Amazon Quick Suite vs Amazon Q: Understanding AWS’s AI Offerings

AWS recently announced Amazon Quick Suite on October 9, 2025, marking a significant evolution in their AI and business intelligence offerings. For many AWS users, this raises an important question: How does Quick Suite differ from Amazon Q? This post breaks down both services, their use cases, and how they complement each other.

TL;DR

Feature Amazon Quick Suite Amazon Q
Primary Focus Business intelligence + automation + research workspace AI assistant for developers and businesses
Target Users Business analysts, operations teams, knowledge workers Developers, DevOps, business users
Main Capabilities BI dashboards, workflow automation, enterprise research Code assistance, chat, AWS service help
Use Case Analyze data, automate workflows, research Write code, troubleshoot, answer questions
Previous Name Amazon QuickSight (evolved) New service (2023)
Integration Unified workspace with multiple agents Standalone AI assistant

What is Amazon Quick Suite?

Announced on October 9, 2025, Amazon Quick Suite is AWS’s new unified digital workspace that combines business intelligence, research, and automation capabilities powered by agentic AI.

Evolution from QuickSight

Quick Suite is the evolution of Amazon QuickSight. Existing QuickSight customers are automatically upgraded to Quick Suite, which includes:

  • ✅ All existing QuickSight BI capabilities (now called “Quick Sight”)
  • ✅ New agentic AI capabilities for research and automation
  • ✅ Same data connections, security, and permissions

Important: This is an interface and capability upgrade—no data migration required.

Quick Suite Components

1. Quick Index - Unified Knowledge Foundation

The backbone of Quick Suite that consolidates all your enterprise data:

┌─────────────────────────────────────────┐
│          Quick Index                    │
│  (Unified Knowledge Repository)         │
└─────────────────────────────────────────┘
            │
    ┌───────┼───────┬───────────┐
    ▼       ▼       ▼           ▼
┌────────┐ ┌────┐ ┌──────┐ ┌─────────┐
│Databases│ │S3  │ │Email │ │SharePoint│
└────────┘ └────┘ └──────┘ └─────────┘

What it does:

  • Creates searchable repository of documents, files, and application data
  • Automatically indexes uploaded content
  • Powers AI responses across Quick Suite
  • Connects to S3, Snowflake, Google Drive, SharePoint, etc.

Example: Search for “Q3 sales report” and instantly get results from documents, emails, databases, and dashboards—all in one unified search.

2. Quick Research - AI-Powered Research Agent

Conducts comprehensive research across enterprise and external sources.

Workflow:

User Question
    ↓
Research Plan Created (automatically)
    ↓
User Refines Plan (via chat)
    ↓
Agent Gathers Data (background processing)
    ↓
Analysis with Citations

Use Cases:

  • Competitive intelligence: “Analyze competitor pricing strategies in Q3”
  • Market research: “What are the top trends in AI adoption for financial services?”
  • Internal research: “Summarize all product feedback from the last quarter”

Key Features:

  • Breaks complex questions into research frameworks
  • Works with enterprise data + external sources
  • Provides citations and reasoning paths
  • Validates findings automatically

3. Quick Sight - AI-Powered Business Intelligence

The evolved QuickSight with enhanced AI capabilities.

New Capabilities:

  • Natural language queries: “Show me sales trends by region for Q3”
  • Auto-generated dashboards: Create visualizations from prompts
  • What-if analysis: “What if we increase marketing spend by 20%?”
  • One-click actions: Create tickets, send alerts from dashboards

Example:

User: "Show me top 10 customers by revenue and their growth rate"
       ↓
Quick Sight generates:
  - Bar chart of customer revenue
  - Line chart showing growth trends
  - Executive summary with insights

4. Quick Flows - No-Code Automation

Enables anyone to automate workflows using natural language.

Example Flow:

Trigger: "When a new customer signs up"
    ↓
Fetch: Customer data from Salesforce
    ↓
Process: AI generates personalized welcome email
    ↓
Action: Send email via Amazon SES
    ↓
Record: Log in Salesforce

Use Cases:

  • Customer onboarding workflows
  • Report generation and distribution
  • Data synchronization between systems
  • Alert and notification automation

Quick Flows Limits:

  • ⚠️ Maximum 35 steps per flow
  • ⏱️ No explicit execution time limit (controlled by agent hours quota)
  • 💰 Consumes agent hours from your subscription allowance
  • 🔄 For longer/more complex workflows, use Quick Automate instead

More Quick Flows Examples:

Example 1: Weekly Sales Report Automation

Trigger: Every Monday at 9 AM
    ↓
Query: Pull last week's sales data from Redshift
    ↓
Generate: AI creates executive summary with insights
    ↓
Create: PDF report with charts
    ↓
Send: Email to leadership team
    ↓
Archive: Store in S3 bucket

Example 2: Support Ticket Automation

Trigger: New support ticket created in Zendesk
    ↓
Analyze: AI categorizes ticket urgency and topic
    ↓
Route: Assign to appropriate team based on category
    ↓
Update: Post status to Slack channel
    ↓
Monitor: Track response time SLA

Example 3: Invoice Processing

Trigger: Invoice uploaded to S3
    ↓
Extract: AI extracts key data (vendor, amount, date)
    ↓
Validate: Check against purchase orders
    ↓
Approve: Route to manager if >$10k, auto-approve if <$10k
    ↓
Record: Create entry in accounting system
    ↓
Notify: Send confirmation email to vendor

5. Quick Automate - Enterprise Process Automation

For technical teams building complex, multi-department workflows.

