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Overview
AWS Lambda has long been appreciated for its pay-per-use model and auto-scaling. But if you’ve relied on the unpredictable free ride of the INIT phase for some runtimes, it’s time to re-evaluate. Starting August 1, 2025, AWS now standardizes billing for the INIT phase, making your function costs more transparent and your optimizations more critical.
This change may seem subtle, but for developers working with cold starts or heavy initialization logic, it affects both performance understanding and billing accuracy.
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Understanding the New AWS Lambda Billing Model
To understand the billing change, you first need to understand the AWS Lambda execution lifecycle. Every Lambda invocation goes through three phases: INIT, INVOKE, and SHUTDOWN.
- INIT Phase: This is the “cold start.” When a new execution environment is needed, Lambda prepares it by downloading your code, setting up the memory and runtime, and running your initialization code.
- INVOKE Phase: This is where your main function handler is executed to process the event.
- SHUTDOWN Phase: After the invocation, the environment is frozen and may be reused for subsequent “warm starts,” bypassing the INIT phase. Eventually, idle environments are shut down.
AWS Lambda now charges for both the INIT phase and the INVOKE phase of function execution. Previously, developers paid only for the time their function code ran. Now, AWS bills for the complete lifecycle of your Lambda function. This billing change applies to all Lambda functions, regardless of runtime or configuration.
How the INIT Phase Billing Works?
AWS Lambda calculates INIT phase charges using the same pricing structure as INVOKE phase charges. The cost depends on:
Memory allocation: Functions with more allocated memory cost more per millisecond.
Duration: The actual time spent in the INIT phase.
Region: Pricing varies by AWS region
Billing Calculation Example
Consider an AWS Lambda function with 512 MB of memory that experiences a cold start:
- INIT phase duration: 2 seconds
- Memory allocation: 512 MB (0.5 GB)
- INIT phase compute: 2 seconds × 0.5 GB = 1 GB-second
- Cost: 1 GB-second × $0.0000166667 = $0.0000166667
This cost adds to your regular INVOKE phase charges for each cold start.
Impact on Development Costs
The new billing model affects different types of applications differently:
- Low-traffic applications see the biggest impact because they experience more cold starts relative to total invocations. Functions that run infrequently may see cost increases of 20-50%.
- High-traffic applications experience less impact because warm starts dominate their execution pattern. The INIT phase cost gets amortized across many invocations.
- Large applications with heavy dependencies face higher INIT phase costs because downloading and initializing large packages takes longer.
Real-World Cost Examples
A function that processes 1,000 requests per month with 10% cold starts:
- Previous model: Only INVOKE phase charges
- New model: INVOKE phase + (100 cold starts × INIT phase cost)
For a 512 MB function with a 1-second INIT phase, this adds approximately $0.0017 per month.
Optimization Strategies for INIT Phase Costs
- Reduce Package Size: The larger your deployment package, the longer the INIT phase will take. Minimize your function’s dependencies and consider using tools like esbuild to bundle and minify your code. This can significantly reduce the time it takes to download and initialize your code.
- Use AWS Lambda SnapStart: For Java, .NET, and Python runtimes, AWS Lambda SnapStart can dramatically reduce cold start times. It creates a snapshot of the initialized execution environment and reuses it for subsequent invocations, effectively eliminating the INIT phase for most calls.
- Implement Provisioned Concurrency: If you have predictable traffic patterns, Provisioned Concurrency can keep a specified number of execution environments pre-initialized and ready to go. This eliminates cold starts but is a more cost-effective option for workloads with sustained usage.
- Analyze Your INIT Duration: You can monitor the Init Duration of your functions in Amazon CloudWatch Logs. AWS provides an Amazon CloudWatch Log Insights query to help you estimate the cost impact of this change.
Key Takeaways
AWS Lambda’s new billing model changes how developers approach serverless function design. The INIT phase billing means cold starts now have a direct cost impact. However, this change also creates opportunities for better optimization and more predictable costs.
Drop a query if you have any questions regarding AWS Lambda and we will get back to you quickly.
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FAQs
1. Do all AWS Lambda functions now incur INIT phase charges?
ANS: – Yes, all AWS Lambda functions that experience cold starts now incur INIT phase charges. This applies to all runtimes and configurations. However, warm starts (when Lambda reuses an existing execution environment) still only incur charges for the INVOKE phase.
2. How can I minimize INIT phase costs without affecting function performance?
ANS: – Focus on reducing package size by removing unused dependencies, using AWS Lambda layers for shared code, and optimizing initialization logic. Implement lazy loading for non-critical dependencies and cache expensive operations in global variables. For latency-sensitive applications, consider provisioned concurrency to eliminate cold starts.
3. Will the new billing model significantly increase my AWS Lambda costs?
ANS: – The cost impact depends on your function’s cold start rate and INIT phase duration. Low-traffic functions with frequent cold starts will see higher relative increases, while high-traffic functions with mostly warm starts will see minimal impact. Functions with large packages or heavy initialization will face higher INIT phase costs. Monitor your usage patterns and optimize accordingly to control costs.
WRITTEN BY Deep Mehta
Deep works as a Research Associate at CloudThat. He builds and manages APIs using FastAPI and works with databases like Amazon DynamoDB. He deploys applications on AWS using AWS Lambda, Amazon S3, and Amazon CloudWatch. He also configures virtual machines and uses cloud-based storage and monitoring tools. He focuses on building reliable systems and applying cloud infrastructure to solve practical problems.
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December 8, 2025
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