discovery and remediation of major downtime events (risk-adjusted) | $177,840 | $188,955 | $202,293 | ||
| Three-year total: $569,088 | Three-year present value: $469,820 | |||||
Reduced MTTR
Evidence and data. By centralizing telemetry and enabling faster investigation through the Elasticsearch AI Platform, PepsiCo reduced the time required to resolve major incidents. Elastic’s correlation and AI‑assisted analysis (AI Assistant) helped teams identify patterns and anomalies faster with guided triage, reducing time spent diagnosing problems and ultimately reducing the overall impact of incidents on business operations.
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Prior to Elasticsearch AI Platform, major incidents at PepsiCo took an average of approximately 8 hours to resolve, often requiring extended, cross‑functional bridge calls involving application, infrastructure, and command center teams.
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Troubleshooting required engineers to manually correlate information across multiple tools, slowing root cause identification and prolonging service disruption.
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After implementing the Elasticsearch AI Platform, PepsiCo consolidated logs, metrics, and events into a single searchable platform, allowing engineers to quickly determine incident origins.
Modeling and assumptions. Based on the interview, Forrester assumes the following:
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MTTR improvements increase over time as PepsiCo standardizes investigation workflows within the command center and engineers spend less time searching for relevant data and more time executing known remediation steps.
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Engineering time savings are estimated based on the number of engineers typically engaged in major incident response and the hours avoided per incident.
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A blended fully burdened hourly rate for an IT engineer or $40 is applied to time saved.
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A standard productivity recapture factor of 90% is used to reflect that not all time saved converts directly into net-new productive output.
Risks. The following risk may impact the magnitude of benefits experienced by other organizations:
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Variances in the resolution team. The number of engineers deployed and the capabilities of those engineers will impact MTTR before and after the adoption of the Elasticsearch AI Platform.
Results. To account for these risks, Forrester adjusted this benefit downward by 5%, yielding a three-year, risk-adjusted total PV (discounted at 10%) of $334,000.
73%
Reduction in MTTR
Reduced MTTR
| Ref. | Metric | Source | Year 1 | Year 2 | Year 3 | |
|---|---|---|---|---|---|---|
| E1 | Major downtime events impacting business operations | Interview | 80 | 85 | 90 | |
| E2 | Engineers deployed for major incident resolution | Interview | 10 | 10 | 10 | |
| E3 | Average MTTR prior to the Elasticsearch AI Platform (hours) | Interview | 8.00 | 8.00 | 8.00 | |
| E4 | Average MTTR using the Elasticsearch AI Platform (hours) | Interview | 4.80 | 3.14 | 2.10 | |
| E5 | Improved MTTR using the Elasticsearch AI Platform (hours) | E3-E4 | 3.20 | 4.90 | 5.90 | |
| E6 | Fully burdened hourly rate for an IT engineer | C5 | $40 | $40 | $40 | |
| E7 | Productivity recapture rate | C6 | 90% | 90% | 90% | |
| Et | Reduced MTTR | E1*E2*E5*E6*E7 | $92,160 | $149,940 | $191,160 | |
| Risk adjustment | ↓5% | |||||
| Etr | Reduced MTTR (risk-adjusted) | $87,552 | $142,443 | $181,602 | ||
| Three-year total: $411,597 | Three-year present value: $333,754 | |||||
Unquantified Benefits
The interviewee mentioned the following additional benefits that the organization experienced but was not able to quantify:
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Increased data visibility. With Elastic in place, teams accessed the same centralized telemetry, dashboards, and contextual insights during incident response. Previously, engineers had to pull logs from one system, check metrics in another, and wait for application or infrastructure owners to share additional data, often slowing investigations and creating uncertainty about which signals were accurate. Elastic replaced this fragmented workflow with a single, searchable platform that allowed any responder to self‑serve diagnostics, reducing cross‑team handoffs, minimizing escalations, and enabling more consistent, streamlined investigation across PepsiCo’s environment.
