No granularity
Cloud billing shows what you spent on Bedrock or Azure OpenAI. It does not show which team, which application, or which developer drove it.
Full observability and optimization of AI spend
AI spend is driven by tokens, GPUs, inference profiles, and coding agents. PointFive is the AI Efficiency OS that helps engineering teams understand what's driving AI spend, identify and quantify waste across every layer of the stack, and enforce the controls needed to scale AI adoption responsibly.
AI spend is the fastest-growing line on the infrastructure bill. And the least understood.
Waste is structural
Every tool call a coding agent makes returns more output than it needs. Every token in that output gets charged. The waste happens automatically, regardless of how good the engineer or the model is.
No controls
Most teams have no way to set model policies, cap spend by team, or enforce which tools coding agents can access. Governance is manual at best.
Token management is probably one of the most critical pieces of the overall AI landscape because that is where you tend to blow your budget the fastest.
We collect data wherever your AI runs.
One platform across cloud AI services, gateways and observability, developer endpoints, and the data platform. Every layer feeds the same cost model.
Cloud AI Services
Managed LLMs, model serving platforms, GPU infrastructure, and data platform AI services.
- AWS Bedrock
- Azure OpenAI
- GCP Vertex AI
- Anthropic
- OpenAI
- SageMaker
- EC2 / Azure / GCP GPU
LLM Gateways and AI Observability
LiteLLM, LangFuse, and compatible gateway, tracing, and observability platforms.
- LangFuse
- LiteLLM
Developer Endpoints via TokenShift
Coding agents, AI-powered development tools, and endpoint AI usage.
- Claude Code
- Cursor
- GitHub Copilot
- Devin Desktop
- Codex
Data Platform AI
Model serving inside the data warehouse, where AI workloads live next to the data.
- Databricks Model Serving
- Snowflake Cortex AI
Why PointFive
Four specific differentiators built for AI workload economics, not generic cloud cost reporting.
Prompt-Aware Optimization
Go beyond infrastructure costs to identify waste inside prompts, tool definitions, cache usage, and agent workflows.
Token-Level Visibility
Tokenomics is the unit of AI cost. Measure and optimize token consumption across models, applications, prompts, and coding agents.
Cost Attribution Beyond Native Billing
Allocate spend at the deployment, application, team, prompt, and developer level with a granularity native cloud billing cannot provide.
Unified AI Cost Intelligence
Normalize spend across AWS, Azure, GCP, Snowflake, Databricks, OpenAI, Anthropic, and endpoint AI tools into a single view.
Results you can expect
What PointFive customers see in their first weeks.
48hTo first savings opportunities
10-20%Reduction in coding-agent token consumption
Up to 80%Savings through model migration and right-sizing
Every dollar of AI spend, visible and optimized.
From cloud AI services and GPU infrastructure to coding agents and prompts.
Understand
- Cost attribution by model, team, application, developer, and token type (input, output, cached, reasoning)
- GPU and accelerator utilization tracking
- Cost spike detection with root-cause analysis down to the model, token pattern, or endpoint
Optimize
- Underutilized provisioned capacity, idle endpoints, and oversized GPU infrastructure
- Expensive models on low-complexity tasks, on-demand batch workloads, and cross-region inference
- Prompt cache misuse, inefficient prefixes, and unnecessary tool definitions
- Structural token waste in coding-agent workflows and prompt compression opportunities
Govern
- Approved-model and tool-access policies by team
- Warn or enforce modes for live guardrails
- PII exposure and personal-use detection
TokenShift is the first product that delivers the visibility, control, and governance your engineering teams do not have.
- Visibility: spend by developer, team, model, session, and technique.
- Control: model policies, spend caps by team, block specific tools or agent frameworks.
- Governance: PII detection, non-work usage flagging, compliance rules, full audit trail.
- Optimization: 10-20% token reduction, no change to output quality.
- Security: single Go binary, fully on-device, pull-only telemetry.
AI cost optimization: frequently asked questions
AI cost optimization is the practice of measuring, attributing, and reducing the cost of running AI workloads across the full stack: managed LLM APIs (Azure OpenAI, AWS Bedrock, GCP Vertex AI), GPU infrastructure, inference endpoints, data-platform model serving, and coding agents on developer machines. It goes beyond traditional cloud cost management by addressing token-based billing, prompt and tool-call waste, and endpoint AI usage that cloud billing never sees.
Traditional cloud cost management focuses on compute, storage, and networking 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, prompt-cache mechanics, and costs that scale non-linearly with usage. AI cost optimization requires understanding these mechanics at the deployment, prompt, and developer level, not just the billing account level.
PointFive connects to AWS, Azure, and GCP through agentless, read-only integrations and normalizes spend across providers into a single view. It also covers data-platform AI (Databricks Model Serving, Snowflake Cortex AI), LLM gateways and observability (LiteLLM, LangFuse), and developer-endpoint AI usage via TokenShift, so engineering leaders can see one number for AI spend regardless of where the workload runs.
Yes. PointFive automatically maps AI spend to teams, applications, prompts, and developers using your existing infrastructure topology and identity signals, so teams get real-time visibility into their AI footprint without manual tagging or spreadsheet reconciliation.
Savings vary by environment, but PointFive typically delivers a first set of savings opportunities within 48 hours, 10-20% reduction in coding-agent token consumption via TokenShift, and up to 80% savings through model migration and right-sizing of provisioned capacity.
PointFive uses agentless, read-only integrations that deploy in hours, not weeks. Most customers see their first AI cost insights and optimization recommendations within 48 hours of connecting their environment.
PointFive supports Azure OpenAI (GPT-5.5, o4-mini), AWS Bedrock (Claude, Amazon Nova, Titan (legacy), Llama, Mistral), and GCP Vertex AI (Gemini, custom models). It also covers GPU infrastructure across AWS, Azure, and GCP, data-platform AI (Databricks Model Serving, Snowflake Cortex AI), LLM gateways (LiteLLM, LangFuse), and coding agents (Claude Code, Cursor, GitHub Copilot, Devin Desktop, Codex) via TokenShift.
Yes. PointFive integrates with Jira, ServiceNow, and Slack for workflow, and its MCP server brings cost intelligence directly into Claude, Cursor, ChatGPT, VS Code, and other MCP-compatible tools, so engineers can investigate cost questions and act on optimization opportunities without leaving their workflow.
Stop guessing what AI costs.
Connect your environment in under 15 minutes. PointFive starts finding savings immediately.