What is Sedai's AWS Lambda Optimization and how does it work?

Sedai's AWS Lambda Optimization is an autonomous solution that continuously tunes memory, concurrency, and cold start settings for every Lambda function. It analyzes traffic and code changes to find the optimal configuration, adjusting memory allocation to balance CPU and execution time, and dynamically managing provisioned and reserved concurrency. This ensures best performance at the lowest cost, without requiring manual intervention. Note: Sedai's approach is best suited for teams seeking ongoing, automated optimization rather than one-time tuning. Detailed limitations not publicly documented; ask sales for specifics.

How does Sedai optimize Lambda memory and CPU settings?

Sedai continuously adjusts each function's memory allocation, measuring the impact on duration and cost to find the configuration that delivers the best performance at the lowest price. Since Lambda allocates CPU proportionally to memory, Sedai avoids the common pitfall of over-allocating memory (and thus cost) or under-allocating CPU (and thus causing latency). Note: For highly specialized workloads, manual tuning may still be required in rare cases.

How does Sedai manage provisioned and reserved concurrency for Lambda?

Sedai analyzes traffic patterns to dynamically adjust provisioned concurrency throughout the day, eliminating cold starts without the cost of a static configuration. It also provides visibility into how reserved concurrency is distributed across all functions, helping protect critical functions from being throttled by high-volume, non-critical ones. Note: For workloads with extremely unpredictable spikes, some manual oversight may be advisable.

Does Sedai support Lambda optimization across multiple AWS accounts or regions?

Yes, Sedai supports Lambda optimization across multiple AWS accounts and regions, enabling centralized management and optimization for organizations with complex cloud environments. Note: Integration steps may vary depending on AWS account structure; consult documentation for details.

How does Sedai ensure safe, autonomous optimizations for Lambda in production?

Sedai is designed with safety as a core principle. It performs continuous health verification before, during, and after every optimization action, uses automatic rollbacks if risk is detected, and applies incremental changes for real-time validation. This patented approach allows Sedai to make safe, autonomous optimizations in production without causing incidents or breaching SLOs. Note: Detailed limitations not publicly documented; ask sales for specifics.

What measurable results have customers achieved with Sedai's Lambda optimization?

Customers have reported significant improvements using Sedai for Lambda optimization. For example, KnowBe4 achieved up to 50% cost savings and reduced average Lambda response time from 18.5 seconds to 80 milliseconds (a 99.5% reduction). Belcorp reduced AWS Lambda latency by 77%, and Campspot achieved a 34% reduction in Lambda latency. Note: Results may vary depending on workload and environment. Read the KnowBe4 case study .

Who can benefit most from Sedai's Lambda optimization?

Sedai's Lambda optimization is best suited for organizations running large fleets of Lambda functions, especially those with dynamic traffic patterns, frequent code changes, or cost and performance optimization goals. Roles that benefit include Cloud Operations, SREs, Platform Engineers, and FinOps teams. Note: Teams with very static or low-usage Lambda workloads may see less benefit.

What common pain points does Sedai solve for Lambda users?

Sedai addresses pain points such as runaway Lambda costs due to over-provisioning, performance bottlenecks from under-allocated CPU, operational toil from manual tuning, and the complexity of managing concurrency across hundreds of functions. It automates these optimizations, freeing teams to focus on innovation. Note: For highly specialized compliance or regulatory requirements, additional validation may be needed.

How is Sedai's Lambda optimization priced?

Sedai uses a resource-based pricing model, where costs are determined by the resources optimized and the value delivered. For Lambda, this means you pay based on the scope of Lambda functions optimized and the savings achieved. All costs are transparently outlined on the Sedai pricing page , with no hidden fees. Note: For exact pricing, contact Sedai sales or request a demo.

Is there a free trial or proof of value for Lambda optimization?

Yes, Sedai offers a free Proof of Value and a 30-day free trial, allowing you to evaluate Lambda optimization benefits before committing. Note: After the trial, standard resource-based pricing applies.

How long does it take to implement Sedai for Lambda optimization?

Initial setup for Lambda optimization can be completed in as little as 15 minutes using agentless or agent-based deployment. This allows Sedai to begin reading metrics and optimizing functions quickly. Note: More complex environments may require additional integration steps.

What integrations does Sedai support for Lambda optimization?

Sedai integrates with monitoring and APM tools such as Datadog, AWS CloudWatch, and Prometheus; CI/CD pipelines like GitHub, GitLab, and Bitbucket; ITSM tools including ServiceNow, PagerDuty, and Jira; and notification platforms. This ensures Lambda optimization fits into your existing workflows. Note: Some integrations may require additional configuration.

