The CloudWise Agent

CloudWise is the agent for your AWS bill. You talk to it in plain English in the Workspace; it answers, renders the right view inline, and hands you the fix. This guide covers how the agent works and the kinds of questions it answers.

"AI Copilot" is now "the agent"

Earlier versions of CloudWise called this an "AI Copilot" that lived in a panel in the corner of the screen. It's now the agent at the center of the Workspace—the conversation is the spine of the product, not a bolt-on. The capabilities below carry over; the surface is new.

🤖 What does the agent do?

The agent understands your AWS cost and waste data and answers naturally—no query language, no hunting across dashboards. Ask a question and it replies in plain English and renders the matching structured view (a table, a chart, a finding card with its dollar impact). For waste, it proposes the fix; on the Agentic plan it can execute the fix after you approve it.

Key Capabilities

• 📊 Natural Language Queries - Ask questions like "Why did my costs spike last week?" instead of writing complex queries
• 🧩 Generative, structured views - The agent renders tables, charts, and finding cards inline—not chat-only
• 🔍 Smart Pattern Recognition - Identifies trends, anomalies, and cost optimization opportunities automatically
• 🗑️ Waste Detection Integration - Automatically surfaces idle resources, unused EBS volumes, and optimization opportunities
• 🔧 Propose-and-approve remediation - Generates the fix (CLI / IaC); on Agentic, executes it with your approval and rollback
• 💡 Proactive Insights - Suggests follow-up questions and highlights important patterns you might miss
• 📈 Forecasting & Predictions - Projects future costs based on historical patterns and usage trends

✨ The verb ladder—what each plan unlocks

The agent is on every plan. What changes as you climb is how much it can do for you—from Ask to Fix.

Ask — Free ($0 forever)

5 questions per day

• ✅ Talk to the agent about your cost and waste data
• ✅ Cost breakdowns, top services, trends
• ✅ Waste findings with propose-only fixes (ready-to-run commands)
• ✅ 1 AWS account

Perfect for: Individual developers, personal projects, getting started with the agent

Watch — Shield ($19/mo or $149/year)

50 questions per day

• ✅ Everything in Ask
• ✅ Day-by-day cost analysis and spike root-cause
• ✅ Service-specific deep dives
• ✅ Cost anomaly detection + Slack/email alerts
• ✅ Up to 5 AWS accounts
• ✅ Conversation history

Perfect for: CTOs, Engineering Managers, and Lead Developers who own the AWS bill but don't have time to become FinOps experts. Ask "Why did my bill spike?" and get a plain English answer.

Fix — Agentic AI ($49/mo or $399/year)

100 questions per day

• ✅ Everything in Watch
• ✅ The agent executes approved remediations in your account (with automatic rollback)
• ✅ Remediation queue with propose-and-approve controls
• ✅ Monthly remediation credits

Perfect for: Teams that want the agent to close the loop—not just find the waste, but clear it.

Govern — Compliance ($99/mo or $799/year)

500 questions per day

• ✅ Everything in Watch
• ✅ Air-Gapped Mode with 365-day retention
• ✅ Upload history (12 uploads), QoQ variance, PDF audit reports
• ✅ Persistent anonymization salt, Cost By Tag analysis

Propose-only by design: Compliance is for regulated teams that cannot connect AWS accounts to third-party tools. Data arrives as anonymized offline uploads, so the agent proposes fixes with a full audit trail but never executes from outside your perimeter.

Perfect for: Regulated enterprises, federal agencies, and security-focused teams who need air-gapped AWS cost analysis with audit-ready reporting.

🚀 How to Use the Agent

Getting Started

1. Open the Workspace - The agent is the center of the Workspace; just start typing
2. Ask Your Question - Type naturally, as if talking to a colleague
3. Review the Response - The agent answers in plain English and renders the matching view (table, chart, or finding card)
4. Act on a finding - Use the proposed command, or (on Agentic) approve the agent to execute the fix
5. Follow Up - Ask clarifying questions or dive deeper; the agent keeps context across the conversation

What to Ask

High-level questions (every plan)

• "What are my top 5 services?"
• "How much am I spending this month?"
• "Which region costs the most?"
• "Show me my cost breakdown"

Deeper analysis (Shield and above)

• "Why did spend spike on the 10th?"
• "Show me daily RDS costs for the last 30 days"
• "What's causing my Lambda costs to increase?"
• "Compare last month vs this month"

💬 Example Queries

🗑️ Waste Detection & Optimization

"What waste do I have in my accounts?"