Quick Flows vs Quick Automate:

Feature Quick Flows Quick Automate
Complexity Simple tasks Complex processes
Users Business users Technical teams
UI Natural language Visual builder + NLP
Features Basic automation Advanced orchestration
Examples Email automation Customer onboarding pipeline

Quick Automate Features:

  • UI agent that navigates websites autonomously
  • Human-in-the-loop approvals
  • Multi-agent orchestration
  • Real-time monitoring and audit trails
  • Version control and rollback

Quick Automate Examples:

Example 1: Employee Onboarding Automation

Trigger: HR adds new employee to Workday
    ↓
Create: Generate employee ID and email account (via API)
    ↓
Provision: Create accounts in Slack, GitHub, Jira, Confluence
    ↓
Assign: Set up permissions based on role and department
    ↓
UI Agent: Navigate internal portal to request hardware
    ↓
Generate: Create personalized onboarding checklist
    ↓
Schedule: Book orientation sessions automatically
    ↓
Human Approval: Manager reviews and approves access levels
    ↓
Execute: Finalize all provisioning
    ↓
Notify: Send welcome email with credentials and next steps
    ↓
Monitor: Track onboarding progress with daily status updates

Example 2: Financial Close Process

Trigger: Month-end close initiated
    ↓
Agent 1 (Data Collection):
    - Pull financial data from ERP system
    - Extract bank statements via UI automation
    - Gather expense reports from Concur
    - Collect revenue data from Salesforce
    ↓
Agent 2 (Reconciliation):
    - Reconcile accounts automatically
    - Flag discrepancies >$1,000
    - Generate variance reports
    ↓
Human Review: Controller reviews flagged items
    ↓
Agent 3 (Reporting):
    - Generate financial statements
    - Create board presentation
    - Populate compliance reports
    ↓
Agent 4 (Distribution):
    - Route reports to stakeholders
    - Update SharePoint with final docs
    - Archive in compliance storage
    ↓
Audit Trail: Complete log of all actions and approvals

Example 3: Customer Order Fulfillment (Multi-Agent)

Order Received from E-commerce Platform
    ↓
Agent 1 (Order Validation):
    - Verify payment processed
    - Check inventory availability
    - Validate shipping address
    - Calculate tax and duties
    ↓
Decision Point: Inventory Available?
    
    YES →
        Agent 2 (Warehouse):
            - Create pick list
            - Reserve inventory
            - Generate packing slip
            - Assign to shipping queue
        ↓
        Agent 3 (Shipping):
            - Select carrier based on rules
            - Generate shipping label via UI automation
            - Schedule pickup
            - Send tracking info to customer
        ↓
        Agent 4 (Finance):
            - Record revenue
            - Update inventory value
            - Generate invoice
    
    NO →
        Agent 5 (Procurement):
            - Check supplier availability
            - Auto-create purchase order if <$5k
            - Request manager approval if >$5k
            - Update customer with ETA
        ↓
        Human Approval: Manager reviews PO
        ↓
        Return to Agent 2 when inventory arrives
    ↓
Monitor: Real-time dashboard showing:
    - Orders in each stage
    - SLA compliance
    - Exception alerts
    - Performance metrics

Example 4: Compliance Audit Automation

Trigger: Quarterly compliance audit scheduled
    ↓
Agent 1 (Data Gathering):
    - UI automation to login to 15+ systems
    - Extract access logs
    - Pull user permission reports
    - Collect change management records
    - Screenshot evidence from web portals
    ↓
Agent 2 (Analysis):
    - Compare current vs. approved access
    - Identify segregation of duty violations
    - Flag inactive accounts with active permissions
    - Detect unusual access patterns
    ↓
Agent 3 (Remediation):
    - Auto-revoke clearly expired access
    - Generate remediation tickets for manual review
    - Assign to appropriate managers
    ↓
Human Review: Compliance team reviews findings
    ↓
Agent 4 (Documentation):
    - Generate audit report with evidence
    - Create remediation tracking dashboard
    - Update compliance database
    - Schedule follow-up reviews
    ↓
Version Control: Track all automation changes for audit trail

Example 5: Marketing Campaign Orchestration

Trigger: New product launch campaign approved
    ↓
Agent 1 (Content Creation):
    - AI generates email copy variations
    - Create landing page content
    - Generate social media posts
    - Produce ad creative briefs
    ↓
Human Approval: Marketing manager reviews content
    ↓
Agent 2 (Channel Setup):
    - Create email campaign in Mailchimp
    - Set up Google Ads campaigns
    - Schedule social posts in Hootsuite
    - Configure landing page in CMS
    ↓
Agent 3 (Audience Segmentation):
    - Query CRM for target segments
    - Create suppression lists
    - Calculate optimal send times per segment
    - Set up A/B test groups
    ↓
Agent 4 (Launch):
    - Deploy campaigns across channels
    - Monitor initial performance
    - Auto-adjust budgets based on early results
    ↓
Agent 5 (Optimization):
    - Track metrics in real-time
    - Pause underperforming ads
    - Scale winning variants
    - Generate performance reports
    ↓
Monitor: Live dashboard with:
    - Campaign performance by channel
    - Cost per acquisition
    - ROI tracking
    - Anomaly alerts

Key Differences in Implementation:

Aspect Quick Flows Quick Automate
Setup Natural language: “Send weekly report every Monday” Visual builder + detailed configuration
Complexity Linear workflows (5-10 steps) Multi-branch logic (50+ steps)
Agents Single agent execution Multiple specialized agents
Error Handling Basic retry logic Advanced error handling + rollback
Approvals Simple approve/reject Multi-level approval chains
Monitoring Basic completion status Real-time dashboards + alerts
Versioning Not version controlled Full version control + rollback

6. Spaces & Chat Agents

Spaces:

  • Personal or team workspaces with specific context
  • Upload files, connect datasets
  • Maintain access permissions
  • Scale from personal to enterprise-wide

Chat Agents:

  • Built-in insights agent for general queries
  • Custom agents for specific departments (sales, compliance, HR)
  • Configured with business context and expertise

Architecture: Account-Level vs Application Instances

Important Distinction:

Quick Suite Architecture:

