Inference Economy: From Shadow to Spotlight

The rapid adoption of AI workloads into core applications and internal operational workflows introduces major governance and financial challenges. These include unmonitored compute consumption, fragmented provider tooling, and spiraling "Shadow AI" expenses.

An organization that doesn't understand its AI usage granularly enough is at serious risk of efficiency loss and unchecked operational costs. They need a Cloud Management Platform not only to understand "classic" service utilization, but also to provide real-time visibility into token usage and GPU utilization.

To meet these modern demands, Maestro is evolving into an Infrastructure Orchestrator and Platform Engineering Hub built specifically to govern the emerging "Neocloud" reality (according to the roadmap we shared earlier).

With the new capabilities, Maestro extends its FinOps, security, and orchestration engines directly to LLM usage by applying two new approaches:

• Treating a customer's LLM Orchestrator as a provider
• Using native provider tools to allocate LLM costs (an implementation for AWS Bedrock)

Treating LLM Orchestrator as Another Provider

To set up a secure enterprise AI deployment, modern organizations frequently establish centralized AI orchestration gateways that create a unified "bridge" between enterprise teams, applications, and Large Language Models. These orchestrators abstract model providers, manage prompt pipelines, and enforce access and data safety policies across the organization.

Although responsible teams may have the necessary toolset to track LLM usage per group/team/project, the costs and statistics associated with these activities are often provided separately from overall infrastructure costs and data. This creates a gap between different types of consumption information related to the same activities, resulting in additional effort for data aggregation and analysis—higher operational costs, higher error risks, and more time needed to get results.

Maestro addresses this challenge in a smooth and elegant way. It allows adding an LLM orchestration engine as another Provider, bringing LLM consumption data into the same environment as other infrastructure costs.

This approach allows tracking LLM orchestrator consumption at the enterprise level, as well as breaking down the data by specific tenant with details by token usage.

The enterprise-level totals and per-service details can be found on the Maestro Radar page (with both totals and per-unit detailing):

For a specific tenant/project/organizational unit, the details are available along with any other utilization data:

Note that Maestro needs usage and cost allocation data from the LLM Orchestrator side, which may require additional effort to import if not provided via API.

Using Inference Profiles to Get Detailed Information on LLM Usage

Public cloud providers are introducing native mechanisms to enable AI workload management. One example is inference profiles that Amazon launched for Bedrock in spring 2026. These profiles act as custom wrappers and serve two main functions:

• Routing requests across regions to enhance throughput and resilience
• Enabling custom tags to track and allocate model invocation costs and usage per project or team

For Maestro, we set inference profiles for accounts per LLM by specifying account tags in profiles. These tags are later used to split the final costs into per-project bills:

The workflow operates through a structured pipeline:

1. Profile Configuration: Inference profiles are established within AWS Bedrock. Each profile is mapped to a specific Large Language Model and injected with unique account identifiers via tags.
2. Data Aggregation: Maestro retrieves utilization data together with profile info and builds a centralized billing report. It aggregates total LLM consumption across the enterprise while retaining the tag-based metadata.
3. Automated Chargeback: The aggregated data is logically partitioned, splitting model costs into distinct per-account or per-project bills.

Inference Profile setup sample

The primary advantage of this tagging strategy is its natural integration with Maestro's existing billing and analytics engines. Cost allocation tags are applied natively within the AWS ecosystem, so the standard billing export already contains the necessary dimensions for a granular chargeback model.

Consequently, Maestro ingests and processes this billing data just as it would with any standard infrastructure expense (such as EC2 instances or S3 storage). There is no requirement to build, maintain, or troubleshoot custom API integrations between Maestro and individual LLM providers. Maestro natively acts as the processor for billing data, translating tagged telemetry into detailed utilization data.

Navigating the Future of the Inference Economy

As enterprises scale in the Neocloud, unmonitored compute and growing "Shadow AI" expenses make unified cost governance essential.

Maestro addresses these challenges by treating LLM orchestrators as native providers and leveraging mechanisms like AWS Bedrock inference profiles. It consolidates AI consumption telemetry alongside traditional infrastructure data, meaning your teams no longer have to piece together data from different sources—you get precise, tag-based chargebacks natively in Maestro.