• Surfaces idle EC2 instances, unattached EBS volumes, and unused resources
• Shows potential monthly savings with confidence levels
• Provides actionable CLI commands to clean up

"Show me idle resources I can clean up"

• Lists resources by waste category (compute, storage, networking)
• Prioritizes by savings potential (high confidence items first)
• Includes step-by-step remediation actions

"How can I reduce my AWS spending?"

• Combines cost analysis with waste detection findings
• Recommends specific resources to terminate or rightsize
• Shows total monthly savings opportunity with breakdown

"What are my top optimization opportunities?"

• Ranks opportunities by potential savings
• Explains each finding with context
• Provides both quick wins and longer-term recommendations

"Are my Glue jobs optimized?"

• Detects oversized jobs wasting DPU-hours
• Flags missing timeouts that risk runaway billing
• Identifies failing jobs burning money on retries
• Recommends migrating dev endpoints to Interactive Sessions
• Warns about Data Catalog growth exceeding the free tier

"Are any Transfer Family servers idle?"

• Identifies servers with no file transfer activity in 30+ days
• Flags unused protocols costing $216/month each
• Detects idle web apps costing $360/month per unit
• Provides stop/delete CLI commands with savings estimates

"Are any Global Accelerators idle or disabled?"

• Detects accelerators with zero traffic for 30+ days ($25.55/month each)
• Flags disabled accelerators still incurring fixed hourly charges
• Identifies accelerators with no endpoints configured
• Provides delete CLI commands with static IP release warnings

"Are my DocumentDB clusters right-sized?"

• Detects clusters with CPU < 20% and low connection counts
• Identifies old manual snapshots accumulating storage costs
• Recommends instance downsizing with savings estimates
• Advisory-only guidance for overprovisioned clusters

"Are any of my FSx filesystems oversized or overprovisioned?"

• Detects filesystems with storage utilization below 40% of provisioned capacity
• Identifies throughput overprovisioning for Windows/ONTAP/OpenZFS filesystems
• Flags old manual FSx backups past retention threshold
• Advisory-only guidance for storage/throughput rightsizing; executor support for old backup cleanup

"Are my Step Functions workflows efficient?"

• Detects retry storm patterns where failure-driven retries exceed 25% of executions
• Identifies high transition density (>50 transitions per success) wasting state transition costs
• Flags Express workflows with P95 duration above 30 seconds (better suited for Standard)
• All findings are advisory-only with actionable optimization guidance

"Are my OpenSearch domains right-sized?"

• Detects oversized domains with avg CPU < 20% and max CPU < 40% — recommends stepping down instance type
• Flags EBS volume overprovisioning when free storage exceeds 60% with low growth rate
• Identifies Reserved Instance purchase opportunities from AWS Cost Explorer RI recommendations
• Advisory-only guidance for domain rightsizing; RI purchases require manual action

"Are my ElastiCache clusters optimized?"

• Detects idle clusters with zero connections for 7+ days
• Flags oversized nodes with CPU below 10%
• Identifies non-production clusters running unnecessary replicas (replication waste)
• Recommends Valkey migration for Redis OSS clusters (20% instant savings)
• Flags spiky workloads better suited for ElastiCache Serverless
• Identifies large R6g clusters eligible for R6gd data tiering (up to 52% savings)
• Checks engine versions for Extended Support surcharges

"Can I save by migrating from Redis to Valkey?"

• Checks which clusters run Redis OSS or Memcached
• Calculates 20% savings from Valkey's permanent pricing discount
• Notes Redis 7.x API compatibility and existing RI applicability

"Do any dev/staging caches have replicas?"

• Uses environment tags to identify non-production clusters
• Flags replicas that cost the same as the primary node
• Calculates per-replica savings across all shards

"Is my Neptune cluster over-provisioned?"

• Analyzes CloudWatch CPUUtilization over 14 days
• Flags instances with <20% average CPU for downsizing
• Calculates savings from moving to the next smaller instance class

"Should this Neptune cluster be Serverless?"

• Compares provisioned instance cost against Neptune Serverless ($0.1098/NCU-hr)
• Only recommends Serverless when provisioned cost exceeds $80.15/month floor
• Warns if SPARQL is used (not supported on Serverless)

"Are there old Neptune snapshots I should clean up?"

• Finds manual cluster snapshots older than 90 days
• Calculates storage cost at $0.021/GB-month
• Flags snapshots from migrations, upgrades, and pre-delete backups

�️ WorkSpaces Optimization

"Which WorkSpaces should switch from AlwaysOn to AutoStop?"