  • ❌ No “application instance” concept
  • Account-level service - One Quick Suite per AWS account
  • Organization through Spaces (workspaces) and custom agents
  • Shared Quick Index across the entire account
  • All users in the account share the same Quick Suite environment
AWS Account
    └── Quick Suite (single instance)
        ├── Quick Index (shared knowledge base)
        ├── Spaces
        │   ├── Personal Space (User A)
        │   ├── Personal Space (User B)
        │   ├── Team Space (Marketing)
        │   └── Team Space (Finance)
        ├── Custom Agents
        │   ├── Sales Agent
        │   ├── Compliance Agent
        │   └── HR Agent
        ├── Dashboards (Quick Sight)
        └── Workflows (Quick Flows/Automate)

Amazon Q Business Architecture:

  • ✅ Has “Application” instances
  • Multiple Q Business applications per AWS account
  • Each application has its own index, data sources, and users
  • Isolated environments for different use cases
AWS Account
    ├── Q Business Application: HR-Assistant
    │   ├── Index
    │   ├── Data Sources (Workday, HR docs)
    │   └── Users (HR team only)
    │
    ├── Q Business Application: Finance-Assistant
    │   ├── Index
    │   ├── Data Sources (ERP, financial docs)
    │   └── Users (Finance team only)
    │
    └── Q Business Application: Engineering-Docs
        ├── Index
        ├── Data Sources (GitHub, Confluence)
        └── Users (Engineering team)

Key Implications:

Aspect Quick Suite Amazon Q Business
Isolation Spaces provide soft boundaries Applications provide hard boundaries
Data Separation Shared Quick Index with permission controls Separate indexes per application
Billing Single $250/month infrastructure fee Per-application infrastructure costs
Management Manage one environment Manage multiple applications
Use Case Unified workspace for entire org Separate apps for different departments
Scaling Add users and spaces to same instance Create new applications as needed

When This Matters:

Choose Quick Suite (account-level) if:

  • You want a unified workspace for the entire organization
  • You prefer managing one environment
  • You want shared knowledge across teams (with permissions)
  • You want to minimize infrastructure costs

Choose Q Business (application instances) if:

  • You need strict data isolation between departments
  • Different teams have completely separate use cases
  • You want independent management and configuration
  • Compliance requires separate environments

What is Amazon Q?

Amazon Q is AWS’s AI-powered assistant announced in 2023, designed to help developers and businesses with:

Amazon Q Developer (formerly Amazon Q for Developers)

Primary Use Cases:

  • Code assistance: Write, debug, and refactor code
  • Code transformation: Upgrade Java applications, migrate frameworks
  • Security scanning: Find vulnerabilities in code
  • AWS service help: Answer questions about AWS services
  • Infrastructure as Code: Generate CloudFormation/CDK templates

Example:

# Developer asks: "Write a Lambda function to process S3 events"
# Amazon Q generates:

import json
import boto3

def lambda_handler(event, context):
    s3 = boto3.client('s3')
    
    for record in event['Records']:
        bucket = record['s3']['bucket']['name']
        key = record['s3']['object']['key']
        
        # Process S3 object
        response = s3.get_object(Bucket=bucket, Key=key)
        content = response['Body'].read()
        
        # Your processing logic here
        
    return {
        'statusCode': 200,
        'body': json.dumps('Processing complete')
    }

Amazon Q Business

Primary Use Cases:

  • Answer questions about company data
  • Summarize documents and meetings
  • Generate content based on enterprise knowledge
  • Connect to 40+ enterprise applications (Salesforce, Slack, SharePoint, etc.)

Example:

User: "What was discussed in the last executive meeting?"
Q Business: "The Q3 executive meeting on Oct 15 discussed:
1. Revenue growth of 15% YoY
2. Launch of new product line in Q4
3. Hiring freeze through end of year
Sources: [Executive_Meeting_Notes_Oct15.pdf]"

Key Differences: Quick Suite vs Amazon Q

1. Scope and Integration

Quick Suite:

  • Unified workspace for BI, research, and automation
  • Multiple specialized agents working together
  • Focused on business operations and analytics

Amazon Q:

  • Standalone AI assistant for specific tasks
  • Separate tools for developers (Q Developer) and business users (Q Business)
  • Focused on productivity and knowledge retrieval

2. Primary Use Cases

Quick Suite:

Business Analyst → Analyze sales data → Create dashboard → Automate report
                                                        ↓
Research Analyst → Research competitors → Generate insights → Share findings
                                                           ↓
Operations Team → Design workflow → Automate process → Monitor execution

Amazon Q:

Developer → Ask coding question → Get code suggestion → Implement solution
                                                     ↓
Business User → Search company docs → Get summarized answer → Share knowledge

3. Automation Capabilities

Quick Suite (Quick Flows + Quick Automate):

  • ✅ Visual workflow builder
  • ✅ Multi-step process automation
  • ✅ System integrations
  • ✅ UI automation (web scraping, form filling)
  • ✅ Enterprise orchestration

Amazon Q:

  • ❌ No native workflow automation
  • ✅ Can generate code for automation (via Q Developer)
  • ✅ Integrates with 40+ apps for data retrieval

4. Business Intelligence

Quick Suite (Quick Sight):

  • ✅ Full BI platform with dashboards
  • ✅ Data visualizations
  • ✅ What-if analysis
  • ✅ Executive summaries
  • ✅ One-click actions from insights

Amazon Q:

  • ❌ No built-in BI capabilities
  • ✅ Can answer questions about data
  • ✅ Can summarize reports
  • ❌ No visualization generation

5. Research Capabilities

Quick Suite (Quick Research):

  • ✅ Comprehensive research agent
  • ✅ Multi-source data gathering
  • ✅ Research plan generation
  • ✅ Competitive intelligence
  • ✅ Citations and reasoning paths

Amazon Q:

  • ✅ Can search enterprise knowledge
  • ✅ Summarizes documents
  • ❌ No dedicated research framework
  • ❌ Limited multi-source synthesis

When to Use What?

