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Navigating Budgets: How to Do a Cost Estimate for a Project?

30/05/2023

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Navigating Budgets: How to Do a Cost Estimate for a Project?

Cost estimation in project management is the process of forecasting the financial and other resources required to execute a project within a specified scope. Estimating costs as precisely as possible can mean the difference between launching projects swiftly or experiencing delays or cancellations.

So how to do a cost estimate for a project?

This article delves deeper into the definition of a cost estimate in project management, as well as reputable strategies and advice for estimating the cost of projects.

The Role of Project Cost Estimation in Project Management
Source: Watchdog | Real Estate Project Management

The Role of Project Cost Estimation in Project Management

To obtain sanction for a project, companies must justify the resources needed to complete activities. Companies must contemplate global budgetary restraints. When funds are allocated to one undertaking, they are no longer available for other endeavors. Therefore, project administrators must ensure that their cost estimates are accurate.

Cost estimation lays the groundwork for a budget that will provide sufficient funding to complete a project on time and to quality standards.

By taking the extra step of designating budgets to specific activities, companies are better able to adhere to the overall budget and avoid wasteful spending. It also assists project management in determining where potential overages may occur, allowing them to relocate resources as necessary to cover costs.

Most Common Cost Estimation Techniques

Diverse methods are used for cost estimation. Below are some of the most common cost estimation techniques in project management:

  • Analogous Estimating.
  • Bottom-up Estimating.
  • Parametric estimating.
  • Three-point Estimating.

Specifically,

Most Common Cost Estimation Techniques
Source: Profolus

Analogous Estimating

Analogous estimation, also known as top-down estimation or historical estimation, is based on historical data from comparable prior undertakings. It entails examining the expenditures of comparable past projects and applying the resulting costs to the current endeavor.

This method involves identifying a project with comparable characteristics to the current one and using its cost information as a premise for estimation. However, the accuracy of analogous estimation is highly dependent on the similarity between projects, and it may be necessary to make assumptions to account for differences.

Bottom-up Estimating

The bottom-up estimating method of cost estimation enables project managers to calculate quantities for each task, providing a clearer picture of the overall budget. Teams or departments may already have staff and resources that can be allocated to a new project, allowing the planner or project manager to determine if funding is available to cover certain aspects.

Parametric estimating

Among cost estimation methods, parametric estimating refers to estimation based on statistical associations between project characteristics and expenses. Project managers identify important variables, such as the project’s scale, scope, or complexity, and use them as inputs for the parametric model.

In contrast to analogous estimation, parametric estimation uses historical data but takes a more quantitative approach by developing mathematical models or formulas.

This method provides a more precise estimate than analogous estimation, as it takes into account specific project characteristics. However, precision is contingent upon the dependability of the selected parameters and the quality of the historical data.

Parametric estimating
Source: LinkedIn

Three-point Estimating

Three-point estimating, which stands for the Program Evaluation and Review Technique (PERT), uses a range of estimates to account for cost estimation uncertainty. Using the three-point estimate enables project managers to obtain more precise estimates by calculating costs based on three scenarios.

  • The most optimistic forecast or scenario
  • The most probable forecast or situation
  • The most pessimistic forecast or scenario

Then, these estimates are used to calculate an average cost, yielding a more accurate estimate. When uncertainty and hazards are significant in an undertaking, three-point estimation is especially useful. By contemplating a variety of potential outcomes, project managers can more effectively allocate resources and manage possible cost overruns.

Step-by-Step Guide: How to Do a Cost Estimate for a Project?

After discovering software project cost estimation techniques, you may consider the steps of cost estimation in project management. So how to do cost estimation for a project? Let’s break them down:

Step-by-Step Guide to Project Cost Estimation
Source: Smartsheet

Set Project Boundaries

Get started on the project by outlining its scope. This requires defining the project’s goals, scope, and outputs. Identify the specific tasks or activities that need to be completed and establish any limitations or constraints that may impact the project’s cost estimation process.

