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AWS Lambda Managed Instances

AWS LabsOfficialPublished by AWS Labs

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From the author

Evaluate, configure, and migrate workloads to AWS Lambda Managed Instances (LMI). Triggers on: Lambda Managed Instances, LMI, capacity provider, multi-concurrency Lambda, dedicated instance Lambda, EC2-backed Lambda, cold start elimination, Graviton Lambda, instance type for Lambda, scheduled scaling for LMI, Lambda cost optimization with Reserved Instances or Savings Plans. Also trigger when users describe high-volume predictable workloads seeking cost savings, want to scale LMI capacity on a schedule, or compare Lambda vs EC2 for steady-state traffic. For standard Lambda without LMI, use the aws-lambda skill instead.
— the description by AWS Labs

AutoGineer summary

Written by AutoGineer from the skill’s own files, not by the author

This skill provides a structured approach to migrating Lambda functions to Lambda Managed Instances (LMI). LMI allows you to run Lambda functions on dedicated EC2 instances within your account, combining the ease of Lambda development with the cost-efficiency and hardware flexibility of EC2. The skill guides users through workload assessment, cost comparison, concurrency tuning, and infrastructure setup, ensuring that LMI is the right fit for their specific traffic patterns and performance requirements.

What it does

  • Assesses workload suitability for LMI based on traffic patterns and cost.
  • Provides formulas for calculating concurrency, memory, and execution environment requirements.
  • Guides the configuration of capacity providers, instance types, and scaling controls.
  • Reviews code for concurrency and thread-safety requirements across supported runtimes.
  • Assists in setting up infrastructure via CLI, SAM, or CDK.
  • Offers troubleshooting steps for common deployment and performance issues.

When to use it

  • When evaluating if LMI is more cost-effective than standard Lambda for steady-state traffic.
  • When you need to eliminate cold starts for high-volume, predictable workloads.
  • When you require specific EC2 instance types or hardware configurations for Lambda functions.
  • When you need to implement scheduled scaling for predictable traffic spikes.
  • When you need to migrate existing Lambda functions to a more performant or cost-optimized environment.

Advantages

  • Optimizes costs for high-volume, steady-state workloads.
  • Eliminates cold starts through provisioned capacity.
  • Provides access to diverse EC2 instance families and Graviton processors.
  • Allows for granular control over scaling and resource utilization.
  • Maintains the familiar Lambda developer experience while leveraging EC2 infrastructure.

Usage examples

“Is my Lambda function a good candidate for LMI?”
The agent will ask for your current monthly Lambda spend, traffic patterns (steady vs. bursty), and runtime to perform a cost-benefit analysis and determine if LMI is appropriate.
“How do I configure LMI for a CPU-intensive Python application?”
The agent will recommend an appropriate instance family (e.g., C-series), suggest a memory-to-vCPU ratio, and provide the necessary configuration steps for your chosen IaC framework.
“Help me set up scheduled scaling for my LMI function.”
The agent will guide you through using EventBridge Scheduler to adjust your Min/Max execution environments based on your specific business hours or traffic schedule.
“My LMI function is experiencing 429 throttles. How do I fix it?”
The agent will help you analyze your current scaling configuration and suggest adjustments to MinExecutionEnvironments or TargetResourceUtilization to resolve the throttling.

Install

  1. Download the skill as a ZIP:

    https://autogineer.com/agent-skills/awslabs/aws-lambda-managed-instances/download

  2. Unzip it into the cross-client skills folder of your project:

    mkdir -p .agents/skills && unzip aws-lambda-managed-instances.zip -d .agents/skills

Documentation by AWS Labs

AWS Lambda Managed Instances (LMI)

Run Lambda functions on current-generation EC2 instances in your account while AWS manages provisioning, patching, scaling, routing, and load balancing. Combines Lambda's developer experience with EC2's pricing and hardware options.

For standard Lambda development, see aws-lambda skill. For SAM/CDK deployment, see aws-serverless-deployment skill.

When to Load Reference Files

Quick Decision: Is LMI Right for This Workload?

SignalLMI is a strong fitStandard Lambda is better
TrafficSteady, predictable, 50M+ req/moBursty, unpredictable, long idle
CostDuration-heavy spend at scaleLow or sporadic invocations
Cold startsUnacceptable (LMI eliminates for provisioned capacity; scale-out may have brief delays)Tolerable or mitigated by SnapStart
ComputeLatest CPUs, specific families, high network bandwidthStandard Lambda memory/CPU sufficient
IsolationDedicated EC2 instances in your account, full VPC controlShared Firecracker micro-VMs acceptable
Scale-to-zeroNot needed (execution environments always running)Required (pay nothing when idle)
Code readinessThread-safe (Node.js/Java/.NET) or any Python codeNon-thread-safe Node.js/Java/.NET, expensive to change

Instructions

Step 1: Assess the Workload

Gather these signals before recommending:

  1. Traffic pattern: Steady vs bursty? Requests per second?

  2. Current costs: Monthly Lambda spend? Existing Savings Plans?

  3. Runtime: Node.js, Java, .NET, or Python?

  4. Memory/CPU: How much memory? CPU-bound or I/O-bound?

  5. Execution duration: Average and P99?

  6. Concurrency readiness: Thread safety (Node.js/Java/.NET)? Shared /tmp paths? Per-invocation DB connections?

  7. VPC: Already in a VPC? Private resource access needed?

Deriving LMI Configuration from Metrics

If Lambda Insights is enabled on the function, use these metrics to calculate your starting configuration. If Lambda Insights is not enabled, suggest adding it to gather accurate workload data — but only proceed with the user's explicit confirmation, as adding the Insights layer may affect function performance or cold start times.