Use Amazon Quick Suite when you need:

  1. Business Intelligence & Analytics
    • Create dashboards and visualizations
    • Perform data analysis with natural language
    • Run what-if scenarios
  2. Process Automation
    • Automate repetitive business workflows
    • Build complex multi-department processes
    • Integrate multiple systems
  3. Comprehensive Research
    • Conduct competitive analysis
    • Synthesize insights from multiple sources
    • Generate research reports with citations
  4. Unified Workspace
    • Single interface for BI, research, and automation
    • Team collaboration on data and workflows
    • Enterprise-wide data organization

Example: “We need to analyze Q3 sales data, create automated monthly reports, and research competitor pricing strategies—all in one platform.”

Use Amazon Q when you need:

  1. Developer Productivity
    • Code generation and debugging
    • AWS service documentation
    • Application modernization
    • Security scanning
  2. Quick Information Retrieval
    • Search company documentation
    • Summarize meetings and documents
    • Answer specific questions
  3. Content Generation
    • Draft emails and documents
    • Summarize reports
    • Generate meeting notes

Example: “I need help writing a Lambda function, understanding this error message, and searching our company wiki for onboarding docs.”

Can They Work Together?

Yes! Quick Suite and Amazon Q are complementary:

Scenario: New Product Launch

Amazon Q Developer:

  • Generate infrastructure code for new service
  • Help debug deployment issues
  • Provide AWS best practices

Quick Suite:

  • Quick Research: Analyze market opportunity and competitors
  • Quick Sight: Create launch dashboard tracking KPIs
  • Quick Flows: Automate customer onboarding workflow
  • Spaces: Organize all launch-related documents and data

Detailed Cost Comparison

Understanding the pricing differences between Quick Suite and Amazon Q is critical for budgeting. Here’s a comprehensive breakdown:

Amazon Quick Suite Pricing

Based on official AWS pricing, Quick Suite uses a per-user subscription + consumption + infrastructure fee model:

Base Subscription Tiers

Tier Price/User/Month What’s Included
Professional $20 Chat agents, spaces, Quick Sight (viewer), Quick Research (2 hrs), Quick Flows (2 hrs)
Enterprise $40 All Professional features + Quick Sight (author), Quick Automate, Quick Research (4 hrs), more agent hours

What’s Included in Each Tier

Professional ($20/user/month):

  • ✅ Chat with agents about enterprise data
  • ✅ Create and share custom chat agents
  • ✅ Create and share spaces
  • ✅ Create and share workflows with Quick Flows
  • ✅ View and interact with Quick Sight dashboards
  • ✅ Run Quick Sight scenarios (what-if analysis)
  • ✅ 2 research agent hours ($12 value)
  • ✅ 2 agent hours for Quick Flows ($6 value)
  • ❌ Cannot author dashboards or automations

Enterprise ($40/user/month):

  • ✅ Everything in Professional
  • ✅ Create dashboards and reports (Quick Sight authoring)
  • ✅ Create and deploy automations (Quick Automate)
  • ✅ Create and share knowledge bases
  • ✅ 4 research agent hours ($24 value)
  • ✅ 4 agent hours for Quick Flows/Automate ($12 value)

Consumption-Based Charges

1. Quick Index Storage:

  • First 50MB free per account (~5,000 documents)
  • $1.00 per MB/month for additional extracted text storage
  • Multimedia processing:
    • Images: $0.003 per image
    • Audio: $0.006 per minute
    • Video: $0.05 per minute

Note: A 3MB Word file typically contains ~300KB extractable text, while a 3MB CSV contains full 3MB as text.

2. Agent Hours (beyond included allowance):

  • Quick Research: $6 per agent hour
  • Quick Flows: $3 per agent hour
  • Quick Automate (testing/debugging): $3 per agent hour
  • Quick Automate (deployed production): $3 per agent hour

What are agent hours? Time used by AI features to run research, workflows, and automations.

3. Infrastructure Fee:

  • $250 per account per month - flat fee for underlying AI infrastructure

4. Optional Quick Sight BI Capabilities:

  • SPICE (in-memory storage): $0.38 per GB/month
  • Pixel-perfect Reports: $1 per report unit/month (500 unit minimum)
  • Alerts and anomaly detection: Based on metrics evaluated

Example Monthly Cost (100 users)

Scenario A: Professional Tier (Basic Usage)

User Subscriptions:
  100 users × $20 = $2,000

Infrastructure Fee:
  $250 (per account, not per user)

Quick Index:
  100MB extracted text storage
  (100MB - 50MB free) × $1/MB = $50

Agent Hours (within allowance):
  2 research hours/user = 200 hours (included)
  2 flow hours/user = 200 hours (included)
  No additional charges

Total: $2,300/month ($23 per user)

Scenario B: Enterprise Tier (Heavy Usage)

User Subscriptions:
  100 users × $40 = $4,000

Infrastructure Fee:
  $250

Quick Index:
  500MB extracted text
  (500MB - 50MB free) × $1/MB = $450

Agent Hours:
  Included: 4 research + 4 flows/automate per user
  Additional usage:
    - 100 extra research hours × $6 = $600
    - 200 extra automation hours × $3 = $600

SPICE (optional):
  100GB × $0.38 = $38

Total: $5,938/month ($59.38 per user)

Scenario C: Mixed Team (50 Pro, 50 Enterprise)

User Subscriptions:
  50 Professional × $20 = $1,000
  50 Enterprise × $40 = $2,000

Infrastructure Fee:
  $250

Quick Index:
  200MB extracted text
  (200MB - 50MB free) × $1/MB = $150

Agent Hours (moderate additional usage):
  50 extra research hours × $6 = $300
  100 extra flow hours × $3 = $300

Total: $4,000/month ($40 per user)

