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Aws Lambda Managed Instances

Organization
awslabs
aws-lambda-managed-instances

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.

Overview

Publisherawslabs
Repositoryagent-plugins
Skill nameaws-lambda-managed-instances
Stars
917
Forks
162
Bundled files
7
LicenseApache-2.0
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 7 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by awslabs on GitHub. Read the source before you install it.

Installation

Install the Aws Lambda Managed Instances AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/awslabs/agent-plugins/tree/main/plugins/aws-serverless/skills/aws-lambda-managed-instances
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/awslabs/agent-plugins.git /tmp/agent-plugins
mkdir -p .claude/skills
cp -r /tmp/agent-plugins/plugins/aws-serverless/skills/aws-lambda-managed-instances .claude/skills/aws-lambda-managed-instances
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Aws Lambda Managed Instances in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Aws Lambda Managed Instances on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Aws Lambda Managed Instances is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

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

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Aws Lambda Managed Instances AI skill do?

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...

Why use Aws Lambda Managed Instances on TypingMind?

Because you install it once and use it with any model. Aws Lambda Managed Instances is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Aws Lambda Managed Instances in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/awslabs/agent-plugins/tree/main/plugins/aws-serverless/skills/aws-lambda-managed-instances. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Aws Lambda Managed Instances?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Aws Lambda Managed Instances?

As many as you like. As long as a model supports skills, you can use Aws Lambda Managed Instances with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Aws Lambda Managed Instances AI skill free?

Yes. It is published on GitHub by awslabs under the Apache-2.0 license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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