To check if Lambda Insights is enabled, look for a LambdaInsightsExtension layer on the function. To add it, find the latest layer ARN for your region from the Lambda Insights documentation and attach the CloudWatchLambdaInsightsExecutionRolePolicy managed policy to the function's execution role.

Target max concurrency (from cpu_total_time and Duration):

PerExecutionEnvironmentMaxConcurrency = floor((0.5 × Duration) / cpu_total_time)

This targets 50% CPU utilization at full concurrency, leaving headroom for scaling.

Memory allocation (from memory_utilization and current memory):

MemorySize = min(32768, max(2048, MaxConcurrency × (memory_utilization / 100) × current_allocated_memory))

This overestimates (assumes no shared base memory) but provides a safe starting point. The outer min caps the result at the 32 GB (32768 MB) LMI maximum.

Minimum execution environments (from baseline ConcurrentExecutions):

MinExecutionEnvironments = max(3, ceil(baseline_concurrent_executions × 2 / MaxConcurrency))

Targets 50% concurrency utilization to leave headroom for traffic bursts.

Without Lambda Insights: Start with the runtime's default max concurrency, 2 GB memory, and MinExecutionEnvironments = 3. Adjust during testing.

Step 2: Build the Cost Comparison

REQUIRED: Present a cost comparison before recommending LMI. Compare at minimum:

ScenarioWhen it wins
Lambda on-demandLow volume, bursty traffic
LMI on-demandHigh volume, steady traffic

Rule of thumb: LMI becomes cost-competitive when your Lambda spend exceeds ~$1,000/month with steady traffic.

For discount analysis (Savings Plans, Reserved Instances), refer users to the AWS Pricing Calculator and references/cost-comparison.md for formulas and worked examples. Discount recommendations require workload-specific forecasting beyond this skill's scope.

Step 3: Configure the Deployment

Instance families (~450 types): C-series (compute, .xlarge+), M-series (general, .large+), R-series (memory, .large+). ARM (Graviton) for best price-performance.

Memory-to-vCPU ratios: 2:1 (default, CPU-bound work), 4:1 (general/mixed workloads), 8:1 (memory-heavy or Python apps). Min 2 GB, max 32 GB.

Multi-concurrency defaults/vCPU: Node.js 64, Java 32, .NET 32, Python 16.

Scaling: MinExecutionEnvironments (default 3), MaxVCpuCount (default 400), TargetResourceUtilization.

Scheduled scaling: For predictable traffic (business hours, marketing events), use EventBridge Scheduler to adjust Min/Max execution environments on a one-time or recurring schedule — scale up before peak, scale down or to zero when idle.

See references/configuration-guide.md for decision trees and detailed tuning.

Step 4: Migrate the Code

Review code for concurrency safety. LMI runs multiple invocations concurrently per execution environment, but the model differs by runtime:

  • Python: Process-based isolation — globals are NOT shared. No thread-safety changes needed. Focus on /tmp conflicts and memory sizing (per-process × concurrency).

  • Node.js: Worker threads — globals shared within a worker. Requires async safety. Callback handlers not supported on Node.js 22.

  • Java/.NET: OS threads/Tasks — handler shared across threads. Requires full thread safety.

Common issues (all runtimes): shared /tmp paths, per-invocation DB connections. Thread-safety issues (Node.js/Java/.NET only): mutable globals, non-thread-safe libs.

See references/thread-safety.md for the review checklist and references/migration-patterns.md for runtime-specific before/after code.

Step 5: Set Up Infrastructure

  1. Create two IAM roles: execution role (for the function) and operator role (for capacity provider EC2 management)

  2. Configure VPC with subnets across multiple AZs (recommended 3+ for resiliency)

  3. Create capacity provider with VPC config and scaling limits

  4. Create or update function with capacity provider attachment

  5. Publish a version (triggers instance provisioning)

See references/infrastructure-setup.md for CLI commands and SAM templates.

Step 6: Validate and Cut Over

  1. Deploy to a non-production environment first

  2. Monitor CloudWatch: CPU utilization, memory, concurrency, throttle rate. If you observe low CPU utilization or ongoing throttles, see references/troubleshooting.md for metric-specific adjustment guidance.