Amazon Q Pricing

Amazon Q has separate pricing for Developer and Business editions:

Amazon Q Developer

Tier Price/User/Month What’s Included
Free $0 Basic code suggestions, limited queries
Pro $19 Unlimited code suggestions, security scans, agent mode

What You Get (Pro):

  • ✅ Code generation in IDE
  • ✅ Security vulnerability scanning
  • ✅ Code transformation (Java upgrades)
  • ✅ Agent mode for complex tasks
  • ✅ CLI assistance
  • ❌ No additional consumption charges

Amazon Q Business

Pricing Model: Usage-based tiers

Tier Price/User/Month What’s Included
Lite $3 Up to 10 conversations/user/month
Plus $20 Up to 100 conversations/user/month
Pro $35 Unlimited conversations

Additional Costs:

  • Index storage: $0.0014 per document/month
  • Data connectors: Included (40+ apps)
  • Plugin actions: $0.10 per action execution

Example Monthly Cost (100 users):

Q Developer (100 developers):

100 users × $19 = $1,900/month
No additional charges

Q Business (100 business users, Plus tier):

Base Subscription:
  100 users × $20 = $2,000

Storage:
  10,000 documents × $0.0014 = $14
  
Plugin Actions:
  500 actions × $0.10 = $50

Total: ~$2,064/month ($20.64 per user)

Q Developer + Q Business (mixed team):

50 developers × $19 = $950
50 business users × $20 = $1,000
Storage & actions = ~$64

Total: ~$2,014/month ($20.14 per user average)

Side-by-Side Cost Comparison

Scenario 1: Business Analytics Team (50 users)

Quick Suite (Professional Tier):

50 users × $20 = $1,000
Infrastructure Fee = $250
Quick Index: 100MB - 50MB free = 50MB × $1 = $50
Agent Hours: Within included allowance

Monthly Total: $1,300 ($26/user)
Annual Total: $15,600 ($312/user)

Quick Suite (Enterprise Tier - with dashboard authoring):

50 users × $40 = $2,000
Infrastructure Fee = $250
Quick Index: 100MB - 50MB = 50MB × $1 = $50
SPICE (optional): 50GB × $0.38 = $19
Agent Hours: Mostly within allowance

Monthly Total: $2,319 ($46.38/user)
Annual Total: $27,828 ($556.56/user)

Amazon Q Business (Plus tier):

50 users × $20 = $1,000
Storage: 5,000 docs × $0.0014 = $7

Monthly Total: $1,007 ($20.14/user)
Annual Total: $12,084 ($241.68/user)

Winner:

  • Quick Suite Professional: Similar price to Q Business, but includes BI and automation
  • Quick Suite Enterprise: ~2x more expensive but includes dashboard authoring
  • Choose based on whether you need BI authoring capabilities

Scenario 2: Development Team (100 developers)

Quick Suite Enterprise (if used for BI analytics):

100 users × $40 = $4,000
Infrastructure Fee = $250
Quick Index: Minimal usage = $20

Monthly Total: $4,270 ($42.70/user)
Annual Total: $51,240 ($512.40/user)

Amazon Q Developer:

100 users × $19 = $1,900

Monthly Total: $1,900 ($19/user)
Annual Total: $22,800 ($228/user)

Winner: Amazon Q Developer is ~46% cheaper and better suited for developers.

Scenario 3: Enterprise (500 users - mixed workloads)

Quick Suite (200 Enterprise, 300 Professional):

200 Enterprise users × $40 = $8,000
300 Professional users × $20 = $6,000
Infrastructure Fee = $250
Quick Index: 2GB extracted text = (2,000MB - 50MB) × $1 = $1,950
Additional Agent Hours:
  - 500 research hours × $6 = $3,000
  - 1,000 flow/automate hours × $3 = $3,000
SPICE (optional): 500GB × $0.38 = $190

Monthly Total: $22,390 ($44.78/user)
Annual Total: $268,680 ($537.36/user)

Amazon Q (mixed team):

200 developers × $19 = $3,800
300 business users × $20 = $6,000
Storage: 50,000 docs × $0.0014 = $70
Actions: 2,000 × $0.10 = $200

Monthly Total: $10,070 ($20.14/user)
Annual Total: $120,840 ($241.68/user)

Quick Suite + Amazon Q (best of both):

Quick Suite: $22,390
Amazon Q Developer (200): $3,800
Amazon Q Business (300): $6,000

Monthly Total: $32,190 ($64.38/user)
Annual Total: $386,280 ($772.56/user)

Winner: Depends on needs:

  • Q alone: ~55% cheaper, but no BI or automation
  • Quick Suite alone: Includes BI + automation + research
  • Both: Most comprehensive, but highest cost

Cost Optimization Strategies

For Quick Suite

  1. Right-size SPICE capacity
    • Use direct query for large datasets
    • Reserve SPICE for frequently accessed data
    • Monitor and adjust capacity monthly
  2. Optimize Quick Index
    • Index only necessary documents
    • Use S3 lifecycle policies for old data
    • Deduplicate content before indexing
  3. Batch Quick Research queries
    • Plan research in advance
    • Reuse existing research outputs
    • Limit premium data source access
  4. Workflow efficiency
    • Optimize Quick Flows to reduce steps
    • Use conditional logic to avoid unnecessary runs
    • Schedule workflows during off-peak hours

Potential Savings: 20-30% reduction

For Amazon Q

  1. Choose appropriate tier
    • Start with Lite for light users
    • Upgrade only heavy users to Pro
    • Monitor conversation usage monthly
  2. Optimize document indexing
    • Index only relevant, current documents
    • Remove outdated content
    • Use smart chunking for large files
  3. Limit plugin actions
    • Use actions only when necessary
    • Batch operations where possible
    • Monitor action usage patterns
  4. Free tier for Q Developer
    • Use free tier for junior developers
    • Reserve Pro for senior engineers
    • Evaluate actual usage before upgrading