• Identifies AlwaysOn WorkSpaces disconnected for 7+ days
• Calculates break-even hours and monthly savings from switching to AutoStop
• Only flags when savings exceed $10/month to avoid false positives

"Do I have oversized WorkSpaces?"

• Analyzes CloudWatch UserSessionsCount over 14 days
• Recommends conservative single-step bundle downgrades (e.g., PowerPro → Power)
• Shows estimated monthly savings per WorkSpace

"Are my WorkSpaces Pools overprovisioned?"

• Detects pools with utilization below 75%
• Calculates excess capacity cost at $35/slot/month
• Only flags pools with 2+ excess slots and savings above $25/month

"Can I reduce WorkSpaces licensing costs with BYOL?"

• Groups Windows license-included desktops across bundles
• Estimates $4/desktop/month savings from Bring Your Own License
• Only flags when 5+ eligible desktops detected
• Includes licensing compliance disclaimer

�🔍 Discovery & Exploration

"What are my biggest cost drivers?"

• Returns top services/regions with cost breakdown
• Identifies percentage of total spend
• Suggests areas for optimization

"Show me my cost trends over time"

• Displays spending patterns
• Highlights increases or decreases
• Identifies seasonal patterns

🎯 Targeted Analysis

"Why did my costs spike last week?"

• Analyzes day-by-day changes
• Identifies specific services causing the increase
• Provides context and potential causes

"How much am I spending on RDS?"

• Service-specific breakdown
• Regional distribution
• Usage patterns and recommendations

🤖 SageMaker Optimization

"Are any of my SageMaker notebooks idle?"

• Identifies InService notebooks with no recent activity
• Shows instance-type-based pricing, reconciled against your actual bill where your CUR allows it (never a generic estimate)
• Provides stop/delete CLI commands

"Which SageMaker endpoints can I right-size?"

• Analyzes CPU and memory utilization via CloudWatch
• Flags oversized endpoints (low CPU + low memory)
• Detects previous-generation instance types (ml.m4, ml.c4, ml.t2)
• Recommends current-gen replacements with savings estimates

� CloudWatch Logs Optimization

"Do any of my log groups have excessive retention?"

• Identifies log groups with 365+ day retention and significant stored data
• Calculates savings from reducing to 30-day retention ($0.03/GB/month)
• Shows reduction factor (e.g., 365→30 days saves 92% of storage cost)

"Show me empty or orphaned log groups"

• Finds log groups with 0 bytes stored that are 30+ days old
• Typically left behind by deleted Lambda functions or ECS services
• Helps reclaim quota (10,000 log groups per region limit)

�📈 Forecasting & Planning

"What will my costs be next month?" (Shield)

• Intelligent forecasting based on patterns
• Identifies recurring vs variable costs
• Excludes one-time charges
• Provides confidence ranges

"Am I on track with my budget?"

• Compares current spending to projections
• Alerts to potential overages
• Suggests cost-saving measures

🔬 Root Cause Analysis

"What caused the Cost Explorer increase on July 17?" (Shield)

• Granular day-by-day analysis
• Identifies specific API calls or usage changes
• Correlates with service changes

"Why are my Lambda costs higher than usual?"

• Invocation count analysis
• Duration and memory usage patterns
• Identifies inefficient functions

🗑️ Waste Detection + AI Copilot

How It Works

CloudWise combines automated waste detection with AI-powered analysis to deliver actionable optimization recommendations:

┌─────────────────────────────────────────────────────────────────┐
│  User Query: "What waste can I clean up?"                       │
└─────────────────────────────────────────────────────────────────┘
                              │
                              ▼
┌─────────────────────────────────────────────────────────────────┐
│  1. AI classifies query as WASTE_ANALYSIS                       │
│  2. Fetches pre-computed waste findings from DynamoDB           │
│  3. Aggregates by category, account, and confidence             │
│  4. Injects waste context into AI prompt                        │
│  5. AI generates human-readable response with recommendations   │
└─────────────────────────────────────────────────────────────────┘
                              │
                              ▼
┌─────────────────────────────────────────────────────────────────┐
│  Response includes:                                              │
│  • Total monthly savings potential                               │
│  • Top waste items by priority                                   │
│  • AWS CLI commands for cleanup                                  │
│  • Step-by-step remediation guidance                             │
└─────────────────────────────────────────────────────────────────┘

