Invisible AI Spend
Cloud providers bury AI costs inside aggregated line items. You see a total bill, but not which models, deployments, or teams are driving it.
Your AI Costs Are Growing Faster Than Your Visibility and Efficiency Efforts.
Engineering teams are deploying AI at unprecedented speed, but cost governance hasn't kept up. PointFive gives you full visibility into AI spend, allocates costs to teams, and finds optimizations that traditional tools miss.
Most Teams Can't See Their AI Costs. That's by Design.
Cloud providers optimize for adoption, not transparency. The result: AI spend is the fastest-growing line item on your cloud bill, and the least understood.
Costs Accelerating Unchecked
AI budgets grow 30%+ year over year. Every new feature, every model upgrade, every inference endpoint compounds spend without natural guardrails.
No Traditional Guardrails
Reserved instances and savings plans don't map to token-based billing. The FinOps playbook that works for compute and storage breaks down for AI.
Deploy Now, Govern Later
Engineering teams move fast. Models ship to production before anyone understands the cost implications. By the time the bill arrives, the architecture is locked in.
The AI Spending Reality
Numbers that define the AI cost optimization opportunity.
30%+AI Budget Growth YoY
84%Orgs Struggle With Cloud Costs
99%Savings on Underutilized PTUs
86%Savings via Model Migration
See Every Dollar of AI Spend. Allocate It to Every Team.
PointFive maps your entire AI cost surface, from managed LLM APIs to GPU infrastructure, into a single view with engineering-level granularity.
- Unified AI Spend View: Observe AI services, infrastructure, and supporting resources across AWS Bedrock, Azure OpenAI, and GCP Vertex AI in one place.
- Token-Level Cost Tracking: Go beyond aggregated billing. Track cost per token, per inference, and per deployment to understand exactly what drives your AI spend.
- Team & Service Attribution: Automatically allocate AI costs to engineering teams, services, and environments without manual tagging or spreadsheet gymnastics.
- Cost Driver Analysis: Identify which models, token patterns, inference endpoints, and supporting infrastructure are responsible for cost growth.
AI Cloud Costs Summary
LiveMonthly AI Spend
$4,260.62
Total AI Resources
11,257
Open Opportunities
8
Cost Breakdown by Service
SageMaker 3 resources$2,534.40
Bedrock 8 resources$1,726.22
Top AI Resources by Cost
- $2,534.40
voyage-multilingual-2
SageMaker Endpoint · pointfive-prod
- $651.94
us-west-2-claude-3-opus
Bedrock Inference · pointfive-prod
- $411.49
us-west-2-claude-3-sonnet
Bedrock Inference · pointfive-prod
Beyond Visibility: Continuous AI Cost Optimization
PointFive doesn't just show you the bill. Our DeepWaste detection engine analyzes your AI workloads to surface optimization opportunities that generic cost tools miss entirely.
AI Key Insights
SageMaker (59% of AI spend)
- Voyage multilingual embedding endpoint accounts for most of your AI spend at $2,534/month
- This is a deployed inference endpoint running continuously
Bedrock (41% of AI spend)
- Primarily using Anthropic Claude models (Opus, Sonnet, Haiku)
- Claude Opus models are the highest cost Bedrock resources (~$1,200/month combined)
No idle or underutilized AI resources detected
Endpoint Deep Dive
SageMaker Endpoint Review
voyage-multilingual-2-embedding-model-endpoint
$2,534.40~$30,413/year
Instance Typeml.g5.xlarge
Regionus-east-1
Auto-ScalingNot configured
Optimization Opportunities
1.Enable Auto-ScalingModerate Savings
Business hours only , Scale to 0 off hours Up to 66% (~$1,700/mo)
Variable load , Target tracking scaling 20-50% depending on pattern
2.Consider Serverless InferenceHigh Savings
< 100 requests/day , Pay only for compute time used
Bursty with long idle , No cost during idle time
PTU vs. PAYG Rightsizing
Detect over-provisioned Provisioned Throughput Units running at low utilization. Automatically recommend switching to pay-as-you-go for dev environments and rightsizing reserved capacity for production.