Potential Savings: 15-25% reduction

Hidden Costs to Consider

Quick Suite

  • Training: Staff training on new automation features
  • Data preparation: Cleaning and structuring data for indexing
  • Integration work: Connecting various data sources
  • Maintenance: Monitoring and optimizing workflows
  • Premium data sources: Third-party data for Quick Research

Estimated: +10-20% of base cost

Amazon Q

  • IDE setup: Initial configuration and rollout
  • Change management: Adopting AI-assisted workflows
  • Security review: Ensuring code suggestions meet standards
  • Content curation: Organizing knowledge base for Q Business
  • Third-party app integrations: Some connectors may have fees

Estimated: +5-15% of base cost

Break-Even Analysis

When Quick Suite Makes Financial Sense

BI Dashboard Creation:

  • Traditional BI tool: 40 hours/dashboard × $100/hour = $4,000
  • Quick Suite: 4 hours/dashboard × $100/hour = $400
  • Savings per dashboard: $3,600
  • Break-even: 1 dashboard/month at $3,500 subscription

Workflow Automation:

  • Manual process: 100 hours/month × $50/hour = $5,000
  • Quick Suite automation: $250/month + 10 hours setup
  • Monthly savings: ~$4,500
  • Break-even: <1 month

Research Tasks:

  • Manual research: 80 hours × $150/hour = $12,000
  • Quick Research: $2,000 + 10 hours review = $3,500
  • Savings per project: ~$8,500
  • Break-even: 1 research project every 2 months

When Amazon Q Makes Financial Sense

Developer Productivity (Q Developer):

  • 20% productivity gain × 100 devs × $120k salary = $2.4M/year
  • Q Developer cost: 100 × $19 × 12 = $22,800/year
  • ROI: 10,400%
  • Break-even: <1 month

Knowledge Retrieval (Q Business):

  • Time saved: 2 hours/week/user × 100 users = 200 hours/week
  • Value: 200 × 4 weeks × $75/hour = $60,000/month
  • Q Business cost: $2,000/month
  • ROI: 3,000%
  • Break-even: <1 week

TCO (Total Cost of Ownership) Comparison - 3 Years

Quick Suite (100 users - Mixed Team: 40 Enterprise, 60 Professional):

Year 1:
  Subscriptions: (40 × $40 + 60 × $20) × 12 = $33,600
  Infrastructure: $250 × 12 = $3,000
  Quick Index: ~$150 × 12 = $1,800
  Agent Hours (additional): ~$400 × 12 = $4,800
  Training: $10,000
  Integration: $15,000
  Total: $68,200

Year 2-3 (each):
  Subscriptions: $33,600
  Infrastructure: $3,000
  Quick Index: $1,800
  Agent Hours: $4,800
  Maintenance: $5,000
  Total: $48,200

3-Year TCO: $164,600 ($1,646/user)

Amazon Q (100 users mixed):

Year 1:
  Q Developer (50): $11,400
  Q Business (50): $12,000
  Storage: $840
  Setup/Training: $5,000
  Total: $29,240

Year 2-3 (each):
  Q Developer: $11,400
  Q Business: $12,000
  Storage: $840
  Total: $24,240

3-Year TCO: $77,720 ($777/user)

Winner: Amazon Q has ~53% lower TCO, but Quick Suite includes BI platform and automation

Free Trial

Quick Suite Free Trial:

  • ✅ 30-day free trial
  • ✅ Up to 25 users per account
  • ✅ Free subscription and infrastructure fee during trial
  • ✅ No credit card required to start

Amazon Q Free Tiers:

  • ✅ Q Developer Free: Basic code suggestions (limited)
  • ✅ Q Business: No free tier, but 30-day trial may be available

Recommendation Matrix by Budget

Budget per User/Month Recommendation
< $20 Amazon Q Business Lite ($3) or Q Developer Free
$20-25 Quick Suite Professional OR Q Developer + Q Business
$25-40 Q Developer Pro + Q Business Plus
$40-50 Quick Suite Professional + Q Developer
> $50 Quick Suite Enterprise + Amazon Q (comprehensive)

Note: Don’t forget the $250/month infrastructure fee for Quick Suite (shared across all users)

Cost Summary

Amazon Quick Suite:

  • 💰 Base cost: $20-40/user/month + $250/account infrastructure fee
  • 📊 Includes BI platform (Quick Sight viewer/author)
  • 🤖 Built-in automation (Quick Flows/Automate)
  • 🔬 AI research agent (Quick Research with included hours)
  • ⚠️ Watch out for: Agent hours overages ($3-6/hour), Quick Index storage ($1/MB)
  • 📈 Best ROI for teams needing BI + automation + research in one platform
  • 🎁 Free trial: 30 days for up to 25 users

Amazon Q:

  • 💰 Lower base cost: $3-35/user/month (no infrastructure fee)
  • 👨‍💻 Q Developer ($19): Excellent for developers (massive productivity gains)
  • 📚 Q Business ($3-35): Knowledge retrieval and document search
  • ⚠️ Watch out for: Plugin actions ($0.10 each), document storage
  • 📈 Best ROI for developer teams and knowledge workers
  • 🎁 Free tier: Q Developer has limited free tier

Both Together:

  • 💰 Total cost: ~$40-65/user/month (includes infrastructure fee)
  • 🎯 Maximum capability (covers all use cases)
  • 📈 Best ROI for large enterprises with diverse needs
  • 🏢 Enterprise-wide transformation potential

Key Pricing Insights:

  1. Small teams (<25 users): Try Quick Suite free trial first
  2. Developer-focused: Amazon Q Developer wins on cost and fit
  3. BI-heavy teams: Quick Suite Professional is competitive at $20/user
  4. Enterprise authoring needs: Quick Suite Enterprise ($40) vs separate BI tool
  5. Infrastructure fee: $250/month for Quick Suite makes it better for larger teams (50+ users)

Pricing based on AWS Quick Suite pricing and Amazon Q pricing as of October 2025.

