Case Study: AWS Cloud Intelligence Integration for Investment Company

October 14, 2025

30 min

Head of Software Engineering

Vadym Shvydkyi

INSART’s tech quarterback. Oversees full-stack architecture from backend to data platforms. His team crafts fintech solutions at startup pace while keeping enterprise-grade quality and reliability front-and-center.

Introduction: Addressing Cloud Cost Inefficiencies in a Growing Financial Enterprise

A large financial company specializing in investment management and financial services for thousands of clients faced a growing challenge: rapidly increasing AWS cloud costs. As the business expanded, new services were introduced, and teams actively deployed cloud resources—but there was no clear understanding of which resources were truly necessary and which were simply consuming budget.

AWS bills were increasing month over month, yet cost management processes remained fragmented and manual. The finance department lacked the tools for transparent cost analysis, and technical teams had no way to quickly identify which services could be optimized or decommissioned.

Key Challenges

Lack of visibility into cloud resource consumption: Different teams were launching AWS services, but there was no centralized analytics platform to track and control usage.
Inefficient resource allocation: Some instances were running 24/7 despite being used only a few hours per day.
Manual cost management: Expense tracking was performed manually using spreadsheets and reports, leading to delays in decision-making.
Absence of automated monitoring mechanisms: Financial risks were often identified too late, without the ability to proactively address them.

We at INSART proposed a solution that would automate cloud resource management and reduce costs without compromising performance. The primary tool we implemented was AWS Cloud Intelligence Dashboard, an advanced analytics platform that automatically collects, analyzes, and visualizes cloud cost data, helping to pinpoint inefficiencies and optimize spending.

Why AWS Cloud Intelligence Dashboard?

Deep integration with AWS Cost & Usage Report (CUR) – the most detailed source of AWS cost data.
Built-in support for Amazon QuickSight, enabling interactive dashboards and visualized insights.
Automated recommendations from AWS Trusted Advisor and AWS Budgets, identifying inefficient resource usage.
Flexible metrics and alerting system, providing proactive cost control and reduction opportunities.

The goal of this implementation was to establish a transparent, automated, and manageable cost control system that would not only reduce unnecessary expenses but also enhance infrastructure scalability and flexibility for future growth.

What You’ll Find in This Case Study:

The real-world cloud cost challenges financial enterprises face
How AWS cost monitoring and automation improve scalability and security
Which AWS solutions were used and how they were integrated
Technical implementation details, including configurations and best practices
Measurable business outcomes and cost savings achieved

Beyond just theory, this case study presents practical AWS configurations, real deployment scenarios, and automation strategies that can be directly applied to other organizations facing similar challenges.

Challenges and Pain Points: Identifying the Cost Blind Spots

As the financial company scaled its operations and expanded its AWS infrastructure, several cost-related challenges emerged. The lack of centralized visibility and automated cost control mechanisms led to inefficiencies that compounded over time. The result? Rising cloud expenses with no clear understanding of where and why resources were being consumed.

Uncontrolled Cloud Resource Expansion

The organization operated multiple AWS accounts across different teams, each provisioning cloud resources independently. While this approach encouraged agility, it also led to:

Resource sprawl—services and instances being launched without clear tracking or deallocation mechanisms.
Idle and underutilized instances consuming budget without providing proportional business value.
Redundant storage solutions, where old snapshots and unused databases remained active despite no real business need.

Without a standardized cost governance framework, identifying and eliminating inefficiencies became a time-consuming, manual process.

Manual Cost Management Slowing Decision-Making

The finance and DevOps teams relied on spreadsheet-based tracking and periodic audits to manage AWS expenses. This method proved to be:

Inefficient, as it required manual reconciliation of AWS invoices with actual usage patterns.
Reactive, where cost anomalies were identified only after significant expenses had been incurred.
Fragmented, as different teams used different tools and methodologies for monitoring cloud spending.

By the time irregularities were spotted, it was often too late to prevent budget overruns. The company needed real-time analytics and automation to take proactive control of cloud expenditures.