Cost estimates are more likely to be accurate if they are narrowly targeted and consistent with the project’s goals if their scope has been clearly defined.

Create a Project Map

Create a Work Breakdown Structure (WBS) to help you divide up the work involved in the project. The Work Breakdown Structure (WBS) is a graphical depiction of the project’s structure that helps pinpoint the factors that affect costs for individual tasks.

Beginning with the primary deliverables, break them down into smaller, more manageable chunks called work packages. If you want to get an accurate cost estimate, you need to keep breaking down the job packages into smaller and smaller tasks or activities.

Find Cost Factors and Give Initial Cost Estimates

For each work package identified in the WBS, identify the cost factors that will influence its expenses. A project’s final cost may be affected by several variables such as: labor, supplies, tools, materials, subcontractors, transportation, permits, and more.

To estimate the price of each component, it is necessary to do research, consult industry standards, evaluate historical data from previous projects, and employ subject matter experts. Provide preliminary cost estimates for each cost driver using methodologies like similar estimation, parametric estimating, or expert opinion.

Combine All Estimated Costs

Once you have determined the expenses for each work package, add together all of the individual cost estimates to determine an initial project total. Add up the projected expenditures for each job package while accounting for any connections or connections between them. Include any overheads and indirect expenses that might apply to the whole project.

Fine-tune Your Cost Estimates

To make sure your cost estimates are accurate and to factor in any risks or uncertainties, review and revise them. Think about the possibility of inflation, market swings, technology advancements, and other one-offs impacting your budget. Discuss the estimates with relevant experts, stakeholders, or team members to ensure accuracy. Improve the accuracy of your projections by factoring in critique and experience gained from prior projects.

Let SupremeTech Help You with Your Project Cost Estimation
Source: Self-Build.co.uk

Let SupremeTech Help You with Your Project Cost Estimation

Project cost estimation accuracy is essential for effective project planning and execution. How to do a cost estimate for a project? By following the step-by-step guide on how to create a project cost estimate, you can ensure that your cost estimation process is thorough and accurate. Nevertheless, navigating the complexities of cost estimation can be difficult, particularly when considering industry benchmarks, data analysis, and risk assessment.

Here, SupremeTech comes into play. With the support of ESTIM — a powerful project estimating software designed to streamline the estimation process and enhance accuracy. By utilizing ESTIM, your company can efficiently create detailed project estimates, analyze costs, and make informed decisions for successful project execution.

Contact us and let SupremeTech be your reliable partner in estimating project costs!