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Avoided revenue loss. Earlier detection and resolution for 50% of major downtime events lowered the risk of delayed transactions, interruptions to business processes, and degraded experiences for customers and partners. For customer‑facing applications such as ordering platforms, revenue‑generating digital services, and field‑facing systems used in sales and distribution, improved reliability helped ensure that users experienced fewer slowdowns, errors, or access issues. While not directly quantified, PepsiCo associated these observability improvements with more predictable system performance, reduced support needs, and greater confidence in the reliability of business‑critical applications. Over time, improved continuity supported operational stability during peak demand periods and reduced exposure to the reputational and revenue risks commonly associated with prolonged or high‑impact incidents.
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Improved security and compliance. Elasticsearch AI provides PepsiCo with improved visibility into anomalies, authentication activity, system behaviors, and infrastructure performance, which can strengthen both security monitoring and compliance processes across critical applications. By centralizing telemetry that was previously scattered across multiple systems, security and operations teams gain faster access to relevant signals when investigating potential vulnerabilities or policy breaches. Over time, consistent access to correlated data and automated insights may help PepsiCo streamline internal audits, respond more quickly to security issues, and maintain a more reliable record of system activity across hybrid environments.
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Cloud migration support. As PepsiCo continues modernizing its application landscape, Elasticsearch AI offers flexibility to support observability across hybrid and cloud‑native architectures without requiring separate tools for each environment. The platform’s ability to ingest diverse telemetry sources and maintain consistent dashboards, alerts, and AI‑assisted investigation workflows can simplify the operational impact of migrating applications or services to the cloud. This consistency reduces friction during transition periods and helps teams maintain visibility into performance, dependencies, and service behavior throughout the migration journey, lowering the operational risk associated with modernization initiatives.
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Faster innovation. With centralized telemetry, AI‑assisted investigation, and comprehensive visibility into application performance, PepsiCo’s engineering teams can more quickly identify bottlenecks, validate changes, and deploy new features with greater confidence. Elasticsearch AI reduces the time developers and SREs spend troubleshooting issues or stitching together data from multiple sources, enabling teams to redirect more time toward delivering new capabilities. As Elastic expands its AI and automation features, PepsiCo has the opportunity to accelerate development cycles even further, supporting faster experimentation, higher‑quality releases, and more responsive innovation across its digital ecosystem.
Flexibility
The value of flexibility is unique to each customer. There are multiple scenarios in which PepsiCo might later realize additional uses and business opportunities for Elasticsearch AI Platform, including:
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Use case expansion. The interviewee noted that they envision AI Assistant being adopted outside of the command center and across application support, infrastructure engineering, and potentially broader business teams. As PepsiCo matures its observability practice, Elasticsearch AI may be extended to new domains such as business process analytics, SRE automation, AIOps workflows, or deeper application performance insights. These additional use cases can increase the strategic value of the platform, drive productivity through automation, and improve operational visibility across a wider portion of the organization.
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Immediate access to new features and enhancements. Elastic’s continuous release cycle allows PepsiCo to adopt new platform capabilities, such as expanded ML models, improved correlation logic, and new data management features, without separate procurement cycles. This provides future flexibility to enhance observability coverage, improve performance, and support emerging operational requirements as Elastic and PepsiCo’s observability strategy evolves.
Flexibility would also be quantified when evaluated as part of a specific project (described in more detail in Total Economic Impact Approach).
Analysis Of Costs
Quantified cost data
Total Costs
| Ref. | Cost | Initial | Year 1 | Year 2 | Year 3 | Total | Present Value |
|---|---|---|---|---|---|---|---|
| Ftr | Elasticsearch AI Platform licensing | $0 | $378,000 | $859,000 | $2,500,000 | $3,737,000 | $2,931,841 |
| Gtr | Deployment and training | $95,760 | $125,885 | $4,200 | $4,200 | $230,045 | $216,827 |
| Htr | Optimization and administration | $0 | $43,680 | $43,680 | $43,680 | $131,040 | $108,626 |
| Total costs (risk-adjusted) | $95,760 | $547,565 | $906,880 | $2,547,880 | $4,098,085 | $3,257,294 |
Elasticsearch AI Platform Licensing
Evidence and data. PepsiCo pays a subscription fee based on data ingestion volume and platform usage rather than per‑user licensing. Subscription costs scale over time as additional applications and telemetry sources are brought under observability coverage, reflecting the expansion of the Elasticsearch AI Platform across more than 60 critical systems. Elastic’s predictable pricing structure enabled PepsiCo to scale observability across teams and systems without incurring incremental licensing costs for additional users.