Lack of Granular Visibility into Cost Allocation

With various projects, teams, and departments using AWS, it became difficult to accurately allocate cloud costs to specific business units. The absence of clear tagging policies and cost attribution models led to:

Cross-charging disputes, where teams struggled to determine their exact share of cloud expenses.
Unclear ROI assessment, as the business impact of cloud investments was not easily measurable.
Overspending on shared resources, since services used by multiple teams lacked clear ownership and accountability.

A structured cost monitoring framework was needed to map every dollar spent to its respective function and purpose.

Security and Compliance Risks from Unmonitored AWS Resources

Beyond financial inefficiencies, unmonitored and uncontrolled AWS resource usage also posed security and compliance risks. Instances left running in development or test environments increased:

The attack surface, making the infrastructure more vulnerable to security breaches.
Regulatory non-compliance, as financial institutions must adhere to strict data governance policies.
Misconfigurations and access control issues, where unauthorized users had unintended privileges.

Without a centralized dashboard to track resource activity, enforce governance policies, and detect potential security gaps, the risk exposure continued to grow.

Why a Traditional Approach Wasn’t Enough

Standard AWS cost monitoring tools like AWS Cost Explorer and basic billing reports provided some level of insight but lacked:

Advanced anomaly detection to flag unusual spikes in usage automatically.
Predictive analytics to anticipate future costs and optimize budget planning.
Customizable alerting mechanisms to notify DevOps teams when resources exceeded predefined thresholds.

To address these issues, INSART implemented AWS Cloud Intelligence Dashboard, which provided granular visibility, automation, and predictive insights—transforming cost management from a reactive process into a proactive strategy.

Solution Reasoning: Chosen Technologies and Approaches

AWS Cloud Intelligence Dashboard is a pre-built analytical solution designed to centralize, visualize, and automate cloud cost management for enterprises running infrastructure on AWS. It combines data from multiple AWS cost governance services, offering:

Comprehensive cost visibility at the account, service, and resource levels
Automated insights and anomaly detection to prevent cost overruns
Pre-configured dashboards for financial reporting and DevOps analysis
Customizable alerts and notifications to proactively manage expenses

This dashboard serves as a single source of truth for cost governance, integrating multiple AWS services into a unified, interactive analytics tool.

Why AWS Cloud Intelligence Dashboard?

Native AWS Integration for Maximum Cost Transparency: unlike third-party tools that require external connectors, AWS Cloud Intelligence Dashboard directly integrates with AWS Cost & Usage Report (CUR), providing the most detailed cost data available. This eliminates blind spots in tracking and ensures that every dollar spent can be traced back to its respective resource, team, or project.

Advanced Analytics & Customizable Reporting: the dashboard leverages Amazon QuickSight for interactive visualizations, allowing teams to slice and dice cloud spending data in real-time. Compared to traditional spreadsheet-based tracking, QuickSight dashboards provide:

Multi-dimensional filtering, enabling cost breakdowns by service, team, environment, and time period
Automated report generation, reducing the need for manual cost analysis
Interactive forecasting tools, helping teams plan budgets with confidence

Proactive Cost Control with AI-Driven Alerts: by integrating AWS Budgets and AWS Trusted Advisor, the dashboard enables real-time anomaly detection and automated cost alerts. Instead of waiting for monthly billing cycles, teams receive instant notifications when:

Costs exceed predefined thresholds
Unused or underutilized resources are detected
Reserved Instances or Savings Plans offer potential savings

These alerts empower DevOps teams to react instantly, reducing unnecessary expenses before they escalate.

