Enterprises have accelerated their pace towards data-driven decision-making. Their ability to manage, understand, and optimize AI and data has become a strategic priority. Revefi is helping organizations address this challenge by bringing greater visibility and control to their AI and data environments. Its approach focuses on simplifying AI economics, AI and data operations, improving efficiency, and enabling teams to make informed decisions with confidence. By working across leading AI providers and agent platforms such as OpenAI, Anthropic, Google Gemini, Amazon Bedrock, Amazon Nova, and OpenClaw, and cloud data platforms including Snowflake, Databricks and Google BigQuery, it combines its FinOps, observability, and intelligence capabilities to help enterprises gain more value from their AI and data investments. The company’s focus remains on making AI and enterprise data more accessible, actionable, and aligned with business priorities.
Deriving Value from AI and Data
Revefi empowers enterprises to establish greater control, accountability, and business value across their AI and modern data environments. Its AI-driven platform provides executive teams with unified visibility into AI usage, performance, cloud data costs, operational performance, and data reliability across leading platforms. By identifying inefficiencies and translating complex infrastructure signals into actionable insights, the team enables organizations to optimize technology investments, strengthen governance, improve operational excellence, and scale data and AI initiatives with greater confidence, efficiency, and financial discipline.
Autonomous Enterprise Intelligence
The team’s mission has remained consistent since its founding in 2021: to equip AI and data teams with the right data, at the right time, at the right spend. What has changed is the extent to which machines can credibly assume ownership of that work. The fundamental shift is that enterprises are moving from instrumentation to accountability.
For three decades, the operational stack was designed to inform dashboards, alerts, and reports. The implicit contract was that a human would interpret the signal and act. The contract broke, as systems scaled faster than the teams managing them, and pricing models shifted. Consumption-based platforms now bill inefficiency continuously, not once.
The evolution is moving from shipping information to shipping work. With agentic AI, the same thesis extends upward; the same questions of cost, correctness, and timing apply to tokens and agents as much as to credits and queries.
Mr. Sanjay Agrawal, CEO, Revefi adds, “Our AI DBA tells you more than the fact that a warehouse is oversized; it right-sizes it, opens the pull request, and reports what it saved.”
Leadership experience reinforces this conviction. With more than a decade at Microsoft Research developing the first self‑tuning database technology, followed by work at Google and co‑founding ThoughtSpot, the founder holds a clear view: systems should manage themselves. The bottleneck was never human intelligence. It was human hours.
Autonomous Cloud Governance
Enterprises have invested heavily in dashboards and monitoring. Still, data teams spend significant time reacting to problems. The team’s view is that detection represents perhaps ten percent of the job, and observability sold the ten percent as the whole.
A dashboard creates a ticket. Someone must triage it, reproduce it, trace the root cause across dependencies, assess whether the fix is worth the disruption, secure the buy-in from workload owners, and then implement it. Observability tools excel at the first step and leave the other six to teams already underwater. Data teams spend thirty to forty percent of their time detecting, investigating, and fixing quality issues. Adding another alert stream compounds the problem, creating alert fatigue.
There is also a scale ceiling, structural rather than human. A skilled DBA can govern thirty to forty databases. Beyond that, hiring scales linearly with data growth, which no enterprise will fund. Manual SQL checks typically cover under ten percent of warehouse tables, leaving most of the estate unwatched.
Lastly, there is a timing failure tied to cloud economics.
Sanjay states, “Many teams still treat cost optimization like surgery; something you do when there is a problem.”
With consumption-based pricing, by the time issues are noticed, the cost has already been incurred. Optimization must be continuous, deliberate, and collaborative. It is better understood as an exercise regimen than an operation.
Intelligent Economic Judgment
Revefi’s experience with the AI DBA highlighted a critical issue: an agent can solve a problem while spending more resources than the problem is worth. Economic judgment is not a feature; it is close to the definition of intelligence in an enterprise setting. On complex tasks such as query optimization, it observed its AI DBA firing more than a million tokens at a single problem. The output was thorough, not wrong, but lacked any sense of whether the problem deserved that level of compute. An agent without economic judgment will burn more than it saves, which is not intelligence but enthusiasm.
