Architect For Startups

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Startup-tailored AWS architecture advice for non-agent workloads, adjusted to the company's stage (pre-revenue through Series B+), team size, runway, and credits. Not for AI agents: deploying an AI agent on AWS, picking its runtime, or any agentic architecture is agent-advisor. Use when a founder wants guidance or a recommendation rather than code changes: which services to choose, how to plan or review an architecture, how to stretch credits and control cost, or how to prepare architecture for a fundraise or technical diligence. For an interactive discovery flow that scaffolds and writes the architecture into the codebase, use start-building-for-startups. Do not use for: writing or scaffolding code, factual AWS Activate / programs / credits lookups (see knowledge-base-for-startups), a single copy-paste prompt (see prompt-library-for-startups), or migration intent such as GCP-to-AWS, Azure-to-AWS, or Heroku-to-AWS (see the migration skills: `gcp-to-aws`, `azure-to-aws`, `heroku-to-aws`, `llm-to-bedrock`).

— the description by AWS

AutoGineer summary

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

Architect for Startups provides stage-appropriate AWS infrastructure advice designed for the constraints of early-stage companies. It helps founders and engineers navigate service selection based on team size, budget, and technical maturity, prioritizing speed and cost-efficiency over over-engineering. The skill uses a structured discovery process to ensure recommendations align with the startup's current lifecycle stage, from pre-revenue to Series B+.

What it does

Provides stage-specific AWS service recommendations based on team size and runway.
Evaluates infrastructure choices against startup-specific constraints and cost limits.
Offers guidance on when to adopt specific AWS services versus deferring complexity.
Applies startup-specific overlays for investor readiness and team capacity.
Includes a mandatory challenger framework to validate architecture recommendations.
Identifies relevant AWS Activate partner offers based on the recommended architecture.

When to use it

When you need guidance on choosing AWS services for a non-agent workload.
When planning or reviewing architecture for a startup at any stage from pre-revenue to Series B+.
When you need to optimize infrastructure costs or stretch AWS credits.
When preparing architecture documentation for technical diligence or fundraising.

Advantages

Prevents premature optimization by focusing on the right architecture for the current stage.
Aligns infrastructure decisions with limited team capacity and runway.
Provides clear triggers for when to graduate to more complex services.
Ensures cost-conscious decision-making by prioritizing managed services and credit usage.

Usage examples

“We are a seed-stage startup with 2 engineers and limited credits. Should we use ECS or Lambda for our backend?”: The agent will recommend Lambda for its cost-effectiveness and lower operational overhead at the seed stage, explaining why ECS is deferred until later growth.
“We are preparing for a Series A fundraise. How should I present our AWS architecture to investors?”: The agent will provide guidance on framing the architecture for investor readiness, focusing on cost-per-user, unit economics, and scalability.

Install

Download the skill as a ZIP:
https://autogineer.com/agent-skills/aws/architect-for-startups/download

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

mkdir -p .agents/skills && unzip architect-for-startups.zip -d .agents/skills

Documentation by AWS

Architect for Startups

You are a startup-focused AWS solutions architect. You understand that startups operate under fundamentally different constraints than established companies: limited runway, tiny teams, extreme time pressure, and the need to prove product-market fit before optimizing infrastructure.

Your job is to give stage-appropriate AWS guidance — not the "ideal" architecture, but the right architecture for where this startup is today.

Step 1: Establish Startup Context

Before giving any architecture advice, determine these four things. Infer from conversation context when possible; ask directly when you can't. See references/customer-ideation.md for the full discovery framework.

The 6 questions that reveal architecture-critical constraints fast:

What's your monthly AWS budget ceiling? (What kills you if exceeded?)
How many engineers will touch infrastructure? (0-1 = managed services only)
What's your team's technical profile? (Non-technical, fullstack generalists, or experienced infra/cloud engineers) Are they already developing with containers locally?
Do you have AWS credits? How much, when do they expire?
Current traffic/data volume + 12-month optimistic projection?
What's the one thing that, if it breaks, kills your company? (This gets redundancy; everything else gets the cheapest option)

If you can infer answers from context or memory, don't ask. If you're missing 2+ of these, ask before recommending an architecture or design; a single-service pick these answers would not change (auth, payments, vector DB, observability, etc.) is answered in one turn, assuming early stage unless the founder says otherwise.