Modeling and assumptions. Forrester did not make any assumptions about Elastic licensing costs as these reflect PepsiCo’s actual licensing ramp up over three years.
Risks. As Forrester priced the composite directly with Elastic, no risk adjustment is applied to licensing costs.
Results. Over three years, licensing costs yield a three-year, risk-adjusted total PV (discounted at 10%) of $2.9 million.
Elasticsearch AI Platform Licensing
| Ref. | Metric | Source | Initial | Year 1 | Year 2 | Year 3 |
|---|---|---|---|---|---|---|
| F1 | Elasticsearch AI Platform licensing | Interview | $378,000 | $859,000 | $2,500,000 | |
| Ft | Elasticsearch AI Platform licensing | F1 | $0 | $378,000 | $859,000 | $2,500,000 |
| Risk adjustment | 0% | |||||
| Ftr | Elasticsearch AI Platform licensing (risk-adjusted) | $0 | $378,000 | $859,000 | $2,500,000 | |
| Three-year total: $3,737,000 | Three-year present value: $2,931,841 | |||||
Deployment And Training
Evidence and data. PepsiCo incurred internal labor costs to support initial deployment, integration, and enablement across observability, application, and infrastructure teams. These costs include time spent implementing the Elasticsearch AI Platform, configuring data pipelines, and training engineers and command center staff to effectively use advanced features like anomaly detection and AI‑assisted investigation.
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In addition to implementation activities, PepsiCo invested time in training observability, application, and infrastructure teams to ensure consistent adoption and effective use of Elastic’s analytics and AI‑assisted investigation capabilities.
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The interviewee noted that while the initial platform setup was relatively straightforward, enablement and refinement continued as more teams and systems were brought onto the platform.
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The interviewee also noted leveraging Elastic Services for deployment-related services that optimized search performance, including data tiering, Elastic Common Schema normalization, and rationalizing the number of shards per cluster.
Modeling and assumptions. Based on the interview, Forrester assumes the following:
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Deployment and training costs consist primarily of internal engineering labor rather than external professional services.
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Initial deployment efforts occur early in the analysis period, with smaller, ongoing training efforts required as observability coverage expands.
Risks. The following risk may impact the magnitude of costs experienced by other organizations:
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Integration complexity. Legacy systems or nonstandard telemetry sources could increase deployment effort.
Results. To account for these risks, Forrester adjusted this cost upward by 5%, yielding a three-year, risk-adjusted total PV (discounted at 10%) of $217,000.
Deployment And Training
| Ref. | Metric | Source | Initial | Year 1 | Year 2 | Year 3 |
|---|---|---|---|---|---|---|
| G1 | Engineering FTEs dedicated to initial deployment | Interview | 1 | |||
| G2 | Fully burdened annual salary for an IT engineer | Interview | $83,200 | |||
| G3 | Elasticsearch AI Platform — Elastic Services | Interview | $0 | $119,890 | ||
| G4 | Subtotal: Initial deployment costs | (G1*G2)+G3 | $83,200 | $119,890 | $0 | $0 |
| G5 | Infrastructure engineers trained on the Elasticsearch AI Platform | Interview | 10 | 10 | 10 | 10 |
| G6 | Engineering time dedicated to onboarding, training, and ongoing education (hours) | Interview | 20 | 0 | 10 | 10 |
| G7 | Fully burdened hourly rate for an IT engineer | C5 | $40 | $40 | $40 | $40 |
| G8 | Subtotal: Training and ongoing education | G5*G6*G7 | $8,000 | $0 | $4,000 | $4,000 |
| Gt | Deployment and training | G4+G8 | $91,200 | $119,890 | $4,000 | $4,000 |
| Risk adjustment | ↑5% | |||||
| Gtr | Deployment and training (risk-adjusted) | $95,760 | $125,885 | $4,200 | $4,200 | |
| Three-year total: $230,045 | Three-year present value: $216,827 | |||||
Optimization And Administration
Evidence and data. The interviewee noted that the Elasticsearch AI Platform required relatively little reactive maintenance and that most ongoing effort focused on optimization and enablement rather than troubleshooting platform issues. PepsiCo dedicates a portion of engineering capacity to support day‑to‑day platform administration, including onboarding new telemetry sources, managing data retention and tiering policies, and supporting teams as observability coverage expanded. Ongoing activities also included refining alerting thresholds, maintaining dashboards, and coordinating incremental enhancements aligned with evolving operational needs.
