AI & GPU cost management
Make every AI dollar earn its keep.
AI spend is the fastest-growing line on your cloud bill — and the least understood. CarnotCloud tracks tokens, models, GPUs and AI APIs alongside the rest of your cloud, and ties them to the features, teams and customers they serve.
Cost per 1K resolved tickets
$18.40
↓ 22% after prompt caching
GPU utilization (p5 fleet)
61%
$14K/mo idle capacity
1 view
for LLM APIs, managed AI services and GPU infrastructure
Cost per outcome
per request, conversation, agent run, feature or customer
Hours
not weeks, to catch a runaway model, prompt or training job
Capabilities
What you get with AI & GPU Cost Management
LLM & API cost tracking
OpenAI, Anthropic, Azure OpenAI, Amazon Bedrock and Google Vertex AI costs by model, API key, project and token type.
GPU utilization & idle cost
See GPU hours, utilization and idle capacity across training and inference fleets — in the cloud and in Kubernetes.
AI unit economics
Cost per conversation, per document, per agent run or per customer, so product teams can price and design AI features profitably.
AI anomaly detection
Get alerted when a prompt change, retry loop or model swap sends spend off-trend — with the key, model and owner attached.
Model optimization insights
Find workloads that could use a smaller model, prompt caching, batch APIs or provisioned throughput — with projected savings.
AI budgets & guardrails
Budgets per team, product or API key with alerts at the thresholds you choose, before the invoice arrives.
From tokens to margins
Know what every AI feature really costs
Token counts don’t mean much to a CFO. CarnotCloud joins AI usage with your product telemetry so you can report cost per customer, per feature and per outcome — and see which AI investments are paying back.
- Map API keys, projects and tags to features and customers
- Blend LLM API, GPU, vector database and storage costs per feature
- Track AI cost as a share of revenue and gross margin
- Share-ready reports for product and finance leaders
Cost per 1K resolved tickets
$18.40
↓ 22% after prompt caching
GPU utilization (p5 fleet)
61%
$14K/mo idle capacity
GPU efficiency
Stop paying for accelerators that sit idle
GPUs are the most expensive resources you run. CarnotCloud shows utilization by node, pod and job, flags idle reservations, and recommends right-sized instance types and capacity reservations.
- Per-pod GPU allocation via NVIDIA DCGM
- Idle and fragmented GPU capacity alerts
- Training vs inference cost split
- Capacity reservation and commitment recommendations for GPU fleets
- checkout $41.2K
- search $28.7K
- ml-inference $22.3K
- platform $14.1K
- Idle & unallocated $19.8K
+6 pts gross margin on AI features
“Our AI bill was a black box. Now we see cost per customer for every model and feature, and we’ve added six points of gross margin by routing work to the right model.”
FAQ
AI & GPU Cost Management: common questions
Which AI providers do you support?
Can we attribute AI cost to individual customers?
Do you see our prompts or completions?
Keep exploring
Works even better together
Unit Economics
Connect cloud cost to business metrics.
Learn moreAnomaly Detection
Machine-learning anomaly detection for AWS, Azure, Google Cloud, Kubernetes and AI spend.
Learn moreKubernetes Cost Intelligence
Bill-accurate Kubernetes cost allocation by cluster, namespace, workload, pod and label — with idle and shared cost distribution and request rightsizing recommendations for EKS, AKS, GKE and OpenShift..
Learn moreSee AI & GPU Cost Management on your own data.
Book a 30-minute demo tailored to your stack, or start with a free savings assessment — read-only, no agents, no obligation.
- Read-only, agentless setup
- First insights in 15 minutes
- SOC 2 Type II & ISO 27001