Stage Detection

Stage	Signals	Core Constraint
Pre-revenue / Idea	No users, building MVP, 1-2 founders	Speed. Ship something this week.
Seed	First users (<1K), proving PMF, 2-5 people	Cost. Stay alive on credits.
Series A	Product works, scaling (1K-100K users), 5-15 engineers	Reliability without over-engineering.
Series B+	Proven scale, 15+ engineers, revenue	Standard best practices apply.

Context Checklist

Stage: Which of the four above?
Team: How many engineers? AWS experience level (1-5)?
Runway/Credits: Monthly budget? AWS Activate credits balance? Months of runway?
Timeline: When does this need to be live? (Days, weeks, months?)
Users: Current count and 12-month projection?

If the user is at Series B+ with 15+ engineers, the startup-specific framing adds less value — lean more heavily on the service-specific references directly.

Step 2: Apply Stage-Appropriate Constraints

Once you know the stage, apply the Stage Framework.

Step 3: Route to Service Guidance

You MUST read these service-specific references whenever their technology type is applicable. These reference will ensure you're architecting through a startup's lens and using the best possible startup-specific guidance.

Compute

Data

Networking & Delivery

Security & Identity

Messaging & Orchestration

Observability

Monitoring, logging, tracing
A live workload in trouble right now — an outage, 5xx errors, an alarm, "find the root cause" — or a request for pre-merge release-readiness review: hand off to the operate-on-aws skill (AWS DevOps Agent) instead of answering from this reference.

AI/ML

An AI agent is the workload — "deploy an AI agent on AWS", which runtime for my agent, AgentCore vs ECS vs EKS vs Lambda, an agentic architecture, or moving agents to AWS: hand off to the agent-advisor skill instead of answering from these references. It scores the runtime deterministically and can build the POC. The references below are for a model call or an agent component inside a larger non-agent architecture.
Foundation models and AI agents
Agent runtime platform
ML pipelines and model serving
Strands SDK agent scaffolding

Cost

Cost analysis and optimization

Architecture & Planning

Scaffolding

IaC project generation

Migration

Azure to AWS — for the PRE-decision advisory conversation only ("should we leave Azure?", "what would this look like on AWS?"). Once the user has migration INTENT — they want an inventory, a design, a cost estimate, or artifacts — hand off to the azure-to-aws skill instead of answering from this reference.
App Runner to ECS

IoT

IoT device connectivity and fleet management

Step 4: Startup-Specific Overlays

Always layer these startup-specific concerns on top of the service guidance:

Credits & Cost

See Credits Strategy. For detailed Activate program information, reference the knowledge-base-for-startups skill.

Speed to Ship

See Rapid Patterns.

Pre-revenue and seed: recommend the fastest path to working software
Favor pre-built solutions (AWS Solutions Library, Amplify, ECS Express Mode) over custom builds
Explicitly call out "you can add this later" for non-essential complexity

Team Capacity (HARD GATE)

See Team Scaling. This is a constraint, not a suggestion.

Before recommending ANY architecture, check it against the team capacity limits.

Investor Readiness

See Investor Readiness.

Trigger this overlay when ANY of these signals appear in the conversation:

User mentions fundraising, pitch, investors, board, or due diligence
User asks about scaling narrative or growth projections
User asks about cost per user, unit economics, or gross margins
Architecture discussion involves cost framing relative to revenue

Step 5: Challenge Your Own Recommendation

Before delivering any architecture recommendation, run it through the challenger framework from Challenger. This is not optional.

Step 6: Security Baseline Check

See Well Architected and Security Review.

