PointFive

What PointFive does

PointFive is an infrastructure efficiency platform that detects deep waste and autonomously remediates it across cloud and AI workloads. It aims to help engineering teams focus on shipping rather than optimizing. The platform provides visibility, control, and governance for AI spend, from cloud services to coding agents, and offers agentic remediation workflows to fix identified inefficiencies. PointFive is building a new category: Cloud & AI Efficiency Management, going beyond traditional tools that only show spending to identify and fix waste.

Products

• PointFive PlatformThe AI Efficiency OS for cloud and AI teams, continuously optimizing every layer of the stack from cloud infrastructure to the coding agent. It provides interfaces like Chat, Agents, and Apps for interacting with its data, detection intelligence, and business context. It focuses on understanding spend, fixing issues, and enabling further development.
• TokenShiftThe AI control plane for organizations, cutting AI token costs at the developer endpoint and providing visibility and governance across coding agents like Claude Code, Cursor, GitHub Copilot, and Devin Desktop. It's a lightweight Go binary that runs locally to optimize token consumption without changing developer workflows.
• DeepWaste™ DetectionA core engine of PointFive that offers 500+ deep detections across cloud, Kubernetes, data, and AI providers. It goes beyond utilization to correlate configuration, telemetry, code, and commitments to surface why a resource is wasteful, not just that it is. It works across AWS, Azure, GCP, Kubernetes, Snowflake, and Databricks.
• InfraFabricA proprietary infrastructure graph that maps dependencies, ownership, and context across the entire cloud estate. It's an agentless, read-only deployment that normalizes cloud, data, and AI into a semantically aware model across 40+ integrations, handling complex billing dimensions.
• PointFive LabsThe team behind DeepWaste™ that continuously develops new detections, validates optimization logic against actual billing data, and tracks changes in cloud and AI pricing. New detections ship weekly, benefiting customers automatically.
• BrainConnects technical and business context across every interaction, powering Chat, Agents, and Apps. It creates a semantic layer from ingested data, making it available across all modules and resolving definitions like 'cost,' 'team,' and 'spend' according to organizational context.

Key capabilities

• DeepWaste™ Detection (500+ detection types across AWS, Azure, GCP, Kubernetes, Snowflake, Databricks)
• InfraFabric (proprietary infrastructure graph for dependency mapping)
• Agentic Remediation (autonomous resolution of waste with validated actions, generating pull requests and tickets)
• AI & ML cost optimization (breaks down AI spend by model and cloud, optimizes usage, right-sizes models)
• TokenShift (extends optimization into the IDE for coding agents, governing and accounting for tokens)
• Multi-cloud and Kubernetes-native context
• Anomaly detection for spend & drift
• Automated owner routing via Slack & Jira
• Realized-savings tracking
• Workspace built around findings (owner, risk, remediation path, savings estimate)
• Chat interface for cloud/AI spend questions
• Agents for driving optimization work from detection to verified savings
• Apps for building custom applications on PointFive's data (Anomaly Detection, Savings Opportunities, Inventory, Commitments, Analytics)
• Prompt-Aware Optimization (identifies waste inside prompts, tool definitions, cache usage, agent workflows)
• Token-Level Visibility (measures and optimizes token consumption across models, applications, prompts, coding agents)
• Cost Attribution Beyond Native Billing (allocates spend at deployment, application, team, prompt, developer level)
• Unified AI Cost Intelligence (normalizes spend across AWS, Azure, GCP, Snowflake, Databricks, OpenAI, Anthropic, endpoint AI tools)
• GPU and accelerator utilization tracking
• Cost spike detection with root-cause analysis
• Approved-model and tool-access policies by team
• PII exposure and personal-use detection
• Agentless deployment (under 15 minutes)
• Read-only access, least-privilege IAM role
• Data encrypted in transit and at rest
• SOC 2 Type II compliant
• Prompt & tool output compression (JSON aliasing, tabular conversion, AST-level code stripping, diff trimming)
• Web fetch noise stripping
• CLI output compression
• Fast CLI substitution
• Context deduplication
• Image right-sizing
• KV-cache optimization

