How NimbleWork Reduced Observability Costs by 61% and Improved Application Reliability with Middleware

NimbleWork, an enterprise software company running applications and microservices on AWS, needed better visibility into application performance, recurring errors, and user experience. By consolidating its previous AWS CloudWatch and Grafana/Prometheus monitoring setup into Middleware, the company reduced observability costs by approximately 61%, improved end-to-end tracing, and automated performance reporting. Middleware also helped the team prioritize fixes and retire obsolete components, enabling a more proactive approach to application reliability.

Its applications run on AWS across a combination of monolithic architecture and microservices. As the platform evolved and served enterprise customers, maintaining application reliability, performance, and a consistent user experience became essential.

Recognizing the Need for Better Observability

As NimbleWork's platform expanded, its engineering team needed greater visibility into how applications performed across different services. Identifying slow transactions, recurring errors, and performance bottlenecks became increasingly important as new features and services were introduced.

The company previously relied on AWS CloudWatch and Grafana/Prometheus for monitoring. While these tools provided infrastructure and service-level information, the team lacked a unified view connecting frontend user interactions with backend application behavior.

Beyond troubleshooting, NimbleWork wanted to make production monitoring a regular engineering practice. Instead of addressing problems only during major incidents, the team needed actionable insights to identify recurring issues, track performance trends, and ensure newly deployed features continued to perform reliably.

Challenges Before Middleware

Fragmented monitoring and higher costs: Managing AWS CloudWatch alongside Grafana and Prometheus increased monitoring complexity and overall expenditure. Correlating application logs, performance metrics, and user activity across separate monitoring workflows required additional engineering effort.

Limited end-to-end visibility: With monolithic applications and microservices running together, tracing requests from the browser through backend services was challenging. Engineers needed more reliable frontend-to-backend correlation to determine where slowdowns and errors originated.

Recurring errors and reactive troubleshooting: As release velocity increased, the team wanted a consistent way to review application behavior after every release and prioritize recurring issues

These challenges highlighted the need for unified observability that could simplify troubleshooting, improve engineering efficiency, and reduce monitoring costs.

How Middleware Helped Solve These Challenges

Unified Visibility Across Applications and Services

Middleware brought Application Performance Monitoring, Log Monitoring, and Real User Monitoring into a single platform.

With distributed tracing and real user monitoring, NimbleWork gained greater visibility into application behavior across frontend interactions and backend services.

With improved RUM-to-APM correlation, NimbleWork gained better visibility into the relationship between user interactions and backend services. Following initial configuration and product improvements, engineers could investigate application behavior more effectively across its mixed architecture.

Engineers could now investigate slow transactions, examine application errors, and review user experiences using connected observability data.

Proactive Error Detection and Performance Monitoring

Using Middleware's error patterns, anomaly detection, and service-level performance insights, NimbleWork established a structured weekly review process.

The team identifies recurring errors, analyzes slow transactions, and prioritizes services requiring engineering attention. This has reduced repeated backend errors and made post-release performance monitoring more consistent.

Middleware’s service-level error analysis helped the team retire obsolete components and reduce operational noise in production.

Automated Investigation and Root Cause Analysis

NimbleWork extended Middleware's capabilities through an internal operations automation service integrated with Middleware APIs and AWS.

The service retrieves slow transactions, application metrics, and error information to generate weekly performance reports and create follow-up tasks for engineering teams. It also uses the collected telemetry to support incident triage, root cause analysis, and recommendations for resolving issues.

NimbleWork's automation is built in-house on Middleware APIs, and OpsAI is a separate Middleware capability, which can correlate telemetry, investigate alerts, identify likely root causes, and suggest fixes.

As a result, observability has become part of the team's regular engineering operations, helping developers identify performance problems and take corrective action without relying entirely on manual dashboard reviews.

Results and Business Impact

61% Reduction in Observability Costs

By consolidating its previous monitoring setup into Middleware, NimbleWork achieved approximately 61% lower observability costs.

Beyond the financial savings, the company simplified its monitoring environment while gaining better access to application performance data, logs, distributed traces, and real user insights.

Faster Troubleshooting and Improved Application Reliability

With logs, traces, and user-session information connected in one platform, engineers can investigate application issues with greater context and identify affected services more efficiently.

Regular error-pattern reviews have reduced recurring backend errors, highlighted problematic services, and uncovered components that previously generated unnecessary operational noise.

Improved Engineering Productivity Through Automation

Middleware's APIs have enabled NimbleWork to automate weekly performance reporting and connect observability insights with its internal incident investigation workflows.

Instead of manually gathering information from multiple sources, the team can generate reports, identify slow transactions, and initiate engineering follow-ups through repeatable processes.

This has helped establish greater accountability for application health, particularly after new features and services are deployed.

Conclusion

For NimbleWork, Middleware has delivered more than cost savings. By bringing application monitoring, logs, distributed tracing, and real user monitoring together, the company has improved how its engineering teams investigate issues, manage recurring errors, and maintain application reliability.

With 61% lower observability costs, automated performance reporting, and better visibility across its AWS environment, NimbleWork has adopted a more efficient, proactive approach to production monitoring.