What Gets Detected

Category	Examples	Detection Method
Compute	Idle EC2 instances, unused Elastic IPs, idle Lambda Provisioned Concurrency, excessive Lambda timeouts, ARM64 migration opportunities, idle Lightsail instances, unattached Lightsail static IPs/disks, old Lightsail snapshots, idle Lightsail load balancers/databases, idle/oversized WorkSpaces, WorkSpaces AutoStop opportunities, overprovisioned WorkSpaces Pools, WorkSpaces BYOL licensing, idle Elastic Beanstalk environments, Beanstalk idle traffic, unnecessary ALB on single-instance Beanstalk, Beanstalk previous-gen instances, over-provisioned Beanstalk, orphaned Beanstalk RDS	CPU utilization, network activity, PC utilization, invocation metrics, attachment status, snapshot age, connection count, WorkSpaces connection/session metrics, pool utilization, environment health, RequestCount metrics, ALB config, instance type generation
Storage	Unattached EBS volumes, old snapshots, orphaned snapshots (source volume deleted or AMI deregistered), S3 rapid growth, S3 wrong storage class, S3 empty buckets, old backups, redundant backups, backup lifecycle tiering, stale backup plan assignments, oversized FSx filesystems, FSx throughput overprovisioned, old FSx backups	Attachment status, age analysis, volume/AMI cross-reference, CloudWatch BucketSizeBytes metrics, Backup vault enumeration, FSx FreeStorageCapacity/throughput metrics
Database	Idle RDS instances, over-provisioned Aurora, Aurora I/O optimization, Aurora Extended Support surcharges, Aurora Serverless v2 opportunities, ElastiCache (7 detectors: idle, oversized, extended support, replication waste, Valkey migration, Serverless optimization, data tiering), Neptune Serverless opportunities, over-provisioned Neptune, old Neptune/DocumentDB snapshots, overprovisioned DocumentDB clusters	Connection count, CPU/request metrics, I/O metrics, engine version, instance class, snapshot age, replication topology, environment tags, connection variance
Networking	Unused NAT gateways, orphaned load balancers, low-traffic ALBs, high LCU cost ALBs, Classic LB migration	Traffic analysis, target health, LCU cost analysis
Containers	Idle ECS services, oversized ECS tasks, missing auto-scaling, unnecessary Container Insights, over-provisioned memory, unused ECR images	Task count, CPU/memory utilization, auto-scaling config, image pull dates
Analytics	Idle EMR clusters, long-running EMR clusters, over-provisioned EMR, EMR auto-termination gaps, previous-gen EMR instances, EMR Spot opportunities, idle/over-provisioned Kinesis streams, On-Demand downgrade, extended retention waste, idle fan-out consumers, idle Firehose, oversized MSK clusters, idle/oversized MQ brokers, idle/oversized Glue jobs, failing Glue jobs, Glue dev endpoint migration, unused crawlers, Data Catalog bloat	API status, run history, CloudWatch metrics, YARN utilisation, instance groups, catalog object counts
ML/AI	Idle SageMaker notebooks, idle endpoints, oversized endpoints, stopped notebook storage, previous-gen instances	Activity, invocations, CPU/memory utilization, instance type
Serverless	Unused Lambda functions, deprecated Lambda runtimes, unused API Gateways, unused AppSync APIs, idle AppSync caches, idle AppSync subscriptions, Step Functions retry storms, high transition density, Express duration waste	Invocation history, runtime version, CloudWatch execution/transition metrics, cache hit/miss ratios, WebSocket connection metrics
Management	No-retention log groups, stale log groups, excessive retention log groups, empty orphaned log groups	Retention policy, stored bytes, age analysis
Commitments	Unused Reserved Instances, unused Savings Plans, expiring RIs/SPs, convertible RI exchanges, SP coverage gaps	RI/SP utilization, expiry dates, pricing analysis

EC2 Reserved Instance & Savings Plan Intelligence

CloudWise's AI Copilot understands the full commitment lifecycle and can help with:

• Buy analysis: "Which instance types should I commit to?" → Powered by ri_opportunity_* and sp_opportunity_* detectors

• Utilization review: "Show me any Reserved Instances or Savings Plans I'm not fully using." → Powered by unused_reserved_instance and unused_savings_plan detectors

• Expiry alerts: "What commitments are expiring in the next 60 days?" → Powered by expiring_reserved_instance and expiring_savings_plan detectors

• Exchange opportunities: "Can I upgrade any of my Convertible RIs for free?" → Powered by convertible_ri_exchange_opportunity detector