Security & Compliance Monitoring: since unmonitored AWS resources can pose security risks, AWS Cloud Intelligence Dashboard incorporates AWS Config and AWS Security Hub to enforce compliance. This allows teams to:

Track publicly exposed resources, such as S3 buckets with public access
Identify misconfigured IAM permissions that could lead to data leaks
Ensure compliance with SOC 2, PCI DSS, and FINRA cloud security requirements

Solution Implementation: Optimization with AWS Cloud Intelligence Dashboard

To solve the cloud cost management challenges, INSART designed and implemented a highly automated cost governance framework leveraging AWS Cloud Intelligence Dashboard alongside a set of AWS-native services. This implementation provided real-time visibility into spending, anomaly detection, automated optimization, and security enforcement—all tightly integrated into the company’s AWS infrastructure.

Data Collection and Processing with AWS Cost & Usage Report (CUR)

The foundation of cost analysis was built on AWS Cost & Usage Report (CUR), which offers granular, time-series billing data for every AWS service used. However, CUR provides raw, unstructured data that must be processed before it becomes useful.

To achieve this, we:

Enabled hourly CUR reports and configured Amazon S3 as the storage destination.
Used AWS Glue to transform the data into queryable format.
Leveraged Amazon Athena for on-demand cost queries.

AWS Glue ETL Job for CUR Processing

AWS Glue was used to convert CUR data into a structured table that could be easily queried. Below is the example of PySpark script used for ETL processing:

import sys

from pyspark.context import SparkContext

from awsglue.context import GlueContext

from awsglue.transforms import *

from awsglue.dynamicframe import DynamicFrame



sc = SparkContext()

glueContext = GlueContext(sc)

spark = glueContext.spark_session



# Define S3 paths

input_path = "s3://company-cost-reports/cur-raw-data/"

output_path = "s3://company-cost-reports/processed-data/"



# Load raw CUR data

cur_df = spark.read.option("header", "true").csv(input_path)



# Filter required columns and clean data

cur_filtered = cur_df.select("lineItemId", "productCode", "usageStartDate", "usageEndDate", "unblendedCost")

cur_filtered = cur_filtered.withColumnRenamed("unblendedCost", "cost")



# Convert to DynamicFrame and write back to S3

dynamic_cur = DynamicFrame.fromDF(cur_filtered, glueContext, "dynamic_cur")

glueContext.write_dynamic_frame.from_options(dynamic_cur, connection_type="s3", connection_options={"path": output_path}, format="parquet")



print("AWS CUR data processed and stored in S3.")

This transformed CUR data was then automatically queried via Amazon Athena and visualized in Amazon QuickSight.

Cost Visualization with Amazon QuickSight

With Athena querying CUR data, the next step was to build real-time dashboards in Amazon QuickSight. These dashboards allowed both DevOps and Finance teams to track spending trends and identify cost anomalies efficiently.

The QuickSight dashboard architecture included:

Data ingestion from Amazon S3 (processed CUR data)
Queries executed via Amazon Athena
Real-time visual updates pushed to QuickSight dashboards

Below is a simplified, anonymized example SQL query used in Amazon Athena to retrieve top 10 highest-costing services:

SELECT productCode, SUM(cost) AS total_cost

FROM cost_usage_data

WHERE usageStartDate >= date('2024-01-01')

GROUP BY productCode

ORDER BY total_cost DESC

LIMIT 10;

Below is an example of the data dashboard, built for the sample request:

Automating Cost Anomaly Detection and Alerts

To eliminate manual tracking, we implemented automated anomaly detection using AWS Trusted Advisor and AWS Budgets.

Trusted Advisor identified idle or underutilized EC2, RDS, and EBS resources, flagging them for deallocation.
AWS Budgets was configured with automated email & SNS alerts for cost threshold breaches.

We deployed a Lambda function that triggers an alert when AWS Budgets detects an overspend.

import json

import boto3



sns_client = boto3.client('sns')

budget_client = boto3.client('budgets')



SNS_TOPIC_ARN = "arn:aws:sns:us-east-1:123456789012:aws-cost-alerts"



def lambda_handler(event, context):

    budget_name = event['detail']['budgetName']

    threshold = event['detail']['threshold']

    actual_cost = event['detail']['actualCost']

    

    message = f"ALERT: Budget {budget_name} has exceeded the threshold of ${threshold}. Current spend: ${actual_cost}."