Modeling and assumptions. Based on the interview, Forrester assumes the following:
-
Support activities focus on configuration, tuning, and enablement, with minimal time spent on platform reliability issues.
Risks. The following risk may impact the magnitude of costs experienced by other organizations:
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Data governance changes. New retention or compliance requirements may increase configuration complexity.
Results. To account for these risks, Forrester adjusted this cost upward by 5%, yielding a three-year, risk-adjusted total PV (discounted at 10%) of $109,000.
Optimization And Administration
| Ref. | Metric | Source | Initial | Year 1 | Year 2 | Year 3 |
|---|---|---|---|---|---|---|
| H1 | FTEs required for ongoing optimization and administration of the Elasticsearch AI Platform | Interview | 0.5 | 0.5 | 0.5 | |
| H2 | Fully burdened annual salary for an IT engineer | G2 | $83,200 | $83,200 | $83,200 | |
| Ht | Optimization and administration | H1*H2 | $0 | $41,600 | $41,600 | $41,600 |
| Risk adjustment | ↑5% | |||||
| Htr | Optimization and administration (risk-adjusted) | $0 | $43,680 | $43,680 | $43,680 | |
| Three-year total: $131,040 | Three-year present value: $108,626 | |||||
Financial Summary
Consolidated Three-Year, Risk-Adjusted Metrics
Cash Flow Chart (Risk-Adjusted)
Cash Flow Analysis (Risk-Adjusted)
| Initial | Year 1 | Year 2 | Year 3 | Total | Present Value | |
|---|---|---|---|---|---|---|
| Total costs | ($95,760) | ($547,565) | ($906,880) | ($2,547,880) | ($4,098,085) | ($3,257,294) |
| Total benefits | $0 | $1,899,395 | $2,929,771 | $3,915,345 | $8,744,512 | $7,089,678 |
| Net benefits | ($95,760) | $1,351,830 | $2,022,891 | $1,367,465 | $4,646,427 | $3,832,384 |
| ROI | 118% | |||||
| Payback | <6 months |
Please Note
The financial results calculated in the Benefits and Costs sections can be used to determine the ROI, NPV, and payback period for the organization’s investment. Forrester assumes a yearly discount rate of 10% for this analysis.
These risk-adjusted ROI, NPV, and payback period values are determined by applying risk-adjustment factors to the unadjusted results in each Benefit and Cost section.
The initial investment column contains costs incurred at “time 0” or at the beginning of Year 1 that are not discounted. All other cash flows are discounted using the discount rate at the end of the year. PV calculations are calculated for each total cost and benefit estimate. NPV calculations in the summary tables are the sum of the initial investment and the discounted cash flows in each year. Sums and present value calculations of the Total Benefits, Total Costs, and Cash Flow tables may not exactly add up, as some rounding may occur.
From the information provided in the interview, Forrester constructed a Total Economic Impact™ framework for those organizations considering an investment in Elastic.
The objective of the framework is to identify the cost, benefit, flexibility, and risk factors that affect the investment decision. Forrester took a multistep approach to evaluate the impact that Elastic can have on an organization.
Due Diligence
Interviewed Elastic stakeholders and Forrester analysts to gather data relative to Elastic.
Interview
Interviewed a decision-maker at organizations using Elastic to obtain data about costs, benefits, and risks.
