How does Sedai use the AWS Lambda Telemetry API to improve performance and availability?

Sedai integrates the AWS Lambda Telemetry API to provide enhanced observability and actionable insights for Lambda functions. By capturing detailed logs, traces, and metrics directly from the Lambda platform, Sedai enables both manual and autonomous remediation of errors and performance issues. This includes surfacing cold start events, execution times, error details, and request/response data, which helps reduce error rates and failed customer interactions (FCIs) for critical services like ecommerce payments. Note: The Telemetry API extension may add a few milliseconds to Lambda usage, but does not impact the actual execution time of your code. Best fit for teams prioritizing Lambda performance and observability; teams with highly specialized, non-x86 workloads may need to confirm compatibility.

What are the main use cases for Sedai's integration with the Lambda Telemetry API?

Sedai leverages the Lambda Telemetry API for two primary use cases: (1) Enhanced manual remediation—by surfacing key execution data for problematic invocations, enabling users to troubleshoot issues that cannot be addressed autonomously; and (2) Enhanced autonomous remediation—by feeding new telemetry signals into Sedai's ML models, which inform availability and optimization recommendations. This approach improves both the speed and accuracy of issue resolution. Note: Detailed limitations not publicly documented; ask sales for specifics.

What types of Lambda execution data does Sedai capture and analyze?

Sedai captures and analyzes Lambda execution data such as exact execution time, request and response details, cold start events, error occurrences, and phase information (e.g., initialization vs. invocation). This data is made available in the Sedai UI for both troubleshooting and optimization purposes. Note: Best fit for x86-compatible Lambda functions; ARM support details not specified.

Is Sedai's Lambda optimization platform available globally?

Yes, Sedai's serverless platform using the Lambda Telemetry API is available globally for x86-compatible serverless functions. You can sign up at app.sedai.io/signup or request a demo at sedai.io. Note: ARM architecture support is not specified; confirm compatibility for non-x86 workloads.

What are the key features of Sedai's autonomous cloud platform?

Sedai's autonomous cloud platform offers features such as autonomous optimization (up to 53% cost savings), application-aware intelligence, proactive issue resolution, full-stack cloud coverage (across AWS, Azure, GCP, Kubernetes), safety-by-design (continuous health verification, automatic rollbacks, incremental changes), release intelligence, and plug-and-play implementation (5–15 minute setup). Note: Teams requiring deep customization or manual control may need to evaluate fit.

What integrations does Sedai support?

Sedai integrates with monitoring and APM tools (Prometheus, Datadog, Cloudwatch, Azure Monitor), Kubernetes autoscalers (HPA/VPA, Karpenter), IaC and CI/CD tools (GitHub, GitLab, Bitbucket, Terraform), ITSM (ServiceNow, PagerDuty, Jira), notification tools, runbook automation platforms, and serverless platforms (AWS Lambda, AWS Fargate). Note: Integration depth may vary by platform; confirm specific requirements for your stack.

What security and compliance certifications does Sedai have?

Sedai is SOC 2 certified, demonstrating adherence to stringent security and compliance standards for data protection. For more details, visit the Sedai Security page. Note: Additional certifications are not publicly documented; contact Sedai for specifics.

What is Sedai's pricing model?

Sedai uses a volume-based pricing model, charging based on the specific resources optimized (e.g., Kubernetes pods, ECS tasks, VMs). There is a free tier and a 30-day free trial available. All costs are transparently listed on the Sedai pricing page. For Kubernetes environments, a demo is recommended to determine the best pricing structure. Note: Pricing for custom or large-scale deployments may require a tailored quote.

How long does it take to implement Sedai, and how easy is it to get started?

Initial onboarding for Sedai takes approximately 15 minutes for agentless or agent-based deployment to begin reading metrics from your environment. Additional setup for integrations may require more time depending on complexity. Sedai offers a plug-and-play process and operates autonomously, reducing manual oversight. Note: Complex environments may require additional configuration.

What technical documentation is available for Sedai users?

Sedai provides a Getting Started Guide, a Kubernetes Optimization Guide, and a Platform Overview, all available at docs.sedai.io/get-started and sedai.io/resources. These resources cover onboarding, optimization strategies, and platform capabilities. Note: Advanced troubleshooting or custom integrations may require direct support.

What business impact can customers expect from using Sedai?

Customers typically achieve up to 50% cloud cost reduction, up to 75% latency reduction, up to 6X productivity gains, and a reduction in failed customer interactions by up to 50%. Financial payback is often realized in under six months, with ROI greater than 400%. For example, KnowBe4 saved $1.2 million on AWS costs, and Palo Alto Networks saved $3.5 million. Note: Results may vary based on environment and usage.

Who are some of Sedai's customers, and what results have they achieved?

Notable customers include KnowBe4 (50% cost savings, $1.2M AWS savings), Palo Alto Networks ($3.5M cloud savings), Belcorp (77% Lambda latency reduction), Campspot (34% Lambda latency reduction), Inflection (improved cold start latency), and Freshworks (reduced latency, improved user experience). See more at sedai.io/customers. Note: Individual results depend on workload and environment.