The remediation was to tie the work to the ROI of the problem. Before going deep, the agent asks whether the task is worth solving. Once wired into the loop, behavior shifted. It stopped treating every problem as a research project and began spending like it had a budget, because it now did. This maps directly onto a pattern the team has long argued about in data. At one customer, a query was running into six figures annually.
The correct response is not “kill it.” If that query drives ten million dollars of business, it is a bargain. If nobody can explain why it runs, then a hundred‑dollar query is waste. Cost is a fine place to start the conversation. It is a terrible place to end it. The industry has a blind spot that predates AI. Every benchmark (TPC‑H, TPC‑C, sorting) optimizes for speed. None measure return per dollar. A generation of systems and engineers was trained to equate thoroughness with quality. A good DBA knows better. A thorough answer that costs more than the problem is the wrong answer.
Enterprise Agent Foundations
Revefi’s AI DBA is designed to manage, optimize, and operate cloud data platforms autonomously. The capability to make contextual judgments that traditionally required an experienced DBA rests on two foundations, both essential and often misunderstood. The first is the agentic foundation: everything beneath the model.
Automatic model selection, prompt optimization, automated ROI on the work, observability, token economics, and governance form the basis of trustworthiness in production. Revefi built this layer before building the DBA, and it remains central.
The second is innate data expertise, which cannot be prompted in. A senior administrator carries knowledge of warehouse architectures, billing models across credits, DBUs, slots, nodes, and platform-specific constraints. Practices that are efficient on one platform may be disastrous on another. Row-by-row updates are reasonable on OLTP systems; on columnar cloud warehouses, the same update rewrites entire micro-partitions. Teams lift-and-shift habits, pass small tests, and then fail in production.
An agent without this embedded expertise is not a DBA. It is a confident intern with a corporate card.
Sanjay shares, “The AI DBA deploys a fleet of specialized sub-agents; each is a domain expert covering FinOps, observability, quality, spend optimization, performance tuning, and governance across each platform.”
Revefi runs controlled experiments before making changes and carries organizational memory: what it tried in each environment, what succeeded, what failed. Recommendations become increasingly specific over time rather than resetting with each session.
Operational Convergence
Revefi brings cost, performance, reliability, and quality into the same operational layer. Separating these dimensions has become increasingly inadequate as enterprises scale their data and AI infrastructure because they are not four problems; they are one event observed from four angles. A query that regresses is simultaneously a performance event, a cost event, an SLA event, and often a quality event. Split across four tools, the result is four root cause analyses that disagree, four owners, and a meeting. Unified, it becomes one finding with one explanation. The deeper reason is that optimizing any one dimension in isolation is actively dangerous. A driving analogy illustrates the point: a self-driving car that only accelerates is not self-driving. It must know when to brake.
Cost optimization without reliability is exactly that limited car. Shrinking a warehouse may technically save money while adding hours of operational pain: penny wise, pound foolish. The only way to make the trade‑off correctly is to hold cost, performance, and reliability in the same view, at the same time. Scale forces the issue. Enterprises cannot run this as four quarterly review meetings across hundreds of thousands of tables. It must be one continuous, automated layer; for the customers, that means hundreds of thousands of monitors running without anyone configuring thresholds.
Observability as Control Plane
AI agents introduce a different kind of operational complexity because they can make decisions, invoke tools, and generate costs autonomously. Observability must evolve from observing state to observing decisions, and then stop being a dashboard to become a control plane.
Classical observability answers, “What is the system doing?” For an agent, the questions are different: what did it decide, why, which model did it route to, what did that step cost, how long did the chain run, and was the work worth doing at all? That requires tracing the full path from user to agent to model to interaction, every tool call, every step of a multi-step workflow, with cost and latency attached at each stage.
