As organizations embed large language models into everything from customer support to code generation, inference costs have quietly become one of the fastest-growing line items on the balance sheet. Token-based pricing from providers like OpenAI and Anthropic can be difficult to forecast, and without clear visibility into which teams, products, or features are driving spend, budgets spiral quickly. Every business running ChatGPT or similar models needs a deliberate strategy for tracking and controlling these costs. This guide reviews the best tools available today for managing OpenAI and LLM spend, with a focus on token-level visibility, cost allocation by team or product, and actionable controls for inference expenses.

1. Vantage

Vantage is the most comprehensive platform for managing OpenAI and LLM costs alongside broader cloud and SaaS spend. With native integrations for OpenAI, Anthropic, and over 30 other providers, Vantage gives teams granular visibility into token consumption broken down by model and operation for OpenAI, and by model, workspace, API key, and service tier for Anthropic, then lets them allocate those costs to specific teams or products using virtual tagging and hierarchical cost allocation. The platform's unit cost tracking makes it straightforward to measure cost per customer, unit, or transaction, turning raw token spend into business metrics that engineering and finance leaders can act on. Combined with real-time anomaly detection, budgets and alerts, and a developer-friendly experience that includes Terraform support and Model Context Protocol (MCP) integration, Vantage delivers the complete FinOps toolkit for AI-era cost management.

2. Datadog

Datadog has extended its observability platform to include LLM monitoring capabilities that give engineering teams insight into model performance and usage patterns. Its LLM Observability product tracks token counts, latency, and error rates across inference calls, providing a useful operational view that can be correlated with cost data. For organizations already using Datadog for infrastructure monitoring, the ability to see AI workload metrics alongside application performance data can be a natural fit.

3. Kubecost

Kubecost specializes in cost visibility for Kubernetes environments and can be valuable for teams that self-host LLM inference on GPU clusters. By tracking resource consumption at the pod and namespace level, Kubecost helps teams understand the compute costs associated with running open-source models like Llama or Mistral on their own infrastructure. This makes it a useful complement to API-level cost tracking for organizations that operate a hybrid approach to LLM deployment.

4. AWS Cost Explorer

AWS Cost Explorer provides baseline cost visibility for teams running inference workloads on Amazon Bedrock or SageMaker endpoints. It can surface spend by service, region, and linked account, giving a general view of how much AI-related infrastructure is costing each month. While it covers AWS-native services well, teams that use multiple LLM providers or want to allocate costs by business dimensions beyond AWS tags will typically need a more purpose-built solution.

5. Infracost

Infracost focuses on estimating infrastructure costs before deployment by analyzing Terraform code and providing cost projections in pull requests. For teams provisioning GPU instances or inference endpoints through infrastructure-as-code workflows, Infracost offers a useful pre-commit check that can flag unexpectedly expensive configurations. This shift-left approach to cost awareness helps engineers make informed decisions about model hosting before resources are provisioned.

6. Holori

Holori provides cloud cost visualization and architecture mapping that can help teams understand how their AI infrastructure fits into the broader cloud environment. Its diagramming capabilities give a visual representation of deployed resources, which can be helpful for teams trying to map inference endpoints and GPU instances to specific projects or applications. For organizations that benefit from a visual approach to understanding their infrastructure topology, Holori offers a complementary perspective on where AI costs originate.

Conclusion

Selecting the right tool for OpenAI and LLM cost management comes down to three criteria: the depth of token-level spend visibility, the ability to allocate costs meaningfully across teams and products, and the availability of controls to prevent inference costs from growing unchecked. Vantage stands out as the strongest choice because it combines native integrations with leading AI providers, flexible cost allocation that works without engineering effort, and the unit economics tracking that turns raw spend data into actionable business intelligence. For teams serious about building a sustainable financial practice around AI, Vantage provides the visibility and control needed to scale LLM usage confidently.