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.
The Challenge
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.
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.
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
Full Visibility
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.
Monthly AI Spend
$4,260.62
Total AI Resources
11,257
Open Opportunities
8
Cost Breakdown by Service
voyage-multilingual-2
SageMaker Endpoint · pointfive-prod
us-west-2-claude-3-opus
Bedrock Inference · pointfive-prod
us-west-2-claude-3-sonnet
Bedrock Inference · pointfive-prod
AI Cost Optimization
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.
● 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
SageMaker Endpoint Review
voyage-multilingual-2-embedding-model-endpoint
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%
Built for Engineering Teams
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.
Enable auto-scaling for SageMaker endpoint
PointFive Context
Enable auto-scaling on SageMaker endpoint
voyage-multilingual-2-embedding-model-endpoint
# 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
}
...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.
Multi-Cloud AI Coverage
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
How It Works
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.
AI Cost Optimization Resources
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 moreFAQ
AI Cost Optimization — Frequently Asked Questions
Start Optimizing
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.