Migration & Adoption

For Existing QuickSight Customers

✅ Automatic upgrade to Quick Suite
✅ No data migration required
✅ Same security and permissions
✅ All existing content preserved
✅ New capabilities available immediately

For New Users

Quick Suite: Best for teams needing integrated BI + automation
Amazon Q: Best for developers or information retrieval needs
Both: Optimal for comprehensive AI adoption across organization

Infrastructure as Code: CDK & CLI Support

Quick Suite - CDK Support

Quick Suite has AWS CDK (L1) support inherited from QuickSight and expanded for new capabilities:

import * as quicksight from 'aws-cdk-lib/aws-quicksight';
import * as cdk from 'aws-cdk-lib';

export class QuickSuiteStack extends cdk.Stack {
  constructor(scope: cdk.App, id: string) {
    super(scope, id);

    // Create Quick Sight data source
    new quicksight.CfnDataSource(this, 'DataSource', {
      awsAccountId: this.account,
      dataSourceId: 'my-data-source',
      type: 'S3',
      dataSourceParameters: {
        s3Parameters: {
          manifestFileLocation: {
            bucket: 'my-bucket',
            key: 'manifest.json'
          }
        }
      }
    });

    // Create Quick Sight dashboard
    new quicksight.CfnDashboard(this, 'Dashboard', {
      awsAccountId: this.account,
      dashboardId: 'my-dashboard',
      name: 'My Dashboard',
      // Quick Suite-specific actions
      quickSuiteActionsOption: {
        actions: [{
          actionId: 'action-1',
          actionName: 'Create Ticket',
          // ... action configuration
        }]
      }
    });
  }
}

CDK Support Status:

  • ✅ Quick Sight dashboards - Full L1 construct support
  • ✅ Data sources - S3, Athena, Redshift, RDS, etc.
  • ✅ Datasets - Define data schemas and transformations
  • ✅ Dashboard actions - One-click actions from dashboards
  • ⚠️ Quick Research - Limited/emerging CDK support
  • ⚠️ Quick Flows - Limited/emerging CDK support
  • ⚠️ Quick Automate - Limited/emerging CDK support
  • ✅ Quick Index - Can configure via CloudFormation/CDK

Available Constructs:

  • CfnDashboard - Create dashboards with Quick Suite actions
  • CfnDataSource - Configure data connections
  • CfnDataSet - Define datasets
  • CfnTemplate - Create reusable templates
  • CfnAnalysis - Create analyses
  • CfnTheme - Customize themes

Quick Suite - CLI Support

Quick Suite provides AWS CLI commands for managing resources:

Dashboard Operations:

# List dashboards
aws quicksuite list-dashboards --aws-account-id 123456789012

# Describe dashboard
aws quicksuite describe-dashboard \
  --aws-account-id 123456789012 \
  --dashboard-id my-dashboard

# Create dashboard
aws quicksuite create-dashboard \
  --aws-account-id 123456789012 \
  --dashboard-id new-dashboard \
  --name "My Dashboard" \
  --source-entity file://dashboard-config.json

Topic Operations (for Q&A):

# Create topic
aws quicksuite create-topic \
  --aws-account-id 123456789012 \
  --topic-id sales-topic \
  --name "Sales Analysis"

# List topics
aws quicksuite list-topics --aws-account-id 123456789012

# Update topic
aws quicksuite update-topic \
  --aws-account-id 123456789012 \
  --topic-id sales-topic \
  --topic file://topic-config.json

# Delete topic
aws quicksuite delete-topic \
  --aws-account-id 123456789012 \
  --topic-id sales-topic

Data Source Operations:

# Create data source
aws quicksuite create-data-source \
  --aws-account-id 123456789012 \
  --data-source-id my-source \
  --name "S3 Data Source" \
  --type S3 \
  --data-source-parameters file://datasource.json

# Update data source permissions
aws quicksuite update-data-source-permissions \
  --aws-account-id 123456789012 \
  --data-source-id my-source \
  --grant-permissions file://permissions.json

Automation/Workflow Operations (emerging):

# Note: Quick Flows/Automate CLI commands are being rolled out
# Check AWS CLI documentation for latest commands

# List workflows (example)
aws quicksuite list-workflows --aws-account-id 123456789012

# Deploy automation (example)
aws quicksuite deploy-automation \
  --automation-id my-automation \
  --configuration file://automation-config.json

Required IAM Permissions:

{
  "Version": "2012-10-17",
  "Statement": [{
    "Effect": "Allow",
    "Action": [
      "quicksuite:CreateDashboard",
      "quicksuite:DescribeDashboard",
      "quicksuite:UpdateDashboard",
      "quicksuite:DeleteDashboard",
      "quicksuite:CreateTopic",
      "quicksuite:DescribeTopic",
      "quicksuite:UpdateTopic",
      "quicksuite:DeleteTopic",
      "quicksuite:CreateDataSource",
      "quicksuite:DescribeDataSource"
    ],
    "Resource": "*"
  }]
}

Amazon Q - CDK & CLI Support

CDK Support:

import * as qbusiness from 'aws-cdk-lib/aws-qbusiness';

// Create Q Business application
new qbusiness.CfnApplication(this, 'QApp', {
  displayName: 'My Q Business App',
  identityType: 'AWS_IAM_IDP_SAML',
  // ... configuration
});

// Create Q Business index
new qbusiness.CfnIndex(this, 'QIndex', {
  applicationId: 'app-id',
  displayName: 'My Index',
  // ... configuration
});

CLI Support:

# Q Business operations
aws qbusiness create-application --display-name "My App"
aws qbusiness list-applications
aws qbusiness create-index --application-id app-123
aws qbusiness create-data-source --application-id app-123 \
  --index-id idx-456 --configuration file://config.json

