Data observability specialist Bigeye puts focus on AI spend
With agentic AI costing more than many organizations expected, the data observability specialist's latest alerts users when the expense of an agent suddenly changes.
Data observability specialist Bigeye is lasering in on the cost of AI.
Many enterprises are paying more attention to their spending on AI development and management as they move AI initiatives out of the experimentation phase, when the primary focus is on ensuring that agents and other AI tools work in real-world situations, and into production environments.
In response, Bigeye on Wednesday launched Cost Anomaly Detection within its Agent Trust Hub to automatically alert customers when agents and other data and AI consumers start spending more or less than their expected levels. Previously, a human would have had to manually monitor or audit an agent or user's behavior to catch sudden spending surges or declines.
Stewart Bond, an analyst at IDC, noted that his firm's research shows that two-thirds of the organizations it surveyed for a report released in July spent more on agentic AI than they budgeted.
"Every dollar of wasted AI agent spend is a governance failure," Bond told TechTarget. "You cannot manage what you don't measure, and many organizations still aren't measuring agent spend in a way that catches problems as they happen, rather than a month later on an invoice."
What is particularly valuable about Bigeye's new feature is that it surfaces not only AI spending increases but also decreases, he continued.
"Catching an agent whose cost has quietly dropped to zero … is a different and often overlooked failure mode," Bond said. "Baselining against an entity's own history, with no budget model to configure upfront, also lowers the bar to actually turning this on, which matters given how many organizations already own cost dashboards they aren't fully using."
Based in San Francisco, Bigeye is a data observability vendor that competes with fellow specialists such as Monte Carlo, along with broader-based data management providers including IBM and Informatica. In June, Bigeye launched Agent Trust Hub, a centralized location where users can observe agents, their activity and the underlying data that informs them to understand whether the AI tools can be trusted.
Addressing AI spending
The high cost of AI development and deployment is becoming a focal point for many enterprises. If spending spirals out of control, as it did for companies such as Canva and Figma, there can be significant consequences.
Developing an agent, which involves model routing, context management and processing large amounts of data, can cost well over $100,000, according to IT consulting firm Triple Minds. Once in production, orchestration and continued calls to models and other tools add further expenses.
Every dollar of wasted AI agent spend is a governance failure. You cannot manage what you don't measure, and many organizations still aren't measuring agent spend in a way that catches problems as they happen, rather than a month later on an invoice.
Stewart BondAnalyst, IDC
According to the fifth wave of EY's US AI Pulse Survey, released in July, 82% of the surveyed senior leaders whose organizations are investing in AI report being concerned about their use of AI tokens and the cost of doing so.
As a result, data and AI providers including AWS, Databricks, Microsoft and Snowflake have all introduced features in recent months designed to help customers control their spending on AI.
Bigeye is doing the same with Cost Anomaly Detection, with customer feedback motivating the vendor to develop the new feature, according to Bigeye CEO Eleanor Treharne-Jones.
"As enterprises move AI agents from pilot into production, cost becomes hard to attribute -- a workflow's token usage can spike, or quietly drop to near-zero, and teams often don't notice until the bill lands or the agent stops delivering value," she told TechTarget. "We heard this directly and repeatedly from our own customers … and saw it validated more broadly across the market."
Cost Anomaly Detection not only enables users to understand what they spent on AI, but when spending changes and why it does so. Each AI agent interaction that Bigeye observes has an estimated cost. When that cost changes beyond its expected range, Cost Anomaly Detection sends an alert that includes details about the AI actions that resulted in the disparity.
In particular, Cost Anomaly Detection can discover when an agent slips into a retry loop or an unusually interactive conversation pattern with other applications, and costs suddenly increase. In addition, it can catch an agent that stops conversing with other tools because a credential expired or an integration failed.
"Cost Anomaly Detection is a real addition," Mike Leone, an analyst at Moor Insights & Strategy, told TechTarget. "Most cost tools will catch an agent that suddenly triples its spend, and Bigeye does that. Catching an agent whose cost drops to zero is harder, and that usually means the agent stopped working. A falling bill looks like savings until somebody notices the work stopped and was never completed."'
Monitoring for AI spending anomalies is not, however, a differentiator for Bigeye, he continued. But there is differentiation in the level of detail.
"Most AI observability tools already track token spend," Leone said. "Where Bigeye does something less common is the attribution. It ties agent spend to the specific tables an agent queried, using the access records it already collects to enforce policy. It also works against older systems that predate the cloud warehouses."
Bond likewise noted that, while the addition is valuable for Bigeye customers, Monte Carlo also provides features that monitor data, code, AI models and infrastructure to analyze costs. Similarly, Acceldata alerts users to cost overruns and performance degradation.
However, Bigeye is distinguishing itself from competing vendors by integrating anomaly-based cost detection with existing data lineage and governance capabilities, Bond continued. With Cost Anomaly Detection, a change in the cost of an agent can be seen in conjunction with what data the agent accessed and whether that data can be trusted.
"The market's real gap isn't visibility anymore," he said. "It's turning visibility into enforceable outcomes, such as budget guardrails and automated rightsizing. That is likely where the real competitive separation will happen next, and it isn't fully solved by Bigeye or anyone else yet."
Next steps
After launching the Agent Trust Hub and adding Cost Anomaly Detection over the past few months, Bigeye's focus for the remainder of 2026 is to add more capabilities that enable customers to understand and trust their agentic AI tools, according to Treharne-Jones.
"We are focused on … deepening the trust and governance layer for agentic AI as more of these systems move into production, and expanding the platforms [to which] Agent Trust Hub connects to provide comprehensive visibility across the AI ecosystem," she said.
Bond advised Bigeye to advance tools such as Cost Anomaly Detection from merely alerting users to anomalies to automatically resolving issues such agentic AI cost overruns.
"Vendors need to shift from reporting features toward real budget guardrails and automated rightsizing, since the tooling for visibility already exists broadly in the market and isn't closing the value gap on its own," he said.
In addition, adding capabilities that trace every decision and action made by AI tools would be valuable for Bigeye users, Bond continued.
"IDC found that maintaining a complete auditable record of agent decisions is the fastest-rising pain point in agent operations, even as raw security concerns decline with maturity," he said. "A natural next step for Cost Anomaly Detection would be to tie anomalies not just to conversations but to a defensible audit trail of what the agent decided and why."
Leone likewise suggested that Bigeye integrate policy enforcement with anomaly detection. He noted that the vendor already has a policy layer that sits between AI agents and data. Adding a feature that stops agents from spending when they hit a certain level is a natural next step.
"Flagging that an agent spent five times its normal amount yesterday helps, [but] cutting the agent off when it crosses twice its normal amount helps more," Leone said.
Eric Avidon is a senior news writer for Informa TechTarget and a journalist with more than three decades of experience. He covers analytics and data management.