Up to 99% savings on underutilized PTUs
Model Migration Intelligence
Identify deployments running older or inefficient models. Newer models often deliver better performance with dramatically lower token costs through improved caching and compression.
Up to 86% savings through model upgrades
Idle Capacity Detection
Flag reserved AI capacity that sits idle, provisioned endpoints with no traffic, GPU instances waiting for jobs that never come. Reclaim or reallocate before the next billing cycle.
Eliminate spend on unused AI resources
Token Economics Analysis
Break down cost-per-request across input tokens, output tokens, and cached tokens. Identify prompt optimization opportunities and cache efficiency gains.
Reduce cost-per-inference by 40-60%
AI Cost Optimization That Lives in the Engineering Workflow
Cost mandates from finance don't work. PointFive embeds AI cost optimization into the tools and workflows engineers already use, so efficiency becomes a natural part of shipping, not an afterthought.
Create Jira Ticket
CLOUDHighTask
Enable auto-scaling for SageMaker endpoint
Assignee: Sarah Chen · Team: ML Platform
cost-optimizationsagemakerauto-scaling
PointFive Context
Resourcevoyage-multilingual-2-embedding
Current Cost$2,534.40/mo
Projected Savings$1,700/mo
Blast RadiusLow, no downstream dependencies
Ready to create in JiraCreate Ticket →
AI-Powered Remediation
IaC-AlignedEnable auto-scaling on SageMaker endpoint
voyage-multilingual-2-embedding-model-endpoint
Fix in: Cursor GitHub Copilot Windsurf
terraform / sagemaker-autoscaling.tf
# Auto-scaling for SageMaker Endpoint
resource "aws_sagemaker_endpoint_configuration" "voyage" {
# Add auto-scaling policy
scaling_config {
min_capacity = 0
max_capacity = 4
target_value = 70.0
scale_in_cooldown = 300
scale_out_cooldown = 60
}
...Terraform planready
Cost projectionready
Rollback planready
Team-Level Cost Allocation
Automatically attribute AI spend to engineering teams, services, and cost centers. No manual tagging required: PointFive maps cloud resources to ownership using your existing infrastructure topology.
Engineering Dashboards
Give every team lead a real-time view of their AI cost footprint. Track spend trends, set budgets, and compare cost-per-feature across services, all without waiting for a monthly finance report.
IDE-Native Remediation
PointFive MCP brings cost intelligence directly into agentic IDEs like Cursor and Windsurf. Engineers discover savings, validate impact, and generate IaC-aligned fixes without leaving their workflow.
Workflow Integration
Push optimization tasks to Jira, ServiceNow, or Slack. Every recommendation comes with full context, resource dependencies, blast radius, and projected savings, so engineers can act confidently.
One Platform for AI Costs Across Every Cloud
Most organizations use AI services from multiple cloud providers. PointFive provides unified AI cost optimization across all of them, no fragmented views, no manual reconciliation.
Azure OpenAI
GPT-4o, GPT-4, o1, o3
- PTU vs. PAYG optimization
- Deployment-level cost attribution
- Token economics per model version
- Reserved capacity rightsizing
AWS Bedrock
Claude, Titan, Llama, Mistral
- Cross-model cost comparison
- On-demand vs. provisioned analysis
- Inference endpoint optimization
- Multi-region cost mapping
GCP Vertex AI
Gemini, PaLM, custom models
- Prediction endpoint utilization
- Training pipeline cost tracking
- Custom model serving efficiency
- Auto-scaling cost impact analysis
From Zero Visibility to Optimized AI Spend in Days
01
Connect in Minutes
Agentless, read-only integration with your cloud accounts. No agents to install, no write access required. PointFive starts building a complete picture of your AI infrastructure immediately.
02
Map Your AI Cost Surface
PointFive automatically discovers every AI service, model deployment, and supporting resource. Costs are attributed to teams and services using your existing infrastructure topology.
03
Surface Optimization Opportunities
DeepWaste detection analyzes token patterns, utilization metrics, and billing mechanics to identify PTU rightsizing, model migrations, idle capacity, and prompt optimization opportunities.
04
Remediate with Confidence
Every recommendation comes with full engineering context, dependencies, blast radius, and projected savings. Remediate through your IDE, ticketing system, or PointFive's AI Co-Workers.