# Q Developer (limited CLI - mostly IDE-based)
# No direct CLI for Q Developer - integrated into IDEs

Comparison: IaC & Automation Support

Feature Quick Suite Amazon Q
CDK Support ✅ L1 constructs for dashboards, data sources ✅ L1 constructs for Q Business
CLI Support ✅ Comprehensive dashboard & topic commands ✅ Q Business only (Q Developer via IDE)
CloudFormation ✅ Full support via QuickSight resources ✅ Q Business resources
Terraform ✅ Via AWS provider ✅ Via AWS provider
SDK Support ✅ Python, JavaScript, Java, .NET ✅ Python, JavaScript, Java, .NET
GitOps Ready ✅ Yes - can version control dashboards ✅ Yes - can version control configs
CI/CD Integration ✅ Deploy dashboards via pipeline ✅ Deploy Q apps via pipeline

IaC Best Practices

Quick Suite:

// Recommended: Separate stacks for different concerns
export class QuickSuiteDataStack extends cdk.Stack {
  public readonly dataSources: quicksight.CfnDataSource[];
  
  constructor(scope: cdk.App, id: string) {
    super(scope, id);
    // Define data sources
  }
}

export class QuickSuiteDashboardStack extends cdk.Stack {
  constructor(scope: cdk.App, id: string, 
              dataStack: QuickSuiteDataStack) {
    super(scope, id);
    // Reference data sources from data stack
    // Create dashboards
  }
}

Amazon Q Business:

export class QBusinessStack extends cdk.Stack {
  constructor(scope: cdk.App, id: string) {
    super(scope, id);
    
    // Create application
    const app = new qbusiness.CfnApplication(this, 'App', {
      displayName: 'Corporate Knowledge Base'
    });
    
    // Create index
    const index = new qbusiness.CfnIndex(this, 'Index', {
      applicationId: app.attrApplicationId,
      displayName: 'Main Index'
    });
    
    // Add data sources
    new qbusiness.CfnDataSource(this, 'S3Source', {
      applicationId: app.attrApplicationId,
      indexId: index.attrIndexId,
      displayName: 'S3 Documents',
      configuration: {
        type: 'S3',
        // ... S3 config
      }
    });
  }
}

Deployment:

# Deploy Quick Suite infrastructure
cdk deploy QuickSuiteDataStack
cdk deploy QuickSuiteDashboardStack

# Deploy Q Business infrastructure
cdk deploy QBusinessStack

# Use CLI for runtime operations
aws quicksuite update-dashboard-published-version \
  --aws-account-id 123456789012 \
  --dashboard-id my-dashboard \
  --version-number 2

Real-World Use Case Comparison

Scenario: E-commerce Company

Using Amazon Q:

Developer Team:
  - Use Q Developer to build new checkout service
  - Get help with AWS service configuration
  - Debug production issues

Business Team:
  - Use Q Business to search product documentation
  - Summarize customer feedback
  - Answer policy questions

Using Quick Suite:

Analytics Team:
  - Quick Sight: Create sales dashboard
  - Quick Research: Analyze competitor pricing
  - Quick Flows: Automate weekly sales reports

Operations Team:
  - Quick Automate: Build order fulfillment workflow
  - Quick Sight: Monitor operational metrics
  - Spaces: Organize process documentation

Both Together:

Complete Platform:
  - Q Developer: Build features faster
  - Quick Sight: Measure feature impact
  - Quick Research: Understand market trends
  - Quick Automate: Streamline operations
  - Q Business: Support knowledge sharing

Decision Matrix

Your Need Recommendation
Build and deploy code Amazon Q Developer
Analyze business data Quick Suite (Quick Sight)
Automate workflows Quick Suite (Quick Flows/Automate)
Research competitors Quick Suite (Quick Research)
Search company docs Amazon Q Business
Create dashboards Quick Suite (Quick Sight)
Modernize applications Amazon Q Developer
Unified BI + automation workspace Quick Suite
Developer productivity Amazon Q Developer
Enterprise knowledge management Amazon Q Business or Quick Suite

Future Outlook

Quick Suite Direction

  • More agentic capabilities
  • Deeper system integrations
  • Enhanced automation features
  • Expanded research sources

Amazon Q Direction

  • Improved code generation
  • More programming languages
  • Enhanced AWS service integration
  • Deeper enterprise app connections

Convergence?

While both are AI-powered, they serve different purposes:

  • Quick Suite = Operational workspace (BI + automation + research)
  • Amazon Q = AI assistant (development + knowledge retrieval)

Expect them to remain complementary rather than competing.

Getting Started

Quick Suite

  1. Visit Quick Suite console
  2. Existing QuickSight customers: Already upgraded!
  3. New users: Create Quick Suite workspace
  4. Connect data sources (S3, Snowflake, databases)
  5. Start with Quick Sight for BI, then explore Quick Flows

Amazon Q

  1. Q Developer: Available in IDE (VS Code, JetBrains, Visual Studio)
  2. Q Business: Set up application, connect data sources
  3. Configure permissions and access
  4. Train team on usage

Conclusion

Amazon Quick Suite and Amazon Q are both powerful AI services from AWS, but they serve different needs:

  • Quick Suite is your unified digital workspace for business intelligence, research, and automation—perfect for analysts, operations teams, and business users who need to analyze data, automate workflows, and conduct research.

  • Amazon Q is your AI assistant for development and knowledge retrieval—ideal for developers building applications and business users searching for information.

For maximum value, consider using both:

  • Use Q Developer to build and maintain your applications
  • Use Q Business for knowledge management and document search
  • Use Quick Suite for business operations, analytics, and automation

The future of work at AWS involves agentic AI teammates, and both Quick Suite and Amazon Q are positioned to be core components of that vision.

Further Reading:

Tags: #aws #quick-suite #amazon-q #business-intelligence #ai #automation #comparison