  3. Shift traffic to the LMI function (note: weighted alias shifting between LMI and non-LMI functions is not currently supported)

  4. Compare costs after 1-2 weeks of production data

  5. Decommission standard Lambda once stable

Best Practices

Configuration

  • Do: Start with 2:1 ratio and runtime default concurrency

  • Do: Use ARM (Graviton) unless x86 dependencies exist

  • Do: Let Lambda choose instance types unless specific hardware needed

  • Do: Set MaxVCpuCount to control cost ceiling

  • Don't: Set MinExecutionEnvironments below 3 in production (reduces multi-AZ coverage). Non-prod environments can use 1 as the minimum.

  • Don't: Over-restrict instance types (lowers availability)

Migration

  • Do: Start with I/O-heavy functions (benefit most from multi-concurrency; CPU-bound functions compete for same CPU)

  • Do: Review code for concurrency safety before attaching to capacity provider (thread safety for Node.js/Java/.NET; /tmp and memory for Python)

  • Do: Plan traffic shifting strategy based on your invocation source (weighted alias shifting between LMI and non-LMI functions is not currently supported)

  • Do: Include request IDs in all log statements

  • Do: Initialize DB pools and SDK clients outside the handler

  • Do: Estimate total /tmp usage under max concurrency

  • Don't: Write to hardcoded /tmp paths without request-unique naming

  • Don't: Skip cost comparison — LMI is not always cheaper

Operations

  • Do: Set CloudWatch alarms on throttle rate > 1% and CPU > 80%

  • Do: Use scheduled scaling (EventBridge Scheduler) for predictable traffic — raise Min/Max before peak periods and lower them (or scale to zero) when idle

  • Don't: Manually terminate LMI EC2 instances (delete the capacity provider instead)

  • Don't: Forget to publish a version — unpublished functions cannot run on LMI

  • Don't: Rely on a deactivated (Min=Max=0) function to self-recover — schedule an explicit scale-up to reactivate it

Limits Quick Reference

ResourceLimit
Memory2 GB min, 32 GB max
Concurrency/vCPU64 (Node.js), 32 (Java/.NET), 16 (Python)
Instance lifespan~12 hours (auto-replaced by Lambda)
EE lifespan~4 hours (auto-replaced by Lambda)
RuntimesNode.js, Java, .NET, Python
Instance familiesC (.xlarge+), M (.large+), R (.large+)
ScalingDoubles within 5 min without throttles

Troubleshooting Quick Reference

IssueCauseFix
429 throttlesTraffic exceeds scaling speedIncrease MinExecutionEnvironments or lower TargetResourceUtilization
Function stuck PENDINGProvisioning instancesWait; check VPC/IAM config
Architecture mismatchFunction ≠ capacity provider archAlign both to same architecture
Cannot terminate instancesManaged by capacity providerDelete capacity provider instead
Race conditionsCode not thread-safeSee references/thread-safety.md

See references/troubleshooting.md for detailed resolution steps.

Configuration

AWS CLI Setup

REQUIRED: AWS credentials configured on the host machine.

Verify access: Run aws sts get-caller-identity

Regional Availability

Available in all commercial AWS Regions except Israel (Tel Aviv), Middle East (Bahrain), Middle East (UAE), and Asia Pacific (Auckland).

Check the Lambda Managed Instances documentation for the latest regional availability.

Language Selection

Default: TypeScript

Override: "use Python" → Python, "use JavaScript" → JavaScript. When not specified, ALWAYS use TypeScript.

IaC Framework Selection

Default: CDK

Override: "use SAM" → SAM YAML, "use CloudFormation" → CloudFormation YAML. When not specified, ALWAYS use CDK.

Error Scenarios

Serverless MCP Server Unavailable

  • Inform user: "AWS Serverless MCP not responding"

  • Ask: "Proceed without MCP support?"

  • DO NOT continue without user confirmation

Unsupported Runtime

  • State: "Lambda Managed Instances does not yet support [runtime]"

  • List supported runtimes

  • Suggest standard Lambda as alternative

Unsupported Region

  • State: "Lambda Managed Instances is not available in [region]"

  • Name the excluded regions: Israel (Tel Aviv), Middle East (Bahrain), Middle East (UAE), Asia Pacific (Auckland)

  • Suggest the nearest supported region

Resources

Files

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Requirements and compatibility

Written for: Claude Code

Works with: Claude Code, Claude apps, OpenAI Codex, GitHub Copilot, Cursor, Gemini CLI, Kiro, OpenCode, Goose, Amp, Roo Code, JetBrains Junie, Factory Droid, Devin (ex-Windsurf), Mistral Vibe, OpenHands, Letta

“Works with” means the tool reads the open Agent Skills format; a skill written for one tool may use features others lack.

Needs: Claude Code frontmatter, Needs an MCP server

Security

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Attribution and licence

Published by
AWS Labs
Publisher
AWS Labs (Official)
Source
awslabs/agent-plugins · plugins/aws-serverless/skills/aws-lambda-managed-instances · commit fbefb37
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Apache License 2.0 (redistribution allowed)
  • LICENSE (added by AutoGineer from LICENSE)
  • NOTICE (added by AutoGineer from NOTICE)
  • ATTRIBUTION.md (written by AutoGineer)
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