• Risk assessment: "Should I commit to a 1-year RI given my architecture changes?" → Powered by Commitment Risk Score (Phase 4-5)

Sample AI Response

When you ask "What idle resources can I clean up?", the agent responds with a finding card like this (amounts shown as placeholders—your real numbers come from your scan):

💰 Total Monthly Savings Potential: $X,XXX

I found N waste items across your accounts. Here are the top opportunities:

🔴 High Priority (High Confidence)

1. Unattached EBS Volume - vol-0abc123def456
   • Account: Production (123456789012) | Region: us-east-1
   • Monthly Waste: $XX.XX
   • Action: aws ec2 delete-volume --volume-id vol-0abc123def456
2. Idle EC2 Instance - i-0def789abc012
   • Account: Development (987654321098) | Region: eu-west-1
   • Monthly Waste: $XX.XX
   • CPU utilization below 2% for 14 days
   • Action: aws ec2 stop-instances --instance-ids i-0def789abc012

💡 Recommended Next Steps:

• Review each finding in the Workspace
• Start with high-confidence items first
• Consider stopping instances before terminating (recoverable)

Waste questions to try

• "What waste do I have across all accounts?"
• "Show me my biggest savings opportunities"
• "What idle resources can I clean up?"
• "Which unused resources are costing me the most?"
• "Which Lambda functions have idle Provisioned Concurrency?"
• "Do I have any Lambda functions on deprecated runtimes?"
• "Which log groups have excessive retention I can reduce?"
• "Show me empty or orphaned CloudWatch log groups"
• "Are any of my MQ brokers over-provisioned?"
• "Show me waste findings with the CLI commands to fix them"

🧠 How the Agent Works

Smart Query Classification

The agent automatically categorizes your questions to provide the best response:

• WASTE_ANALYSIS - Optimization, waste detection, and cleanup recommendations
• SIMPLE - Basic questions answered with dashboard data
• TEMPORAL_ANALYSIS - Time-based questions requiring granular data
• ROOT_CAUSE - Spike investigation requiring detailed cost reports
• CORRELATION - Multi-service pattern analysis
• COST_FORECAST - Future cost predictions
• SERVICE_SPECIFIC - Focused analysis of specific AWS services

Waste Detection Integration

When you ask optimization-related questions, AI Copilot automatically:

1. Fetches waste findings from CloudWise's waste detection scanner
2. Aggregates by category - Compute, Storage, Networking, Database, Commitments, etc.
3. Prioritizes by confidence - High/Medium/Low confidence ratings
4. Includes actionable commands - AWS CLI commands for cleanup
5. Shows savings potential - Monthly cost savings for each finding
6. Surfaces commitment risk - For RI/SP recommendations, includes risk score and max safe term

Example waste-related keywords that trigger this:

• "waste", "idle", "unused", "optimize", "reduce costs"
• "savings", "cleanup", "terminate", "delete"
• "recommendations", "opportunities", "rightsizing"

Intelligent Data Filtering

AI Copilot uses advanced client-side filtering to:

• ✅ Reduce token usage by 75-95% - Only sends relevant data to AI
• ✅ Speed up responses by 40% - Less data to process
• ✅ Lower costs - More efficient AI queries
• ✅ Maintain accuracy - Smart filtering preserves context

Example: When you ask "Show me RDS costs," AI automatically:

1. Extracts "RDS" from your question
2. Filters cost data to only RDS entries (1,613 → 83 data points)
3. Sends filtered data to AI model
4. Returns faster, more focused response

The model behind the agent

The agent runs on Amazon Bedrock (Anthropic Claude), server-side. Your AWS key and the model context stay in CloudWise's infrastructure—the agent builds its context from your real scan data via typed tools, never by sending your raw bill to a third party. The model selection is managed for you; you don't configure it.

Analytics Transparency

Every response includes analytics showing:

• Query Classification - How AI interpreted your question
• Data Sources - Which data was used (dashboard/granular)
• Model Used - Which AI model answered
• Tokens Used - Resource consumption
• Filtering Stats - Data reduction percentage

This transparency helps you understand AI's reasoning and optimize future queries.

🎓 Tips for Best Results

Ask Clear, Specific Questions

✅ Good: "Why did RDS costs increase 50% in October?"
❌ Too Vague: "Why are costs high?"