Use cases

• Optimizing AI spend across various models and cloud providers (Bedrock, Vertex, Azure OpenAI)
• Forecasting AI costs across every model and cloud
• Optimizing AI usage with smart caching and routing
• Right-sizing AI models, PTUs, and idle endpoints
• Attributing coding-agent spend to teams and repositories
• Enforcing model and budget compliance per agent
• Detecting and remediating idle resources, over-provisioned infrastructure, orphaned storage, and inefficient AI workloads
• Automating cloud cost optimization workflows from detection to fix to ROI
• Drafting GitHub and Terraform pull requests for remediation
• Opening Jira tickets and paging owners in Slack for remediation
• Tracking realized savings
• Asking questions about cloud or AI spend
• Driving optimization work from detection through to verified savings
• Creating custom applications on PointFive's data for anomaly detection, savings opportunities, inventory, commitments, and analytics
• Identifying waste inside prompts, tool definitions, cache usage, and agent workflows
• Measuring and optimizing token consumption for AI workloads
• Allocating AI spend at granular levels (deployment, application, team, prompt, developer)
• Tracking GPU and accelerator utilization
• Detecting and analyzing cost spikes
• Optimizing underutilized provisioned capacity, idle endpoints, and oversized GPU infrastructure
• Identifying and addressing expensive models on low-complexity tasks, on-demand batch workloads, and cross-region inference
• Optimizing prompt cache misuse, inefficient prefixes, and unnecessary tool definitions
• Addressing structural token waste in coding-agent workflows and prompt compression opportunities
• Enforcing approved-model and tool-access policies
• Detecting PII exposure and personal-use in AI interactions
• Optimizing AWS services (EC2, RDS, S3, EBS, Lambda, EKS, Fargate, NAT Gateway, CloudWatch, MSK, SageMaker, Bedrock)
• Optimizing GCP services (Compute Engine, GKE, Cloud SQL, BigQuery, Cloud Run, Vertex AI, Cloud Logging)
• Autonomous DynamoDB cost optimization
• Achieving cloud efficiency as an engineering priority
• Optimizing S3, EBS, EC2, EKS, and RDS costs
• Optimizing custom LLM training pipelines across multiple AWS regions and NVIDIA GPU architectures

AI approach

PointFive uses proprietary AI (DeepWaste™ detection engine and InfraFabric graph) to identify and remediate cloud and AI waste. It also offers TokenShift for AI token optimization at the developer endpoint. The platform leverages AI for continuous monitoring, anomaly detection, and agentic remediation workflows, and has a dedicated team (PointFive Labs) for continuous development of new detections and optimization logic.

Tech named: DeepWaste™, InfraFabric, TokenShift, Brain

Industries served

• Financial Services
• Healthcare Software
• Audio-Visual Technology
• Professional Services
• Payments
• Sports Retail

What it says sets it apart

• Detects deep waste that other tools miss (400+ detection types)
• Autonomous remediation with safe, validated actions (turns insights into fixes)
• Building a new category: Cloud & AI Efficiency Management
• Only platform that follows AI waste from the cloud to the coding agent
• Breaks open AI bills (Bedrock, Vertex, Azure OpenAI) to show spend by model
• TokenShift extends optimization into the IDE for coding agents
• Goes beyond utilization to correlate configuration, telemetry, code, and commitments
• Architecture-aware findings, not just CSV exports
• Agentic, auditable remediation workflow (drafts PRs, opens Jira tickets, pages owners, verifies fixes)
• Realized-savings tracking, not just projections
• Four-layer intelligence foundation (InfraFabric, DeepWaste™, PointFive Labs, Brain) for deep insights
• Prompt-Aware Optimization (beyond infrastructure costs)
• Token-Level Visibility (unit of AI cost)
• Cost Attribution Beyond Native Billing (granular allocation)
• Unified AI Cost Intelligence (single view across multiple providers and tools)
• Cross-stack advantage (sees environment and layers above, coordinating policies and detecting savings at intersections)
• Fast Time to Value (agentless deployment in under 15 minutes, first savings opportunities in 48 hours)
• AI-Powered, 24/7 continuous monitoring and action
• Zero Savings Gaps (full coverage across cloud, PaaS, data & AI)
• No Context Switching (engineers fix issues in tools they already use)
• Read-only access, no agents to install or maintain
• TokenShift runs locally with no proxy architecture and no access to code or credentials
• Deterministic by design: AI builds once, the app runs as code, no hallucination on execution

Funding rounds we track

From the AI funding tracker — rounds as reported by the linked publications.