DoiT — the cloud‑cost intelligence firm behind Cloud Intelligence™ — has rolled out Attribute, a new platform that promises real‑time AI spend attribution down to the token, model request and GPU cycle. The announcement, made on July 7, 2026, signals a shift toward finer‑grained cost visibility for enterprises wrestling with soaring generative‑AI budgets.
What Attribute does
Attribute installs a lightweight eBPF sensor inside the operating‑system kernel and begins monitoring every unit of compute, memory, network and I/O that a workload consumes. The sensor correlates these metrics with the originating process, container, pod and, crucially, with outbound calls to managed LLM services such as OpenAI, Anthropic, Google Gemini and AWS Bedrock. By joining provider‑level token pricing with on‑premise resource usage, Attribute delivers “token economics” that break down cost per token, per request, per session, per customer and per AI agent—all without requiring SDKs, tagging policies or code changes.
Why granular AI cost tracking matters
Enterprise AI spend is accelerating. Gartner forecasts global AI‑related expenditures to exceed $500 billion by 2025, with a majority of that growth driven by generative‑AI workloads. Yet a recent Forrester survey of 500 senior technology leaders found that only 15 % can calculate AI ROI without significant bottlenecks, and many report cost overruns hovering near tolerance limits. The lack of transparent attribution makes it difficult for finance and product teams to justify AI projects, allocate budgets, or optimise model usage.
How Attribute differs from existing tools
Traditional cost‑allocation approaches rely on manual instrumentation: developers embed SDK calls, enforce tagging conventions, or maintain separate billing accounts for each model provider. Those methods fall short when multiple tenants share a GPU or when a single API key services dozens of downstream services. Attribute flips the model by measuring what actually runs at the kernel level, eliminating the need for any developer effort. Competing solutions—such as cloud‑provider cost‑management dashboards or third‑party tagging platforms—still depend on metadata that can be incomplete or inaccurate. Attribute’s eBPF‑based telemetry provides an audit‑ready trail that aligns directly with provider pricing structures.
Implications for enterprise AI strategy
For CIOs and AI program managers, the ability to assign spend to specific features or agents unlocks several strategic levers:
- Margin optimisation – Teams can pinpoint high‑cost prompts or inefficient model versions and re‑engineer them for lower token consumption.
- Charge‑back accuracy – Finance can implement precise internal billing, aligning costs with product lines, business units or external customers.
- Governance and compliance – Real‑time visibility satisfies audit requirements and helps organisations stay within regulatory spend caps.
The technology also dovetails with broader AI automation platforms. By extending the same kernel‑level measurement to Kubernetes clusters, multi‑tenant databases and storage buckets, enterprises gain a unified view of the full cost stack that supports AI‑driven services.
Competitive landscape and adoption considerations
While major cloud ecosystems—Google Cloud, Amazon Web Services, Microsoft Azure—offer native cost‑allocation dashboards, they generally stop at the service‑level granularity and do not dissect token‑level usage across mixed‑provider environments. Salesforce and Adobe’s AI‑enhanced SaaS products similarly lack transparent backend cost reporting, leaving large‑scale adopters to build custom solutions.
Attribute’s zero‑instrumentation promise reduces deployment friction; DoiT claims a typical installation completes in 15 minutes. However, organisations must still grant kernel‑level access, which may raise security‑policy questions in highly regulated sectors. Early adopters should pilot the sensor in a controlled namespace, validate the fidelity of the token‑cost mapping, and integrate the output with existing FinOps tooling.
What it means for enterprise marketing teams
Marketing operations increasingly rely on AI‑generated content, personalised recommendations and autonomous agents. With token‑level spend data, marketers can directly measure the cost of a campaign’s AI components, compare the ROI of different prompting strategies, and negotiate better pricing with model providers based on actual usage patterns. The granular insight also supports budget forecasting for upcoming product launches that incorporate generative AI features.
Marketing teams can leverage this data to fine‑tune spend, justify budgets, and align AI initiatives with broader digital advertising goals.
Future outlook
If the industry embraces kernel‑based telemetry as a standard, we may see a new class of “AI cost observability” platforms that integrate seamlessly with existing AIOps stacks. Such tools could enable automated throttling of expensive prompts, dynamic model selection based on cost‑performance curves, and real‑time alerts for anomalous spend—capabilities that align with the next wave of AI governance frameworks.
Market Landscape
The AI cost‑management market is still nascent. IDC predicts that by 2027, 30 % of Fortune 500 firms will have deployed dedicated AI‑spend monitoring solutions, up from less than 5 % in 2023. Vendors are differentiating on three fronts: data granularity, multi‑cloud coverage, and integration depth with FinOps platforms. Attribute’s kernel‑level approach positions it at the high‑granularity end of the spectrum, while its support for major LLM providers ensures broad multi‑cloud relevance. Competing entrants—such as CloudZero’s AI‑cost module and Snowflake’s usage analytics—focus more on aggregating cloud‑billing data rather than tracing individual tokens. As enterprises demand tighter control, the market is likely to consolidate around solutions that can prove audit‑ready, real‑time attribution without imposing developer overhead.
Top Insights
- Token‑level visibility: Attribute tracks each AI token, request and GPU cycle, giving finance teams a concrete basis for cost allocation.
- Zero‑instrumentation deployment: The eBPF sensor installs in minutes, removing the need for SDKs or tagging policies that often stall adoption.
- Cross‑provider coverage: By joining on‑premise telemetry with OpenAI, Anthropic, Google and AWS pricing, the platform unifies spend across disparate LLM ecosystems.
- Enterprise impact: Accurate AI spend attribution improves margin management, supports internal charge‑back, and strengthens governance for regulated industries.
- Market momentum: Gartner and IDC forecasts indicate rapid growth in AI‑cost management tools, with a shift toward real‑time, granular observability.
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