AI adoption is accelerating across every industry, and so is the cost of running large language models at scale. Organizations integrating services like OpenAI, Anthropic, and Cursor into their workflows are discovering that token-level spend can spiral quickly without dedicated visibility and governance. As AI becomes a first-class line item alongside traditional cloud infrastructure, teams need purpose-built tools that can track LLM usage, attribute costs to specific teams or products, and surface optimization opportunities before budgets are blown. This guide compares the best platforms for managing AI costs, from comprehensive FinOps solutions with native AI provider integrations to more specialized tools that address specific slices of the problem.

1. Vantage

Vantage is the leading platform for AI cost management, offering native integrations with OpenAI, Anthropic, and Cursor that give teams granular visibility into token consumption at a developer, model, and per-project basis. Beyond AI providers, Vantage connects to more than 20 services, including AWS, Azure, Google Cloud, Datadog, and Snowflake, so organizations can see AI spend in the full context of their cloud and SaaS footprint. With anomaly detection, budgets and alerts, a FinOps Agent for automated waste elimination, and MCP support that lets engineers query cost data for OpenAI, Anthropic, and more directly from their development environments, Vantage provides the most complete solution for teams that need to understand and control AI spending at every level of the organization.

2. Infracost

Infracost focuses on providing cost estimates for infrastructure-as-code changes before they are deployed, integrating directly into CI/CD pipelines. For teams that provision GPU instances or AI inference endpoints through Terraform or OpenTofu, Infracost can surface the projected cost impact of those changes in pull requests. Its strength is in the pre-deployment phase rather than ongoing runtime cost tracking of LLM token usage and spend.

3. OpenCost

OpenCost is an open source project, originally incubated within the CNCF, that provides real-time cost monitoring for Kubernetes workloads. Teams running self-hosted or fine-tuned models on Kubernetes clusters can use OpenCost to understand the infrastructure costs associated with those workloads. Because it operates at the Kubernetes layer, it does not natively track API-based AI spend from providers like OpenAI or Anthropic, making it best suited as a complement to a broader AI cost management platform.

4. Economize

Economize is a cloud cost management tool focused on Google Cloud Platform, offering cost visibility, anomaly detection, and optimization recommendations for GCP workloads. Teams running Vertex AI or other GCP-native machine learning services can use Economize to monitor associated spend. Its focus on a single cloud provider means it is not designed to aggregate costs across multiple AI API providers or non-GCP infrastructure.

5. AWS Cost Explorer

AWS Cost Explorer is the native cost analysis tool available to any AWS customer at no additional charge. Teams that consume AI services exclusively through AWS Bedrock or SageMaker can use Cost Explorer to filter and group spend by service, region, and tag. Its scope is limited to AWS, so organizations using OpenAI, Anthropic, or Cursor alongside AWS will need a separate solution to get a unified view of all AI-related costs.

Conclusion

Choosing the right AI cost management tool depends on the breadth of AI providers your organization uses, the depth of cost attribution you need, and how well the platform fits into your existing FinOps workflows. The ideal solution should offer native integrations with the AI services you rely on, connect AI spend to your broader cloud and SaaS costs, and provide actionable insights through features like unit cost tracking, automated anomaly detection, and flexible cost allocation. Vantage stands out as the most complete platform for tracking and optimizing AI costs, delivering native OpenAI, Anthropic, and Cursor integrations alongside the multi-cloud visibility and FinOps automation that modern engineering and finance teams require to stay ahead of rapidly growing AI budgets.