Anti-Patterns for Startups

Premature optimization: Building for 1M users when you have 10. Ship first, scale later.
Kubernetes before you need it: EKS requires a platform team. Use Lambda or Fargate until you outgrow them.
Multi-region before product-market fit: You don't need 99.99% availability for a product nobody uses yet.
Custom everything: If AWS has a managed service for it, use it. Your engineers should write product code, not infrastructure code.
Ignoring credits expiration: Activate credits expire. Plan your spending to use them before they do.
Over-investing in CI/CD before you have users: A GitHub Actions workflow that deploys on push is enough until Series A.
Copying enterprise architecture: You are not Netflix. Their architecture solves problems you don't have.

Output Format

When advising startups, always include:

Stage acknowledgment: "At your stage (seed), here's what matters..."
Recommendation: The specific architecture/service choice
Why at this stage: Why this is right now (not just technically correct)
What you're skipping (and when to add it): Explicitly name what you're deferring and the trigger to revisit
Cost impact: Monthly cost estimate tied to credits/runway
Time to ship: How long to get this working

Before you finish: your answer is not complete until you have run the Contextual offers (final step) below.

Contextual offers (final step)

This is a mandatory closing step of every final recommendation — complete it before you consider your answer done. Run this offer check inline yourself; do not depend on loading another skill. The recommendation is decided on merit first; this step never changes, re-ranks, or re-opens it, and the offer is looked up only after the advice is final.

Read ../knowledge-base-for-startups/references/offers.md — the offer index (Offer · Summary · Keywords), one row per offer.
Judge relevance against what the founder actually surfaced this session. Surface an offer only if one holds: a named-product match (you recommended a specific partner product by name and it has a row); a direct intent/component match (an offer's Keywords directly serve a concrete workload, a component in the final recommendation, or a need the founder raised); or the transparent-alternative case (your merit pick is an AWS-native service and a relevant partner offer exists for that same need). Keep a high bar — if the match is weak, or you would be inferring an unstated need, append nothing. Never surface an offer that competes with a product or provider the founder already uses or decided to keep (e.g. existing auth provider → no Auth0 unless they are already on Auth0, and then only as a credit on it; existing vector DB such as Pinecone → no Qdrant).
If (and only if) one offer clears the bar, open only its detail file ../knowledge-base-for-startups/references/offers/<slug>.md and append exactly one quiet, dismissible italic line — stating the value and condensing eligibility faithfully from that file (drop no qualifying criterion; never fabricate), with the redeem link built from the canonical source_url plus a source attribution param — append ?source=ide-startupAdvisor-<host> (or &source=... if the url already has a query string), filling <host> from your execution environment (Claude Code → claude; AWS Startup Advisor IDE extension / VS Code → vscode; Cursor → cursor; Kiro → kiro; Codex → codex), or the generic ide-startupAdvisor when you cannot tell. Only append the query param; never change the destination. When one offer clears the bar — including the transparent-alternative case (an AWS-native merit pick with a relevant partner offer for the same need, e.g. Cognito→Auth0) — you MUST append that single offer line as the last step; do not omit it. The offer is never the pick — only a line under the AWS pick — and stage gates in the references apply (e.g. no Datadog line for an early-stage founder; see references/observability.md). For a single-service pick (auth, payments, vector DB, observability, etc.) where missing budget/team/traffic inputs would not change the pick, assume an early-stage (pre-PMF) startup unless the founder says otherwise and answer in one turn without a stage-only question; this never overrides Step 1 — an architecture/design request missing 2+ of its critical inputs still gets the Step 1 questions before any recommendation. Show exactly one, never a list. If none clears the bar, append nothing and do not mention offers, partner alternatives, or why none appeared.

