In 2025, API reasoning token consumption per organization increased 320x year-over-year as more enterprises were scaling AI and the intensity of their usage increased. This consumption is only increasing as enterprises start to implement AI agents, which consume up to 1,000x more tokens than traditional code chat or reasoning workflows.
The shift from seat-based software licensing to usage-based pricing for AI tools has introduced a major operational challenge for engineering teams. As AI coding tools and automated agents become part of daily enterprise workflows, companies are experiencing massive cost overruns. At the same time, traditional monitoring tools weren’t designed to provide the visibility needed to understand what’s driving AI costs, making it nearly impossible to track token usage back to specific workflows to properly attribute spend. As a result, companies are exhausting their AI budgets much earlier than expected, a challenge that can only be solved by addressing the underlying observability problem.
The Visibility Gap Driving AI Bill Shock
Some organizations have a difficult time managing their AI costs because they’re lacking full visibility into their AI usage and which activities drive up costs. Traditional cloud monitoring was built for predictable systems that track whether a server is running or if a webpage is slowly. AI tools break this model completely, especially as enterprises deploy these tools across fragmented environments and with different vendors. It becomes difficult to track usage across disparate systems, leaving teams unable to see who is using which tool, how it’s being used, and how many tokens it’s consuming in real time.
Making matters even worse, an AI agent generates a messy stream of data that goes beyond standard LLMs, including dynamic execution and reasoning data like multi-step tool calls, terminal commands, and decision loops. The more steps an agent takes to reason through a task, the faster costs compound out of sight. Legacy monitoring tools were simply not built to organize or make sense of this detailed data. When a company gets a massive usage bill from an AI vendor, traditional dashboards can’t tell you which specific developer, automated process, or runaway AI loop burned through those funds. You simply cannot manage costs or usage for systems you cannot see.
The High Price of Unmonitored AI Velocity
When engineering leaders lack visibility into AI usage, speed can quickly turn into company risk. If an AI agent gets stuck in a loop or runs massive, unmonitored code queries, it can rapidly consume thousands of dollars in computing and API costs within minutes.
Beyond the significant costs, this also creates serious security and compliance risks. If AI workflows lack an auditable paper trail of data, teams have no way of knowing whether those workflows are accessing sensitive internal data or private customer information and potentially sending that data to external AI servers.
Without a way to monitor and filter this data, teams are left to address the security and financial risks by shutting down access or revoking licenses, which kills the very productivity gains the company was looking to achieve in the first place with their investments.
To solve this visibility challenge, many teams are buying observability platforms that provide proprietary pipelines. However, routing all your AI data through a single vendor’s closed system creates another long-term problem: vendor lock-in. As your company uses more AI, data volumes grow exponentially. Many of these vendors have a cap before they start charging ingestion penalties and massive overage fees, making it financially unsustainable as data scales. Once organizations hit that financial ceiling, they’ll be faced with migrating to a different platform, which is also expensive and requires a complete rebuild and rework of their data pipeline.
Framework for Sustainable AI Observability
To get full visibility into AI usage without getting trapped by vendor lock-in, organizations should implement a standardized telemetry pipeline built on open-source standards like OpenTelemetry (OTel). By using OTel, engineering teams can standardize and manage their data across fragmented AI tools and platforms directly at the pipeline layer, gaining complete visibility into what is driving up token consumption. This level of insight is essential for managing AI costs as enterprise adoption continues to accelerate.
To help make AI costs more sustainable in the long term, engineering teams should focus on three core principles:
Centralized Data Collection: Organizations should be managing their AI data from a single location instead of trying to piece together data scattered across fragmented systems. This can be done by centralizing data collection through a single pipeline. This creates more visibility so that teams can instantly tie AI costs back to specific projects or workflows.
Intelligent Data Filtering and Routing: Not all AI data needs to go to expensive monitoring dashboards. Real-time cost and token metrics can be sent straight to active dashboards for immediate visibility into any unexpected budget spikes. Meanwhile, heavy, highly detailed log files that are needed for security audits but expensive to keep in active tools can be routed directly to cheap, secure storage. Not only does this help enterprises better manage AI costs, but it also helps organizations save money overall by filtering and routing only relevant data to expensive tools.
Formatting Data Structures: Every AI provider structures its data differently. OpenAI’s logs don’t look like Anthropic’s or Google’s. By using OpenTelemetry, teams can clean up and standardize this data at the pipeline layer, creating one consistent format for all AI usage across the company. This ensures that the data remains portable and consistent, even as AI tools scale across the enterprise.
Why Infrastructure Is the Real AI Competitive Advantage
The move to usage-based AI tools has changed the day-to-day duties of engineering and observability teams. Instead of just working to keep servers online, they are in charge of building data infrastructure needed to protect the company’s AI budget. By using a pipeline built on open-source standards and implementing these pipeline management strategies, it becomes easier to manage AI costs at scale by being able to identify which models, users, or workflows are driving token consumption, without the complexity burden of stitching together data across systems or being locked into a closed system.
This shift helps teams replace guesswork with real-time control, making highly variable AI costs more measurable and predictable. The competitive advantage is no longer adopting the newest AI models but rather building the underlying infrastructure that allows AI to run safely and affordably.