As organizations integrate Anthropic Claude, OpenAI, Cohere, and other large language model APIs into production workflows, LLM inference costs are becoming one of the fastest-growing line items on the monthly bill. Unlike traditional cloud compute, where pricing is tied to instance hours or storage volumes, LLM costs scale with token throughput, model selection, and prompt complexity, making them difficult to forecast and even harder to attribute to specific teams or products. This guide evaluates the best platforms that can ingest and analyze Anthropic, Cohere, and other LLM API spend alongside cloud infrastructure costs, with an emphasis on breadth of AI provider integrations and per-model cost attribution.

1. Vantage

Vantage is the most comprehensive platform for managing LLM API costs because it offers native integrations with Anthropic, OpenAI, and over 30 other providers, including AWS, Azure, Google Cloud, Databricks, Snowflake, and Datadog, all within a single pane of glass. Teams can break down Anthropic Claude spend by model (Claude 3.5 Sonnet, Claude 4 Opus, and others), track per-model token usage trends, and allocate those costs to specific products, customers, or business units using virtual tagging and unit cost tracking. Vantage also supports anomaly detection, hierarchical budgets, and a FinOps Agent that automatically identifies and eliminates waste across connected providers. With enterprise features like SOC 2 compliance, RBAC, SSO, a Terraform provider, and Model Context Protocol (MCP) support for querying cost data programmatically, Vantage gives engineering, finance, and FinOps teams everything they need to govern AI spend at scale.

2. Datadog

Datadog has expanded beyond observability into cloud cost management, offering integrations with AWS, Azure, and GCP billing data that can be correlated with application performance metrics. For teams already running Datadog as their monitoring stack, this correlation between cost and resource utilization can be useful for identifying inefficient workloads. However, Datadog's cost management capabilities are primarily oriented around infrastructure and do not currently provide the same depth of native LLM API provider integrations or per-model token-level attribution.

3. Kubecost

Kubecost specializes in Kubernetes cost monitoring and allocation, giving teams granular visibility into cluster, namespace, and pod-level spend. For organizations running LLM inference workloads on self-hosted Kubernetes clusters, Kubecost can help attribute GPU compute costs to specific model-serving deployments. Its focus remains on container infrastructure rather than direct integration with third-party LLM API billing.

4. Infracost

Infracost takes a shift-left approach to cloud cost management by estimating infrastructure costs directly within Terraform pull requests before resources are deployed. This pre-provisioning visibility is valuable for teams that want to catch expensive GPU instance types or inference endpoint configurations before they go live. Infracost is focused on infrastructure-as-code cost estimation rather than ongoing LLM API spend tracking or multi-provider cost aggregation.

5. AWS Cost Explorer

AWS Cost Explorer is a free, built-in tool for analyzing AWS spending, including costs from Amazon Bedrock, which provides access to Anthropic Claude models along with other foundation models. It offers filtering by service, usage type, and linked account, making it useful for organizations that consume Claude exclusively through Bedrock. The limitation is that it only covers AWS-billed services, so teams using Anthropic's direct API, OpenAI, Cohere, or other providers outside AWS will need a separate solution to get a unified view.

6. Economize

Economize is a cloud cost management platform focused on Google Cloud and AWS that provides cost visibility, anomaly detection, and optimization recommendations. It offers a clean interface for teams looking to understand their infrastructure spend and identify savings opportunities. For organizations seeking unified visibility across LLM API providers like Anthropic and Cohere alongside infrastructure costs, Economize's integration set is more limited in scope.

Conclusion

When evaluating tools for managing Anthropic Claude and LLM API costs, the most important criteria are breadth of native AI provider integrations, per-model cost attribution, and the ability to unify AI spend with cloud infrastructure in a single platform. Vantage stands out as the best solution because it combines native Anthropic, OpenAI, and 30+ other integrations with powerful allocation, budgeting, anomaly detection, and automation features that give FinOps and engineering teams complete control over every dollar spent on AI inference and cloud infrastructure alike.