Deep Dives on AI Cost Optimization
Our engineering and research teams publish continuously on the frontier of AI cost management. Explore our latest thinking.
Feb 11, 2026
Azure OpenAI Cost Saving Optimizations
Four proven strategies for optimizing Azure OpenAI costs: PTU reservations, quota rightsizing, PAYG shifting, and capacity scheduling.
Read moreFeb 2, 2026
FinOps for AI: Master Your GenAI Unit Economics Across Every Cloud
How to achieve unified visibility into AI spend across AWS Bedrock, Azure OpenAI, and GCP Vertex AI in a single platform.
Read moreJan 22, 2026
FinOps for AI: The "Tokenomics" Frontier
Why token-level unit economics, not aggregate spend, are the key to sustainable AI cost optimization. Real case studies with 86-99% savings.
Read moreJan 13, 2026
FinOps for AI: Cloud Is No Longer Only a Math Problem
AI cost optimization isn't just about reducing spend. It's about optimizing cost-per-outcome while maintaining quality.
Read moreJan 7, 2026
The Hidden Economics of Managed LLMs in Azure OpenAI
How managed LLM pricing hides deployment-level costs, and why Cloud & AI Efficiency Management provides the visibility you need.
Read moreDec 11, 2025
The Collapse of Cost Visibility as a Strategy
Why dashboards and alerts alone fail to drive efficiency. The shift from reporting spend to continuous optimization.
Read moreAI Cost Optimization: Frequently Asked Questions
AI cost optimization is the practice of reducing and managing the costs associated with running AI workloads in the cloud, including managed LLM APIs (Azure OpenAI, AWS Bedrock, GCP Vertex AI), GPU infrastructure, inference endpoints, and AI training pipelines. It goes beyond traditional cloud cost management by addressing token-based billing, provisioned throughput economics, model selection efficiency, and prompt optimization.
Traditional cloud cost management focuses on compute, storage, and networking resources with predictable pricing models. AI workloads introduce fundamentally different economics: token-based billing, provisioned throughput vs. pay-as-you-go decisions, model version efficiency differences, and costs that scale with usage in non-linear ways. AI cost optimization requires understanding these mechanics at the deployment level, not just the billing account level.
PointFive provides a unified platform that connects to AWS, Azure, and GCP through agentless, read-only integrations. It automatically discovers all AI services and resources, attributes costs to teams and services, and uses its DeepWaste detection engine to identify optimization opportunities specific to each provider's AI pricing mechanics, from Azure OpenAI PTU rightsizing to AWS Bedrock inference optimization to GCP Vertex AI training pipeline efficiency.
Yes. PointFive automatically maps AI spend to engineering teams, services, and cost centers using your existing infrastructure topology. This means no manual tagging or spreadsheet work, teams get real-time dashboards showing their AI cost footprint, spend trends, and optimization opportunities specific to their services.
Savings vary by environment, but PointFive customers have achieved up to 99% cost reduction on underutilized provisioned throughput deployments and 86% savings through model migration to newer, more cache-efficient versions. Across all cloud resources, PointFive typically identifies savings of 15-30% of total cloud spend.
PointFive uses agentless, read-only integrations that deploy in hours, not weeks. There are no agents to install and no write access to your cloud environment. You can expect to see your first AI cost insights and optimization recommendations within 48 hours of connecting your cloud accounts.
PointFive supports managed LLM services including Azure OpenAI (GPT-4o, GPT-4, o1, o3), AWS Bedrock (Claude, Titan, Llama, Mistral), and GCP Vertex AI (Gemini, PaLM, custom models). It also covers GPU infrastructure (including NVIDIA H100, H200, and B300 instances), AI training pipelines, and inference endpoints across all major cloud providers.
Yes. PointFive integrates with Jira, ServiceNow, and Slack for workflow management. Its MCP (Model Context Protocol) integration brings cost intelligence directly into agentic IDEs like Cursor and Windsurf, allowing engineers to discover savings and generate remediation code without leaving their development environment.
See your AI costs, and your savings, in 48 hours.
Book a demo and we'll map your AI spend across every provider, surface optimization opportunities, and show you exactly where to save.