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This happens under the following circumstances: When the Lambda function is invoked for the first time.After a period of inactivity (execution environments are garbage collected after a few minutes of no activity – meaning it will be shut down automatically).When scaling up to handle additional concurrent requests. Cold starts introduce latency because AWS needs to set up a new execution environment from scratch. 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Cold Start Latency Cold start latency varies depending on the runtime, deployment package size, and whether the function runs inside a VPC: Node.js and Python: ~200ms–500ms for non-VPC functions.Java or .NET: ~500ms–2s due to heavier runtime initialization.VPC-Based Functions: Add ~500ms–1s due to ENI initialization. Warm Starts In contrast to cold starts, Warm Starts reuse an already-initialized execution environment. AWS keeps environments "warm" for a short time after a function is invoked, allowing subsequent requests to bypass initialization steps. Key Differences: Cold Start: New container setup → High latency.Warm Start: Reused container → Minimal latency (~<100ms). Reducing Cold Starts Cold starts can significantly impact the performance of latency-sensitive applications. Below are some actionable strategies to reduce cold starts, each with good and bad practice examples for clarity. 1. Use Smaller Deployment Packages to optimize lambda function Good Practice: Minimize the size of your deployment package by including only the required dependencies and removing unnecessary files.Use bundlers like Webpack, ESBuild, or Parcel to optimize your package size.Example: const DynamoDB = require('aws-sdk/clients/dynamodb'); // Only loads DynamoDB, not the entire SDK Bad Practice: Bundling the entire AWS SDK or other large libraries without considering modular imports.Example: const AWS = require('aws-sdk'); // Loads the entire SDK, increasing package size Why It Matters: Smaller deployment packages load faster during the initialization phase, reducing cold start latency. 2. Move Heavy Initialization Outside the Handler Good Practice: Place resource-heavy operations, such as database or SDK client initialization, outside the handler function so they are executed only once per container lifecycle – a cold start.Example: const DynamoDB = new AWS.DynamoDB.DocumentClient(); exports.handler = async (event) => {     const data = await DynamoDB.get({ Key: { id: '123' } }).promise();     return data; }; Bad Practice: Reinitializing resources inside the handler for every invocation.Example: exports.handler = async (event) => {     const DynamoDB = new AWS.DynamoDB.DocumentClient(); // Initialized on every call     const data = await DynamoDB.get({ Key: { id: '123' } }).promise();     return data; }; Why It Matters: Reinitializing resources for every invocation increases latency and consumes unnecessary computing power. 3. Enable Provisioned Concurrency1 Good Practice: Use Provisioned Concurrency to pre-initialize a set number of environments, ensuring they are always ready to handle requests.Example:AWS CLI: aws lambda put-provisioned-concurrency-config \ --function-name myFunction \ --provisioned-concurrent-executions 5 AWS Management Console: Why It Matters: Provisioned concurrency ensures a constant pool of pre-initialized environments, eliminating cold starts entirely for latency-sensitive applications. 4. Reduce Dependencies to optimize the lambda function Good Practice: Evaluate your libraries and replace heavy frameworks with lightweight alternatives or native APIs.Example: console.log(new Date().toISOString()); // Native JavaScript API Bad Practice: Using heavy libraries for simple tasks without considering alternatives.Example: const moment = require('moment'); console.log(moment().format()); Why It Matters: Large dependencies increase the deployment package size, leading to slower initialization during cold starts. 5. Avoid Unnecessary VPC Configurations Good Practice: Place Lambda functions outside a VPC unless necessary. If a VPC is required (e.g., to access private resources like RDS), optimize networking using VPC endpoints.Example:Use DynamoDB and S3 directly without placing the Lambda inside a VPC. Bad Practice: Deploying Lambda functions inside a VPC unnecessarily, such as accessing services like DynamoDB or S3, which do not require VPC access.Why It’s Bad: Placing Lambda in a VPC introduces additional latency due to ENI setup during cold starts. Why It Matters: Functions outside a VPC initialize faster because they skip ENI setup. 