    

    sns_client.publish(TopicArn=SNS_TOPIC_ARN, Message=message, Subject="AWS Cost Budget Alert")

    

    return {"statusCode": 200, "body": json.dumps("Alert sent successfully.")}

This function is triggered whenever a cost anomaly is detected, instantly notifying DevOps teams.

Please note that the function provided is just an anonymized and simplified sample, used for demo purposes only.

This flowchart visualizes how cost data flows from AWS services into AWS Cloud Intelligence Dashboard for processing, analysis, and visualization. It shows how AWS-native services interact to collect, transform, and present cloud cost data in a structured format.

Optimizing Cloud Costs with Automated Scaling & Shutdowns

One of the biggest sources of cost waste was EC2 instances running 24/7. Many of these were used only during business hours, but remained operational overnight and on weekends.

To solve this, we implemented AWS Instance Scheduler, which:

Stopped non-essential EC2 instances outside working hours.
Scaled down compute resources based on real-time usage.
Integrated with AWS Lambda for additional customization.

An Instance Scheduler Configuration was built as follows; this setup, in total, reduced the compute costs by 35% in the first quarter.

Resources:

  InstanceSchedulerLambda:

    Type: AWS::Lambda::Function

    Properties:

      Handler: index.lambda_handler

      Runtime: python3.8

      Code:

        S3Bucket: "instance-scheduler-code"

      Role: !GetAtt LambdaExecutionRole.Arn

Strengthening Security & Compliance with AWS Config

Cost optimization isn’t just about reducing spending—it’s also about minimizing risk. Unmonitored instances often led to security misconfigurations, which could expose sensitive financial data.

To enforce best practices, we deployed AWS Config to:

Continuously monitor AWS accounts for security violations.
Ensure compliance with FINRA, SOC 2, and PCI DSS standards.
Detect publicly accessible resources and automatically revoke public permissions.

Sample Config for enforcing MFA for all AWS IAM users allowed preventing security breaches. Pseudosized version of this config is provided below:

{

  "Type": "AWS::Config::ConfigRule",

  "Properties": {

    "ConfigRuleName": "enforce-mfa",

    "Scope": { "ComplianceResourceTypes": ["AWS::IAM::User"] },

    "Source": {

      "Owner": "AWS",

      "SourceIdentifier": "IAM_USER_MFA_ENABLED"

    }

  }

}

With this fully automated cost governance solution, the company was able to:

Achieve real-time visibility into cloud spending via AWS CUR & QuickSight.
Implement automated cost optimization via instance scheduling & anomaly detection.
Strengthen security compliance using AWS Config & Trusted Advisor.

This transformed cloud cost management from a manual, reactive process into a scalable, automated solution that ensures long-term financial and operational efficiency.

Results and Conclusions: Achieving Cost Efficiency and Operational Control

The implementation of AWS Cloud Intelligence Dashboard fundamentally transformed how the company monitored, optimized, and governed its cloud spending. By integrating real-time cost visibility, automated anomaly detection, and proactive security measures, the organization eliminated inefficiencies, reduced unnecessary expenses, and strengthened compliance.

Key Outcomes:

By automating resource allocation, eliminating idle instances, and enforcing budget alerts, the company achieved a 35% decrease in overall AWS expenses within the first quarter. This was driven by:

Auto-scaling compute and storage resources to match actual demand
Shutting down non-essential instances outside business hours
Enforcing reserved instance purchasing policies to leverage cost savings

With Amazon QuickSight dashboards and AWS Cost & Usage Report (CUR) integration, the company moved from manual cost tracking to a fully automated, data-driven approach. This allowed teams to:

Analyze spending trends in real time rather than relying on monthly invoices
Generate automated reports and cost breakdowns for different business units
Identify high-cost services and optimize their configurations

Before implementing the solution, cost spikes were only discovered reactively, after significant overruns had already occurred. With AWS Trusted Advisor and AWS Budgets, the company:

Received instant alerts for unexpected spending anomalies
Configured AWS Lambda scripts to automatically deallocate unused resources
Established proactive cost monitoring, eliminating human oversight delays

By integrating AWS Config and AWS Security Hub, the company ensured that cost optimization efforts did not introduce security risks. The solution:

Automatically detected misconfigured IAM roles and public-facing resources
Ensured compliance with financial industry regulations (SOC 2, PCI DSS, FINRA)
Reduced attack surface by shutting down unnecessary cloud assets

This flowchart demonstrates the automated decision-making process used to identify cost inefficiencies and trigger optimization actions. It highlights the role of AWS Budgets, Trusted Advisor, and Lambda automation in reducing unnecessary cloud expenses.

Key Lessons Learned and Takeaways

Visibility is the Foundation of Cost Optimization

Without real-time analytics and structured cost attribution, cloud expenses can quickly spiral out of control. Implementing AWS Cloud Intelligence Dashboard provided centralized insights that enabled data-driven decision-making.

Automation is Critical for Effective Cost Governance

Manual cost tracking and resource allocation lead to inefficiencies. Automating cost control through Lambda functions, instance scheduling, and anomaly alerts ensures that cloud spending is continuously optimized without requiring human intervention.

Cost Optimization and Security Must Go Hand in Hand

Reducing cloud expenses should never come at the cost of weakened security. By integrating cost monitoring with security compliance, the company ensured that both financial efficiency and regulatory adherence were achieved.

Final Thoughts

Cloud cost optimization is no longer just about trimming expenses—it’s about engineering efficiency at scale. In modern financial enterprises, where cloud footprints grow exponentially, relying on manual oversight and traditional budgeting methods is neither practical nor sustainable.

This project showcased how a data-driven, automated cost governance framework can transform cloud financial management. By leveraging AWS-native analytics, AI-driven anomaly detection, and real-time cost visibility, the company was able to eliminate blind spots, enforce proactive governance, and optimize resource allocation without compromising performance or security.

Beyond the immediate financial impact—a 35% reduction in cloud spending within the first quarter—this approach fundamentally changed the organization’s cloud operating model. Instead of reacting to cost overruns after the fact, DevOps and finance teams now operate with full visibility, instant alerts, and automated controls.

For enterprises scaling their AWS infrastructure, the key takeaway is clear:
Cost optimization isn’t a one-time effort—it’s a continuous engineering process. With the right combination of automation, analytics, and security-first cost governance, organizations can ensure their cloud investments remain both financially efficient and operationally resilient.

Case Study: AWS Cloud Intelligence Integration for Investment Company

Table of Contents

Introduction: Addressing Cloud Cost Inefficiencies in a Growing Financial Enterprise

Key Challenges

Why AWS Cloud Intelligence Dashboard?

What You’ll Find in This Case Study:

Challenges and Pain Points: Identifying the Cost Blind Spots

Uncontrolled Cloud Resource Expansion

Manual Cost Management Slowing Decision-Making

Lack of Granular Visibility into Cost Allocation

Security and Compliance Risks from Unmonitored AWS Resources

Why a Traditional Approach Wasn’t Enough

Solution Reasoning: Chosen Technologies and Approaches

Why AWS Cloud Intelligence Dashboard?

Solution Implementation: Optimization with AWS Cloud Intelligence Dashboard

Data Collection and Processing with AWS Cost & Usage Report (CUR)

AWS Glue ETL Job for CUR Processing

Cost Visualization with Amazon QuickSight

Automating Cost Anomaly Detection and Alerts

Optimizing Cloud Costs with Automated Scaling & Shutdowns

Strengthening Security & Compliance with AWS Config

Results and Conclusions: Achieving Cost Efficiency and Operational Control

Key Outcomes:

Key Lessons Learned and Takeaways

Visibility is the Foundation of Cost Optimization

Automation is Critical for Effective Cost Governance

Cost Optimization and Security Must Go Hand in Hand

Final Thoughts