Provide Context When Needed

✅ Good: "Compare my Lambda costs in us-east-1 vs eu-west-1"
❌ Missing Context: "Compare Lambda"

Use Follow-Up Questions

AI remembers conversation context:

1. "Show me Cost Explorer costs"
2. "Why did it spike on August 15?" (AI knows you mean Cost Explorer)
3. "How can I reduce this?" (AI provides Cost Explorer optimization tips)

Leverage the views the agent renders

Ask the agent to scope a view, then drill in:

1. "Show me the last 90 days grouped by service"
2. "What are the main trends here?"
3. The agent narrows its analysis to that view

Use Conversation History (Shield)

• Search past conversations - Find previous analysis instantly
• Resume conversations - Continue where you left off
• Track analysis over time - Compare insights from different periods

🔒 Privacy & Security

Your Data Protection

• ✅ Encrypted in transit - All AI queries use HTTPS/TLS
• ✅ No training on your data - Your cost data is never used to train AI models
• ✅ Secure storage - Conversations stored in encrypted DynamoDB
• ✅ User isolation - Your data is never shared with other users

What the agent does NOT do

• ❌ Train on your data - Your cost data is never used to train models
• ❌ Share data externally - Data stays within CloudWise infrastructure
• ❌ Execute changes without approval - Remediation is propose-and-approve. Only the Agentic plan can execute fixes, and only after you explicitly approve each one (with rollback). Free, Shield, and Compliance are propose-only.
• ❌ Touch IAM, VPCs, security groups, or data - Even on Agentic, the remediation role is scoped to cost-saving actions only (see the AI Remediation Guide)

📊 Performance & Efficiency

Speed Optimizations

• Hybrid data approach: Client-side filtering before AI processing
• Deduplication: Prevents redundant API calls
• Caching: Recent data cached for instant reuse

Cost Efficiency

Token Reduction Examples:

• Generic question: ~12,000 tokens → ~2,500 tokens (80% reduction)
• Service-specific: ~12,000 tokens → ~800 tokens (93% reduction)
• Pattern analysis: ~15,000 tokens → ~3,200 tokens (79% reduction)

Your Benefits:

• More queries within daily limits
• Faster responses
• Lower operational costs
• Sustainable AI usage

🆘 Troubleshooting

Agent Not Responding

1. Check your daily limit - View remaining questions in the Workspace
2. Check your data - Ensure you have cost data for the period you're asking about
3. Try a simpler question - Start with basics like "What are my costs?"

Unexpected Responses

1. Review the disclosure line - The agent shows what it checked ("checked your remediation activity")
2. Rephrase your question - Be more specific or provide more context
3. Start a new thread - Begin fresh if the context seems wrong

Daily Question Limit Reached

Free (5/day): Upgrade to Shield for 50 questions/day
Shield (50/day) · Agentic (100/day) · Compliance (500/day): Limit resets daily at midnight UTC

🎯 Best Practices

For Developers

• Use the agent to understand cost patterns during development
• Ask "What changed since my last deployment?" after releases
• Monitor specific services you're actively working on
• Ask the agent to scope a view, then dig into the trends

For FinOps Teams

• Start your day with "Show me yesterday's cost changes"
• Use forecasting to plan budgets: "What will next quarter cost?"
• Investigate anomalies: "Why did costs spike on [date]?"
• Track optimization impact: "Did costs decrease after rightsizing?"

For Business Leaders

• Monthly reviews: "Compare this month to last month"
• Budget planning: "What are our cost trends?"
• Executive summaries: "What are my top 3 cost concerns?"
• ROI analysis: "How much are we saving from optimizations?"

🚀 What's Next?

On the roadmap

• 🤝 Team Collaboration - Share conversations and insights with your team
• 🧠 Cross-session memory - The agent remembers your stated preferences across threads
• 🔗 Integration APIs - Use the agent's capabilities in your own tools

💡 Need Help?

Resources

• 📖 Quick Start Guide - Get started with CloudWise
• 💰 Optimization Guide - Cost-saving strategies
• ❓ FAQ - Common questions answered
• 🔧 Troubleshooting - Fix issues quickly

Contact Support

• ✉️ Email: support@cloudcostwise.io
• 💬 In-app chat: Click the help icon
• 📱 Response time: Within 24 hours

Upgrade Options

Ready to unlock more AI power?

• 🚀 View Pricing
• 📊 Compare Plans
• 🎁 Get Started for Free

---

🤖 Pro Tip: The more specific your questions, the better the agent can help. Instead of "Why are costs high?", try "Why did Lambda costs in us-east-1 jump between September and October?"

⚡ Quick Start: Open the Workspace now and ask: "What are my biggest cost opportunities?"