6. Choose Lightweight Runtimes to optimize lambda function Good Practice: Use lightweight runtimes like Node.js or Python for faster initialization than heavier runtimes like Java or .NET.Why It’s Good: Lightweight runtimes require fewer initialization resources, leading to lower cold start latency. Why It Matters: Heavier runtimes have higher cold start latency due to the complexity of their initialization process. Summary of Best Practices for Cold Starts AspectGood PracticeBad PracticeDeployment PackageUse small packages with only the required dependencies.Bundle unused libraries, increasing the package size.InitializationPerform heavy initialization (e.g., database connections) outside the handler.Initialize resources inside the handler for every request.Provisioned ConcurrencyEnable provisioned concurrency for latency-sensitive applications.Ignore provisioned concurrency for high-traffic functions.DependenciesUse lightweight libraries or native APIs for simple tasks.Use heavy libraries like moment.js without evaluating lightweight alternatives.VPC ConfigurationAvoid unnecessary VPC configurations; use VPC endpoints when required.Place all Lambda functions inside a VPC, even when accessing public AWS services.Runtime SelectionChoose lightweight runtimes like Node.js or Python for faster initialization.Use heavy runtimes like Java or .NET for simple, lightweight workloads. Error Handling and Logging Error handling and logging are critical for optimizing your Lambda functions are reliable and easy to debug. Effective error handling prevents cascading failures in your architecture, while good logging practices help you monitor and troubleshoot issues efficiently. Structured Error Responses Errors in Lambda functions can occur due to various reasons: invalid input, AWS service failures, or unhandled exceptions in the code. Properly structured error handling ensures that these issues are captured, logged, and surfaced effectively to users or downstream services. 1. Define Consistent Error Structures Good Practice: Use a standard error format so all errors are predictable and machine-readable.Example: {   "errorType": "ValidationError",   "message": "Invalid input: 'email' is missing",   "requestId": "12345-abcd" } Bad Practice: Avoid returning vague or unstructured errors that make debugging difficult. { "message": "Something went wrong", "error": true } Why It Matters: Structured errors make debugging easier by providing consistent, machine-readable information. They also improve communication with clients or downstream systems by conveying what went wrong and how it should be handled. 2. Use Custom Error Classes Good Practice: In Node.js, define custom error classes for clarity: class ValidationError extends Error {   constructor(message) {     super(message);     this.name = "ValidationError";     this.statusCode = 400; // Custom property   } } // Throwing a custom error if (!event.body.email) {   throw new ValidationError("Invalid input: 'email' is missing"); } Bad Practice: Use generic errors for everything, making identifying or categorizing issues hard.Example: throw new Error("Error occurred"); Why It Matters: Custom error classes make error handling more precise and help segregate application errors (e.g., validation issues) from system errors (e.g., database failures). 3. Include Contextual Information in Logs Good Practice: Add relevant information like requestId, timestamp, and input data (excluding sensitive information) when logging errors.Example: console.error({     errorType: "ValidationError",     message: "The 'email' field is missing.",     requestId: context.awsRequestId,     input: event.body,     timestamp: new Date().toISOString(), }); Bad Practice: Log errors without any context, making debugging difficult.Example: console.error("Error occurred"); Why It Matters: Contextual information in logs makes it easier to identify what triggered the error and where it happened, improving the debugging experience. Retry Logic Across AWS SDK and Other Services Retrying failed operations is critical when interacting with external services, as temporary failures (e.g., throttling, timeouts, or transient network issues) can disrupt workflows. Whether you’re using AWS SDK, third-party APIs, or internal services, applying retry logic effectively can ensure system reliability while avoiding unnecessary overhead. 1. Use Exponential Backoff and Jitter Good Practice: Apply exponential backoff with jitter to stagger retry attempts. This avoids overwhelming the target service, especially under high load or rate-limiting scenarios.Example (General Implementation): async function retryWithBackoff(fn, retries = 3, delay = 100) {     for (let attempt = 1; attempt <= retries; attempt++) {         try {             return await fn();         } catch (error) {             if (attempt === retries) throw error; // Rethrow after final attempt             const backoff = delay * 2 ** (attempt - 1) + Math.random() * delay; // Add jitter             console.log(`Retrying in ${backoff.toFixed()}ms...