What industries does Sedai serve?

Sedai serves customers in cybersecurity (e.g., Palo Alto Networks, KnowBe4), financial services (Experian), healthcare, e-commerce (Wayfair, Campspot), IT and technology (HP, Freshworks), consumer goods (Belcorp), and digital commerce (Informed). Note: Industry-specific compliance or integration needs should be confirmed with Sedai.

What core problems does Sedai solve for cloud and Lambda users?

Sedai addresses cost inefficiencies (up to 50% cloud cost reduction), operational toil (automates repetitive tasks, up to 6X productivity gains), performance and latency issues (up to 75% latency reduction), lack of proactive issue resolution (reduces failed customer interactions by up to 50%), complexity in multi-cloud/hybrid environments, and misaligned priorities between engineering and finance. Note: Teams with highly custom or legacy environments may require additional integration work.

How does Sedai differ from other cloud optimization platforms?

Sedai is differentiated by its patented safety-by-design approach: it makes safe, autonomous optimizations in production without causing incidents or breaching SLOs. Unlike platforms that require manual approval or make all-at-once changes, Sedai performs gradual, validated optimizations with continuous health checks and automatic rollbacks. It also offers application-aware intelligence, full-stack coverage, and release intelligence. Note: Teams needing full manual control or custom scripting may prefer alternative solutions.

Improving Lambda Performance & Availability at Scale with the New AWS Telemetry API

Summary

One of the key goals of Sedai’s autonomous cloud management platform is to maximize application availability. AWS Lambda is launching the new Telemetry API, and as a launch partner Sedai uses Telemetry API capabilities to provide additional insights and signals to help improve the availability of customers’ Lambda functions.

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Overview of AWS’s New Telemetry API

The new Lambda Telemetry API enables AWS users to integrate monitoring and observability tools with their Lambda functions. AWS customers and the serverless community can use this API to receive telemetry streams from the Lambda service, including function and extension logs, as well as events, traces, and metrics coming from the Lambda platform itself.

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Sedai's Use Case for the new AWS Telemetry API

Sedai uses the Telemetry API to help improve Lambda performance and availability for our customers. We see two related use cases for the new capability:

Enhanced manual remediation. Troubleshooting is improved as the new Telemetry API allows us to pull out key execution data for errors (i.e., problematic invocations), and share that in our product UI to help customer resolve issues that cannot be addressed autonomously.
Enhanced autonomous remediation. The Telemetry AI provides new signals for our ML models. We are working on providing additional signals in our ML models building on the new metrics & spans and log data available in the Telemetry API to help inform availability & optimization recommendations.

When the Lambda function is running, the function generates the logs. The logs are available to a custom extension that we wrote than runs alongside the function. We can see a lot of useful information from the log about the runtime such as:

exact execution time
details of a request
what is the response
if a cold start occurred
whether the function errored out

Below is an example inside the Sedai platform of logs captured and an example of the detailed error information that can be accessed:

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We also see additional use cases over time for the Telemetry API as a flexible source of monitoring data.

Benefits for Our Customers

Top benefits for Sedai customers:

Improved availability. Sedai’s serverless customers can identify and address root causes through faster manual or autonomous remediation, helping reduce Lambda error rates and FCIs (Failed Customer Interactions) including critical services such as ecommerce payments.
Reduced product operating costs. We are able to manage the costs of product delivery as the new Telemetry API offers insights in a simple and unified way versus using disparate tracing sources. Capturing all tracing data would have been cost-prohibitive. Note there is an indirect cost for usage of the Telemetry API as the new extension may add perhaps a few milliseconds of Lambda usage, though not impacting the actual execution time of the underlying code in the Lambda itself. We are able to share these benefits with our customers through lower prices.

We are able to manage the costs of product delivery as the new Telemetry API offers insights in a simple and unified way versus using disparate tracing sources.

Benefits for Our Engineering Team

From the perspective of our internal engineering team directly using the Telemetry API we have also seen three major benefits:

Simplified instrumentation: by making logs, platform traces, and platform metrics available directly to the Extension, Telemetry API has made it easier for Sedai to obtain the data we need without having to build any complex instrumentation or deploy additional libraries.
Enhanced observability: the Telemetry API provides deeper insights into the different phases of the Lambda execution environment lifecycle (initialization, invocation, etc.) which help us offer enhanced insights to our customers. These insights include better cold start visibility (through events related to init phase), understanding whether initialization happened normally (during init phase) or during invoke phase due to timeout/reset (through “phase” field on the init events).
Access to new metrics and spans: New metrics such as durationMs on the platform.runtimeDone event provide the time taken by Lambda Runtime to execute our customer’s code in the function handler. This helps us isolate the time taken by Extension (if any) to run after the Runtime was done running the code in the function handler. There are also 2 new spans on the platform.runtimeDone event — responseLatency and responseDuration.

Availability

Sedai’s serverless platform using the Telemetry API is available now globally for x86-compatible serverless functions. Sign up at app.sedai.io/signup or request a demo at sedai.io. To learn more about the feature read AWS's launch blog here.

Frequently Asked Questions

Product Information & AWS Lambda Optimization