Serverless architecture for optimizing Amazon Connect call-recording archival costs

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The real value here is not serverless novelty but delaying irreversible audio compression until operational work is finished. That sequencing matters because call recordings initially serve analytics, quality review, and retryable downstream processing, yet later become cold compliance artifacts. By separating those phases, the design turns storage from a passive expense into a managed workflow. Practitioners should read this as an archive-optimization pattern: preserve high-fidelity data while it is still useful, then downgrade it only after its business value declines.

The mechanism is deliberately asynchronous. A scheduled Step Functions workflow scans the prior weekโ€™s objects in S3, pushes them into SQS, and lets Lambda workers resample each .WAV file with ffmpeg before rewriting it into a cheaper storage class. That architecture reduces coordination overhead and allows retries without blocking ingestion. The tagging and dead-letter handling are operationally important because they provide traceability when conversion fails. In practice, the design aligns compute cost with the narrow window when files actually need transformation.

The main tradeoff is that savings depend on accepting nonreversible quality loss and on confident assumptions about access patterns. If a recording later needs high-fidelity replay or expanded analytics, the compressed version may be inadequate, so version retention and lifecycle cleanup must be governed carefully. The cited cost model is persuasive, but it assumes large, stable volumes and disciplined processing windows. For practitioners, the lesson is narrower than the headline: archive economics improve when transformation is deferred, controlled, and explicitly bounded.

In this post, we provide a serverless solution to cost-optimize the storage of contact-center call recordings. The solution automates the scheduling, storage-tiering, and resampling of call-recording files, resulting in immediate cost savings. The solution is an asynchronous architecture built using AWS Step Functions, Amazon Simple Queue Service (Amazon SQS), andย AWS Lambda. Amazon Connect provides an omnichannel cloud contact center with the ability to maintain call recordings for compliance and gaining actionable insights using Contact Lens for Amazon Connect and AWS Contact Center Intelligence Partners. The storage required for call recordings can quickly increase as customers meet compliance retention requirements, often spanning six or more years. This can lead to hundreds of terabytes in long-term storage.

Solution overview

When an agent completes a customer call, Amazon Connect sends the call recording to anย Amazon Simple Storage Solution (Amazon S3)ย bucket with: a date and contact ID prefix, the file stored in the .WAV format and encoded using bitrate 256 kb/s, pcm_s16le, 8000 Hz, two channels, and 256 kb/s.ย The call-recording files are approximately 2 Mb/minute optimized for high-quality processing, such as machine learning analysis (see Figure 1).
Asynchronous architecture for batch resampling for call-recording files on Amazon S3

Figure 1. Asynchronous architecture for batch resampling for call-recording files on Amazon S3

When a call recording is sent to Amazon S3, downstream post-processing is often performed to generate analytics reports for agents and quality auditors. The downstream processing can include services that provide transcriptions, quality-of-service metrics, and sentiment analysis to create reports and trigger actionable events. While this processing is often completed within minutes, the downstream applications could require processing retries. As audio resampling reduces the quality of the audio files, it is essential to delay resampling until after processing is completed. As processed call recordings are infrequently accessed days after a call is completed, with only a small percentage accessed by agents and call quality auditors, call recordings can benefit from resampling and transitioning to long-term Amazon S3 storage tiers. In Figure 2, multiple AWS services work together to provide an end-to-end cost-optimization solution for your contact center call recordings.
AWS Step Function orchestrates the batch resampling of call recordings

Figure 2. AWS Step Function orchestrates the batch resampling of call recordings

Anย Amazon EventBridgeย schedule rule triggers the step function to perform the batch resampling process for all call recordings from the previous 7 days. In the first step function task, the Lambda function task iterates the S3 bucket using theย ListObjectsV2ย API, obtaining the call recordings (1000 objects per iteration) with the date prefix from 7 days ago. The next task invokes a Lambda function inserting the call recording objects into the Amazon SQS queue. The audio-conversion Lambda function receives the Amazon SQS queue events via theย event source mappingย Lambda integration. Each concurrent Lambda invocation downloads a stored call recording from Amazon S3, resampling the .WAV withย ffmpegย and tagging the S3 object with a โ€œconverted=Trueโ€ tag. Finally, the conversion function uploads the resampled file to Amazon S3, overwriting the original call recording with the resampled recording using a cost-optimized storage class, such asย S3 Glacier Instant Retrieval. S3 Glacier Instant Retrieval provides the lowest cost for long-lived data that is rarely accessed and requires milliseconds retrieval, such as for contact-center call-recording playback. By default, Amazon Connect stores call recordings withย S3 Versioningย enabled, maintaining the original file as a version. You can use lifecycle policies toย delete object versionsย from a version-enabled bucket to permanently remove the original version, as this will minimize the storage of the original call recording. This solution captures failures within the step function workflow with logging and a dead-letter queue, such as when an error occurs with resampling a recording file. A Step Function task monitors the Amazon SQS queue using theย AWS Step Function integration with AWS SDKย with SQS and ending the workflow when the queue is emptied. Table 1 demonstrates the default and resampled formats.
Detailed AWS Step Functions state machine diagram