`);             await new Promise((res) => setTimeout(res, backoff));         }     } } // Usage Example const result = await retryWithBackoff(() => callThirdPartyAPI()); Bad Practice: Retrying without delays or jitter can lead to cascading failures and amplify the problem. for (let i = 0; i < retries; i++) {     try {         return await callThirdPartyAPI();     } catch (error) {         console.log("Retrying immediately...");     } } Why It Matters: Exponential backoff reduces pressure on the failing service, while jitter randomizes retry times, preventing synchronized retry storms from multiple clients. 2. Leverage Built-In Retry Mechanisms Good Practice: Use the built-in retry logic of libraries, SDKs, or APIs whenever available. These are typically optimized for the specific service.Example (AWS SDK): const DynamoDB = new AWS.DynamoDB.DocumentClient({     maxRetries: 3, // Number of retries     retryDelayOptions: { base: 200 }, // Base delay in ms }); Example (Axios for Third-Party APIs):Use libraries like axios-retry to integrate retry logic for HTTP requests. const axios = require('axios'); const axiosRetry = require('axios-retry'); axiosRetry(axios, {     retries: 3, // Retry 3 times     retryDelay: (retryCount) => retryCount * 200, // Exponential backoff     retryCondition: (error) => error.response.status >= 500, // Retry only for server errors }); const response = await axios.get("https://example.com/api"); Bad Practice: Writing your own retry logic unnecessarily when built-in mechanisms exist, risking suboptimal implementation. Why It Matters: Built-in retry mechanisms are often optimized for the specific service or library, reducing the likelihood of bugs and configuration errors. 3. Configure Service-Specific Retry Limits Good Practice: Set retry limits based on the service's characteristics and criticality.Example (AWS S3 Upload): const s3 = new AWS.S3({ maxRetries: 5, // Allow more retries for critical operations retryDelayOptions: { base: 300 }, // Slightly longer base delay }); Example (Database Queries): async function queryDatabaseWithRetry(queryFn) {     await retryWithBackoff(queryFn, 5, 100); // Retry with custom backoff logic } Bad Practice: Allowing unlimited retries can cause resource exhaustion and increase costs. while (true) {     try {         return await callService();     } catch (error) {         console.log("Retrying...");     } } Why It Matters: Excessive retries can lead to runaway costs or cascading failures across the system. Always define a sensible retry limit. 4. Handle Transient vs. Persistent Failures Good Practice: Retry only transient failures (e.g., timeouts, throttling, 5xx errors) and handle persistent failures (e.g., invalid input, 4xx errors) immediately.Example: const isTransientError = (error) =>     error.code === "ThrottlingException" || error.code === "TimeoutError"; async function callServiceWithRetry() {     await retryWithBackoff(() => {         if (!isTransientError(error)) throw error; // Do not retry persistent errors         return callService();     }); } Bad Practice: Retrying all errors indiscriminately, including persistent failures like ValidationException or 404 Not Found. Why It Matters: Persistent failures are unlikely to succeed with retries and can waste resources unnecessarily. 5. Log Retry Attempts Good Practice: Log each retry attempt with relevant context, such as the retry count and delay. async function retryWithBackoff(fn, retries = 3, delay = 100) {     for (let attempt = 1; attempt <= retries; attempt++) {         try {             return await fn();         } catch (error) {             if (attempt === retries) throw error;             console.log(`Attempt ${attempt} failed. Retrying in ${delay}ms...`);             await new Promise((res) => setTimeout(res, delay));         }     } } Bad Practice: Failing to log retries makes debugging or understanding the retry behavior difficult. Why It Matters: Logs provide valuable insights into system behavior and help diagnose retry-related issues. Summary of Best Practices for Retry logic AspectGood PracticeBad PracticeRetry LogicUse exponential backoff with jitter to stagger retries.Retry immediately without delays, causing retry storms.Built-In MechanismsLeverage AWS SDK retry options or third-party libraries like axios-retry.Write custom retry logic unnecessarily when optimized built-in solutions are available.Retry LimitsDefine a sensible retry limit (e.g., 3–5 retries).Allow unlimited retries, risking resource exhaustion or runaway costs.Transient vs PersistentRetry only transient errors (e.g., timeouts, throttling) and fail fast for persistent errors.Retry all errors indiscriminately, including persistent failures like validation or 404 errors.LoggingLog retry attempts with context (e.g., attempt number, delay,  error) to aid debugging.Fail to log retries, making it hard to trace retry behavior or diagnose problems. Logging Best Practices Logs are essential for debugging and monitoring Lambda functions. However, unstructured or excessive logging can make it harder to find helpful information. 