Where can I find technical documentation for setting up Lambda optimization?

Comprehensive onboarding guides and technical documentation for Lambda optimization are available at docs.sedai.io/get-started . These resources cover setup, integration, and best practices. Note: For advanced scenarios, contact Sedai support.

Is Sedai SOC 2 certified?

Yes, 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: For additional certifications, contact Sedai directly.

What are some real-world examples of companies using Sedai for Lambda optimization?

KnowBe4 achieved up to 50% cost savings and a 99.5% reduction in Lambda response time. Belcorp reduced Lambda latency by 77%, and Campspot saw a 34% reduction. These results are documented in public case studies available on the Sedai resources page . Note: Outcomes depend on workload and environment.

What feedback have customers given about Sedai's Lambda optimization?

Customers have praised Sedai for quick onboarding (as little as 15 minutes), agentless or agent-based deployment, and minimal management burden. For example, Robert Berger, Chief Architect at Informed, stated: 'The optimization of the memory and the CPU of our Lambdas is something that I just have Sedai worry about. And we just see the results.' Note: Individual experiences may vary.

How does Sedai determine the right memory setting for a function?

Sedai uses a reinforcement learning model that makes incremental adjustments to memory in the live environment, measures the resulting impact on execution duration and cost, and converges on the configuration that delivers the best performance-to-cost ratio for that specific function's behavior.

Does Sedai's memory optimization account for the cost of execution time, not just allocation?

Yes. Because Lambda pricing is based on both memory allocated and execution duration, increasing memory can sometimes lower total cost by reducing how long a function runs. Sedai's model accounts for this relationship and optimizes for total cost, not just allocated memory.

Will dynamic provisioned concurrency adjustments cause cold starts during transitions?

Sedai builds a predictive model from your function's traffic patterns and seasonality, adjusting concurrency ahead of anticipated demand rather than reactively. This approach is designed to maintain warm instances through traffic ramps rather than responding after cold starts occur.

How does Sedai handle functions with unpredictable or spiky traffic?

Sedai's model accounts for traffic variability when building concurrency schedules. For highly unpredictable functions, recommendations are more conservative — maintaining sufficient baseline capacity while avoiding the cost of over-provisioning across the full day.

Does Sedai support Lambda functions across multiple AWS accounts or regions?

Yes. Sedai supports multi-account and multi-region Lambda deployments, with concurrency visibility and optimization applied across the full account structure.

Lambda Optimized Continuously, Not Just Once

Memory, concurrency, and cold starts require constant tuning as traffic and code change — but most teams set them once and move on. Sedai autonomously finds the optimal configuration for every function, continuously.

Lambda's Simplicity Is Real. Its Cost Model Is Genuinely Hard to Optimize.

Memory allocation controls CPU, and CPU controls execution time, which controls cost. These relationships make Lambda difficult to tune manually — especially across hundreds of functions with different traffic profiles and runtime behaviors.

Memory is the wrong knob when what you're tuning is CPU.

Lambda allocates CPU proportionally to memory. Teams that optimize on memory alone — without accounting for execution time tradeoffs — routinely end up paying for unused RAM or creating latency problems by under-allocating CPU.

Static provisioned concurrency wastes money by design.

Provisioned concurrency eliminates cold starts but is billed continuously regardless of actual traffic. A fixed setting that made sense at peak will overspend during every off-peak hour until someone adjusts it.

Reserved concurrency across hundreds of functions doesn't manage itself.

Maintaining appropriate concurrency limits across a large function fleet — as traffic patterns and deployment frequencies change — is ongoing work that SRE teams consistently deprioritize.

"Pretty much everything that we're doing is using Lambdas and Step Functions. It was a whole area that I thought I was going to have to spend a lot of resources on. But [now] the optimization of the memory and the CPU of our lambdas is something that I just have Sedai worry about. And we just see the results."

Robert Berger

Chief Architect // Informed

How We Help

Memory & Performance Optimization

Sedai continuously adjusts each function's memory allocation — measuring impact on duration and cost — to find the configuration that delivers best performance at the lowest price.

Provisioned Concurrency Management

Sedai analyzes traffic patterns to dynamically adjust provisioned concurrency throughout the day, eliminating cold starts without the cost of a static configuration.

Reserved Concurrency Visibility

Sedai surfaces how account-level concurrency is distributed across all functions, making it practical to protect critical functions from being throttled by high-volume, non-critical ones.

Frequently Asked Questions

Features & Capabilities