Financial Model Framework
Constructed a financial model representative of the interview using the TEI methodology and risk-adjusted the financial model based on issues and concerns of the interviewee.
Case Study
Employed four fundamental elements of TEI in modeling the investment impact: benefits, costs, flexibility, and risks. Given the increasing sophistication of ROI analyses related to IT investments, Forrester’s TEI methodology provides a complete picture of the total economic impact of purchase decisions. Please see Appendix A for additional information on the TEI methodology.
Total Economic Impact Approach
Benefits
Benefits represent the value the solution delivers to the business. The TEI methodology places equal weight on the measure of benefits and costs, allowing for a full examination of the solution’s effect on the entire organization.
Costs
Costs comprise all expenses necessary to deliver the proposed value, or benefits, of the solution. The methodology captures implementation and ongoing costs associated with the solution.
Flexibility
Flexibility represents the strategic value that can be obtained for some future additional investment building on top of the initial investment already made. The ability to capture that benefit has a PV that can be estimated.
Risks
Risks measure the uncertainty of benefit and cost estimates given: 1) the likelihood that estimates will meet original projections and 2) the likelihood that estimates will be tracked over time. TEI risk factors are based on “triangular distribution.”
Financial Terminology
Present value (PV)
The present or current value of (discounted) cost and benefit estimates given at an interest rate (the discount rate). The PVs of costs and benefits feed into the total NPV of cash flows.
Net present value (NPV)
The present or current value of (discounted) future net cash flows given an interest rate (the discount rate). A positive project NPV normally indicates that the investment should be made unless other projects have higher NPVs.
Return on investment (ROI)
A project’s expected return in percentage terms. ROI is calculated by dividing net benefits (benefits less costs) by costs.
Discount rate
The interest rate used in cash flow analysis to take into account the time value of money. Organizations typically use discount rates between 8% and 16%.
Payback
The breakeven point for an investment. This is the point in time at which net benefits (benefits minus costs) equal initial investment or cost.
Appendix A
Total Economic Impact
Total Economic Impact is a methodology developed by Forrester Research that enhances a company’s technology decision-making processes and assists solution providers in communicating their value proposition to clients. The TEI methodology helps companies demonstrate, justify, and realize the tangible value of business and technology initiatives to both senior management and other key stakeholders.
Appendix B
Supplemental Material
Related Forrester Research
Introducing The Forrester Observability Reference Architecture, Forrester Research, Inc, October 7, 2022
Carlos Casanova and Naveen Chhabra, The Observability Dance — Enabling Observability By Design,” Forrester Blogs
Carlos Casanova, Can You See Me Now? New Observability Reports!, Forrester Blogs
Carlos Casanova, A Vet, Doctor, And Biomechanical Engineer Walk Into A Bar: Monitoring, Observability, And AIOps In 2022, Forrester Blogs
Appendix C
Endnotes
1 The data Forrester gathered from PepsiCo and which informed the financial analysis consisted of a mix of observed and estimated data. The model should not be considered an exact replica of PepsiCo’s real-world financial return from using the Elasticsearch AI Platform. It is one possible data-backed representation of this ROI.
2 Total Economic Impact is a methodology developed by Forrester Research that enhances a company’s technology decision-making processes and assists solution providers in communicating their value proposition to clients. The TEI methodology helps companies demonstrate, justify, and realize the tangible value of business and technology initiatives to both senior management and other key stakeholders.
Disclosures
Readers should be aware of the following:
This study is commissioned by Elastic and delivered by Forrester Consulting. It is not meant to be used as a competitive analysis.
Forrester makes no assumptions as to the potential ROI that other organizations will receive. Forrester strongly advises that readers use their own estimates within the framework provided in the study to determine the appropriateness of an investment in Elastic.
Elastic reviewed and provided feedback to Forrester, but Forrester maintains editorial control over the study and its findings and does not accept changes to the study that contradict Forrester’s findings or obscure the meaning of the study.
Elastic provided the customer name for the interview but did not participate in the interview.
Consulting Team:
David Park
Nick Mayberry
Published
June 2026
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