1. Mask or Exclude Sensitive Data Good Practice: Avoid logging sensitive information like:User credentialsAPI keys, tokens, or secretsPersonally Identifiable Information (PII)Use tools like AWS Secrets Manager for sensitive data management.Example: Mask sensitive fields before logging: const sanitizedInput = {     ...event,     password: "***", }; console.log(JSON.stringify({     level: "info",     message: "User login attempt logged.",     input: sanitizedInput, })); Bad Practice: Logging sensitive data directly can cause security breaches or compliance violations (e.g., GDPR, HIPAA).Example: console.log(`User logged in with password: ${event.password}`); Why It Matters: Logging sensitive data can expose systems to attackers, breach compliance rules, and compromise user trust. 2.  Set Log Retention Policies Good Practice: Set a retention policy for CloudWatch log groups to prevent excessive log storage costs.AWS allows you to configure retention settings (e.g., 7, 14, or 30 days). Bad Practice: Using the default “Never Expire” retention policy unnecessarily stores logs indefinitely. Why It Matters: Unmanaged logs increase costs and make it harder to find relevant data. Retaining logs only as long as needed reduces costs and keeps logs manageable. 3. Avoid Excessive Logging Good Practice: Log only what is necessary to monitor, troubleshoot, and analyze system behavior.Use info, debug, and error levels to prioritize logs appropriately. console.info("Function started processing..."); console.error("Failed to fetch data from DynamoDB: ", error.message); Bad Practice: Logging every detail (e.g., input payloads, execution steps) unnecessarily increases log volume.Example: console.log(`Received event: ${JSON.stringify(event)}`); // Avoid logging full payloads unnecessarily Why It Matters: Excessive logging clutters log storage, increases costs, and makes it harder to isolate relevant logs. 4. Use Log Levels (Info, Debug, Error) Good Practice: Use different log levels to differentiate between critical and non-critical information.info: For general execution logs (e.g., function start, successful completion).debug: For detailed logs during development or troubleshooting.error: For failure scenarios requiring immediate attention. Bad Practice: Using a single log level (e.g., console.log() everywhere) without prioritization. Why It Matters: Log levels make it easier to filter logs based on severity and focus on critical issues in production. Conclusion In this episode of "Mastering AWS Lambda with Bao", we explored critical best practices for building reliable AWS Lambda functions, focusing on optimizing performance, error handling, and logging. Optimizing Performance: By reducing cold starts, using smaller deployment packages, lightweight runtimes, and optimizing VPC configurations, you can significantly lower latency and optimize Lambda functions. Strategies like moving initialization outside the handler and leveraging Provisioned Concurrency ensure smoother execution for latency-sensitive applications.Error Handling: Implementing structured error responses and custom error classes makes troubleshooting easier and helps differentiate between transient and persistent issues. Handling errors consistently improves system resilience.Retry Logic: Applying exponential backoff with jitter, using built-in retry mechanisms, and setting sensible retry limits optimizes that Lambda functions gracefully handle failures without overwhelming dependent services.Logging: Effective logging with structured formats, contextual information, log levels, and appropriate retention policies enables better visibility, debugging, and cost control. Avoiding sensitive data in logs ensures security and compliance. Following these best practices, you can optimize lambda function performance, reduce operational costs, and build scalable, reliable, and secure serverless applications with AWS Lambda. In the next episode, we’ll dive deeper into "Handling Failures with Dead Letter Queues (DLQs)", exploring how DLQs act as a safety net for capturing failed events and ensuring no data loss occurs in your workflows. Stay tuned! Note: 1. Provisioned Concurrency is not a universal solution. While it eliminates cold starts, it also incurs additional costs since pre-initialized environments are billed regardless of usage. When to Use:Latency-sensitive workloads like APIs or real-time applications where even a slight delay is unacceptable.When Not to Use:Functions with unpredictable or low invocation rates (e.g., batch jobs, infrequent triggers). For such scenarios, on-demand concurrency may be more cost-effective.

                13/01/2025

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                Bao Dang D. Q.

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                  Best Practices for Building Reliable AWS Lambda Functions

                  13/01/2025

                  323

                  Bao Dang D. Q.

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