PointFive gives engineering teams full visibility into AI spend, optimizes workload performance, allocates costs to teams, and finds savings that traditional tools miss, from token-level economics to PTU rightsizing across every cloud provider.
Numbers that define the AI cost optimization opportunity.
PointFive maps your entire AI cost surface — from managed LLM APIs to GPU infrastructure — into a single view with engineering-level granularity.
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
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)
Bedrock (41% of AI spend)
SageMaker Endpoint Review
voyage-multilingual-2-embedding-model-endpoint
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
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
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
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%
Traditional tools only show the bill. PointFive provides the precision needed to scale AI features profitably by breaking down costs into clear, actionable units.
Real-time cost tracking per token, per inference, and per user cost. Unlike cloud bills that summarize your spending, PointFive accounts at the individual token level.
Run "What-If" scenarios for PTU vs. PPM economics and model migrations before you commit.
Automatically map AI spend to specific deployments and engineering owners without manual tagging.
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
Assignee: Sarah Chen · Team: ML Platform
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
}
...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.
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.
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.
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.
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.
GPT-4o, GPT-4, o1, o3
Claude, Titan, Llama, Mistral
Gemini, PaLM, custom models
01
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
PointFive automatically discovers every AI service, model deployment, and supporting resource. Costs are attributed to teams and services using your existing infrastructure topology.
03
DeepWaste detection analyzes token patterns, utilization metrics, and billing mechanics to identify PTU rightsizing, model migrations, idle capacity, and prompt optimization opportunities.
04
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.
Our engineering and research teams publish continuously on the frontier of AI cost management. Explore our latest thinking.
Feb 11, 2026
Four proven strategies for optimizing Azure OpenAI costs: PTU reservations, quota rightsizing, PAYG shifting, and capacity scheduling.
Read more →Feb 2, 2026
How to achieve unified visibility into AI spend across AWS Bedrock, Azure OpenAI, and GCP Vertex AI in a single platform.
Read more →Jan 22, 2026
Why token-level unit economics — not aggregate spend — are the key to sustainable AI cost optimization. Real case studies with 86-99% savings.
Read more →Jan 13, 2026
AI cost optimization isn't just about reducing spend. It's about optimizing cost-per-outcome while maintaining quality.
Read more →Jan 7, 2026
How managed LLM pricing hides deployment-level costs, and why Cloud & AI Efficiency Management provides the visibility you need.
Read more →Dec 11, 2025
Why dashboards and alerts alone fail to drive efficiency. The shift from reporting spend to continuous optimization.
Read more →Book a demo and we'll map your AI spend across every provider, surface optimization opportunities, and show you exactly where to save.