Figure 3. Detailed AWS Step Functions state machine diagram

Resampling

Table 1. Default and resampled call recording audio formats
Audio sampling formats File size/minute Notes
Bitrate 256 kb/s, pcm_s16le, 8000 Hz, 2 channels, 256 kb/s 2 MB The default for Amazon Connect call recordings. Sampled for audio quality and call analytics processing.
Bitrate 64 kb/s, pcm_alaw, 8000 Hz, 1 channel, 64 kb/s 0.5 MB Resampled to mono channel 8 bit. This resampling is not reversible and should only be performed after all call analytics processing has been completed.

Cost assessment

For pricing information for the primary services used in the solution, visit: The costs incurred by the solution are based on usage and are AWS Free Tier eligible. After the AWS Free Tier allowance is consumed, usage costs are approximately $0.11 per 1000 minutes of call recordings. S3 Standard starts at $0.023 per GB/month; and S3 Glacier Instant Retrieval is $0.004 per GB/month, with $0.003 per GB of data retrieval. During a 6-year compliance retention term, the schedule-based resampling and storage tiering results in significant cost savings. In the 6-year example detailed in Table 2, the S3 Standard storage costs would be approximately $356,664 for 3 million call-recording minutes/month. The audio resampling and S3 Glacier Instant Retrieval tiering reduces the 6-year cost to approximately $41,838. Table 2. Multi-year costs savings scenario (3 million minutes/month) in USD
Year Total minutes (3 million/month) Total storage (TB) Cost of storage, S3 Standard (USD) Cost of running the resampling (USD) Cost of resampling solution with S3 Glacier Instant Retrieval (USD)
1 36,000,000 72 10,764 3,960 4,813
2 72,000,000 108 30,636 3,960 5,677
3 108,000,000 144 50,508 3,960 6,541
4 144,000,000 180 70,380 3,960 7,405
5 180,000,000 216 90,252 3,960 8,269
6 216,000,000 252 110,124 3,960 9,133
Total 1,008,000,000 972 356,664 23,760 41,838
To explore PCA costs for yourself, useย AWS Cost Explorerย or chooseย Bill Detailsย on theย AWS Billing Dashboardย to see your month-to-date spend by service.

Deploying the solution

The code and documentation for this solution are available by cloning theย git repositoryย and can be deployed withย AWS Cloud Development Kit (AWS CDK). Bash # clone repository git clone https://github.com/aws-samples/amazon-connect-call-recording-cost-optimizer.git # navigate the project directory cd amazon-connect-call-recording-cost-optimizer Modify theย cdk.context.jsonย with your environmentโ€™s configuration setting, such as theย bucket_name. Next, install the AWS CDK dependencies and deploy the solution: :# ensure you are in the root directory of the repository ./cdk-deploy.sh Once deployed, you can test the resampling solution by waiting for the EventBridge schedule rule to execute based on theย num_days_ageย setting that is applied. You can also manually run the AWS Step Function with a specified date, for exampleย {"specific_date":"01/01/2022"}. The AWS CDK deploymentย creates the following resources:
  • AWS Step Function
  • AWS Lambda function
  • Amazon SQS queues
  • Amazon EventBridge rule
The solution handles the automation of transitioning a storage tier, such as S3 Glacier Instant Retrieval. In addition,ย Amazon S3 Lifecyclesย can be set manually to transition the call recordings after resampling to alternativeย Amazon S3 Storage Classes.

Cleanup

When you are finished experimenting with this solution, cleanup your resources by running the command: cdk destroy This command deletes the AWS CDK-deployed resources. However, the S3 bucket containing your call recordings and CloudWatch log groups are retained.

Conclusion

This call recording resampling solution offers an automated, cost-optimized, and scalable architecture to reduce long-term compliance call recording archival costs.

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