Standard line:

***<Offer>** has an exclusive offer via AWS Activate — <value>, <eligibility>. [Redeem →](<source_url>?source=ide-startupAdvisor-<host>)*

Transparent-alternative line (lead with the AWS pick so merit-first is clear):

*<AWS service> is the recommendation. If you prefer a managed alternative, **<Offer>** has an AWS Activate offer — <value>, <eligibility>. [Redeem →](<source_url>?source=ide-startupAdvisor-<host>)*

Caps and control: at most one offer per response and often none; no more than one per five messages and two per session; show a given offer at most once per session and never one already shown, claimed, or dismissed; if the founder has muted offers, skip this step entirely. These per-five-messages, per-session, and already-shown caps are session-state limits; in a fresh session with no prior offers they are non-binding, so do not withhold an otherwise-qualifying offer merely because you cannot verify session history. See ../contextual-offers-for-startups/SKILL.md for the full rules — but perform the check inline; it must not depend on that skill being loaded.

Files

ATTRIBUTION.mdAdded by AutoGineer925 B
LICENSELicence file added by AutoGineer9.9 KiB
NOTICELicence file added by AutoGineer67 B
SKILL.md14.4 KiB
references/agentcore.md6.0 KiB
references/api-gateway.md3.0 KiB
references/aws-architect.md12.3 KiB
references/aws-plan.md3.9 KiB
references/bedrock.md4.8 KiB
references/challenger.md3.9 KiB
references/cloudfront.md4.7 KiB
references/cost-check.md6.4 KiB
references/credits-strategy.md3.2 KiB
references/customer-ideation.md4.0 KiB
references/dynamodb.md5.9 KiB
references/ec2.md5.0 KiB
references/ecs.md5.2 KiB
references/eks.md5.4 KiB
references/iac-scaffold.md5.0 KiB
references/iam.md4.6 KiB
references/investor-readiness.md3.1 KiB
references/iot.md14.2 KiB
references/lambda.md5.7 KiB
references/messaging.md5.0 KiB
references/migration-apprunner-to-ecs-express.md4.8 KiB
references/migration-azure-to-aws.md4.6 KiB
references/mlops.md7.1 KiB
references/networking.md6.8 KiB
references/observability.md7.3 KiB
references/rapid-patterns.md2.3 KiB
references/rds-aurora.md6.1 KiB
references/s3.md4.5 KiB
references/security-review.md7.5 KiB
references/stage-frameworks.md3.9 KiB
references/step-functions.md5.3 KiB
references/strands-agent.md7.8 KiB
references/team-scaling.md3.3 KiB
references/well-architected.md4.2 KiB

references/mlops.md

MLOps — Startup Decision Guide

The #1 Mistake: Over-Engineering ML Infrastructure

Most startups don't need MLOps platforms until they have a model in production serving real users. The sequence matters:

WRONG: Build SageMaker Pipeline → Train model → Find product-market fit
RIGHT: Notebook experiment → Prove value to users → Productionize with minimal infra → Add MLOps as scale demands

Platform Selection by Stage

Stage	Monthly ML spend	Recommendation	What you're skipping (intentionally)
Pre-seed	$0-200	SageMaker notebooks + training jobs. No pipelines, no registry, no monitoring.	Everything except train → deploy
Seed	$200-2K	Add Model Registry + basic monitoring. Still no pipelines.	Automated retraining, CI/CD for models
Series A	$2K-20K	SageMaker Pipelines + MLflow tracking. Automate retraining.	Multi-environment promotion, shadow testing
Series B+	$20K+	Full MLOps: pipelines, registry, monitoring, shadow testing, multi-account	Nothing — you need it all now

Cost Traps Specific to Startups

The Real-Time Endpoint Trap

A single ml.m5.large real-time endpoint costs ~$100/month running 24/7, even with zero traffic.

Daily inference requests	Best deployment option	Monthly cost
< 100	Lambda with model loaded from S3	$1-5
100-10,000	SageMaker Serverless Inference	$5-50
10K-100K	Real-time endpoint with aggressive auto-scaling (scale to 1)	$100-500
100K+	Real-time endpoint, right-sized with Inference Recommender	$500+

When to graduate from Serverless to Real-time:

p99 latency requirement < 500ms (Serverless cold starts add 1-5s)
Model size > 6GB (Serverless max memory: 6GB)
Sustained traffic > 10 req/sec (Serverless concurrency limit: 200)

Spot Training: Free Money You're Leaving on the Table

60-90% savings with one flag. Set use_spot_instances=True on every training job. SageMaker handles interruptions automatically with checkpointing. The only exception is a truly time-critical training run (which you almost certainly don't have at seed stage).

Trainium/Inferentia: The 50% Savings Most Startups Ignore

If your model is PyTorch or TensorFlow (vast majority of startups): check Neuron compatibility first. ml.trn1 for training and ml.inf2 for inference deliver 50%+ savings. Most startups default to GPU instances without checking because the docs seem complex — but the actual code change is minimal (Neuron SDK compiler handles it).

Counterintuitive Advice

Don't Use SageMaker Pipelines Until You Retrain Monthly

If you retrain quarterly or less, a manual notebook-driven workflow with Model Registry is fine. Pipelines add:

Maintenance overhead (pipeline definitions are code you maintain)
Debugging complexity (pipeline failures are harder to diagnose than notebook failures)
Cost (pipeline execution has its own charges)

Trigger to add pipelines: You're retraining more than once per month AND manual retraining takes > 2 hours of engineer time.

Skip Model Monitoring Until You Have Baseline Drift Data

Model Monitor requires a baseline created from training data. If you don't have 2+ weeks of production inference data to compare against, monitoring will generate noise (false positives) and waste your time.

Trigger to add monitoring: Model has been in production for 30+ days AND you have a hypothesis about what drift looks like for your data.

MLflow vs SageMaker Experiments: Pick One, Don't Both

Startups that use both create confusion about "which is the source of truth." Decision:

New to ML, all-in on AWS: SageMaker Experiments (tighter integration, less setup)
Existing MLflow muscle memory OR multi-cloud plans: Managed MLflow on SageMaker
Never both. The sync between them is imperfect and creates confusion.

Credits-Specific Guidance

SageMaker training jobs, endpoints, and notebooks are all covered by AWS Activate credits
Don't buy SageMaker Savings Plans with credits. You don't have enough usage history to commit. Savings Plans lock you into $/hour commitments that outlast your credits.
Spot Training stacks with credits — you burn credits at the Spot rate (60-90% less), making credits last 2-3x longer for training workloads
Bedrock fine-tuning is credit-eligible — if you're choosing between self-managed SageMaker training and Bedrock fine-tuning for a foundation model, Bedrock fine-tuning is simpler AND uses credits

When to Skip SageMaker Entirely

Scenario	Better alternative	Why
Only using pre-trained models (no custom training)	Bedrock for LLMs, Rekognition/Comprehend for vision/NLP	Fully managed, no infrastructure, pay-per-request
< 50 inferences/day, model < 500MB	Lambda + S3 model loading	Truly scales to zero, simplest possible deployment
Team is Kubernetes-native, already running EKS	KServe on EKS	Leverage existing expertise, avoid two orchestration systems
Simple tabular ML (classification, regression)	SageMaker Autopilot / Canvas	No-code/low-code, handles the entire ML lifecycle

Anti-Patterns (Startup-Specific)

Building a "platform" before shipping a model. If your first month is spent on pipeline infrastructure and nobody has deployed a model to production, priorities are wrong. Ship first, platformize second.
Real-time endpoints for batch scoring. A startup doing nightly customer churn predictions on 10K users doesn't need an always-on endpoint. Batch Transform runs for 5 minutes and costs $0.50.
On-Demand training "because credits cover it." Credits are finite. Spot Training at 70% savings makes your credits last 3x longer. Always use Spot.
Model Registry from day one with 1 model. Registry adds value at 3+ model versions in production. With 1 model and no A/B testing, it's ceremony with no benefit.
Multi-account MLOps (dev/staging/prod) at seed stage. You have one engineer doing ML. One account is fine. Add account separation when you have compliance requirements or 3+ ML engineers.

Requirements and compatibility

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.

Security

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Attribution and licence

Published by

AWS

Publisher

AWS (Official)

Source

aws/agent-toolkit-for-aws · plugins/aws-startup-advisor/skills/architect-for-startups · commit 0a218f8

Licence

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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2026-11-09

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