The team learned the dependency lesson painfully. One night the AI DBA visibly slowed down. No deploy, no configuration change, nothing internal. The root cause was a provider-side latency jump of five to ten times on a single model. The immediate fix was almost insulting: “switch off the model.” But the implication was sobering: reliability was hostage to a component not owned, on terms not set, with latency invisible until customers felt it. The architectural fix was decisive: never depend on a model that cannot be observed in real time and swapped on the fly. The control‑plane point matters. A dashboard that reports what has already been paid is a receipt. When an agent can generate spend autonomously in seconds, policy must be enforced at runtime: caps, approvals, exceptions that can stop the burn while it is happening.
Dynamic Spend Control
Conventional FinOps was designed around relatively predictable infrastructure consumption, while agentic AI can generate highly variable token usage. Three fundamental changes are required in the way enterprises budget, forecast, and govern technology expenditure.
First, budget the unit of work, not the month. A monthly envelope assumes a stable run rate. Agentic workloads do not have one, as a single prompt change can move consumption by an order of magnitude. The durable unit is cost per task, per resolution, per agent interaction. Manage that, and the monthly number takes care of itself.
Second, enforce at runtime and not at reconciliation. Policies and spend caps must sit in the path of execution, with approvals and exceptions, so overspend is stopped rather than reported. Velocity is the problem. Revefi has seen a single team stand up one agentic workload over a warehouse and consume ten percent of an annual budget within days. Another customer onboarded a natural‑language interface over BigQuery and added seven figures of warehouse spend almost immediately because every optimization in that environment had been tuned for humans opening dashboards, and agents do not behave like humans.
Third, forecast distributions rather than averages. The average agent run is likely cheap and irrelevant. Risk lives entirely in the tail: the run that misses a join condition and scans billions of rows. Forecasting on means will be wrong in the only direction that matters.
He says, “We map to the FinOps frameworks and token-economics models enterprises already use, so the conversation starts with governance instead of evangelism.”
Token Economics Governance Layer
The organization provides attribution down to users, agents, models, and individual interactions. Granular cost attribution can become the foundation for governing enterprise AI and eventually influence which AI systems organizations choose to deploy. The team emphasizes that the second part, shaping which AI systems get deployed, is underappreciated. Governance, however, is the concrete starting point. At one Fortune 500 enterprise, a single user on a single AI use case incurred roughly seventy-six thousand dollars in unexpected token spend. The team surfaced it within minutes, and the customer stopped the burn immediately. That is only possible if attribution reaches the individual interaction.
In another example, a Fortune 200 enterprise with an aggressive AI spend of nine figures saw AI spend increase eightfold in under four months. It went across twenty-five thousand users and more than two hundred million requests a month. At that velocity, monthly reconciliation becomes archaeology. Within three weeks, Revefi identified seven-figure savings opportunities.
The second-order effect is strategic. Once cost can be attributed to an outcome, systems can be compared on a basis that matters: cost per resolved task, cost per successful workflow, not cost per million tokens. Today most enterprises select models on benchmark scores and list price. Neither predicts the bill, because the bill is a function of workflow attempts, context size, and retries. Procurement conversations will shift from “which model is best” to “which model delivers our unit economics,” and attribution is the only instrument that answers that question. There is a hygiene layer beneath this. Revefi detects missing organization, service, model, or agent metadata before incomplete telemetry quietly corrupts chargeback.
AI FinOps and AI Observability
Revefi developed AI visibility for its internal AI DBA prior to offering it to external customers. The company first applied the technology to its own operations. Running a fleet of agents in production quickly demonstrated that effective oversight depends on complete visibility. The team observed one agent consuming more than a million tokens on a single query issue and noted an unexpected overnight rise in a provider’s latency. In response they engineered a layer tracing every request from user through agent to model, attaching cost and latency to each step, and enabling immediate model swaps. Customers encountered the identical challenge scaled across thousands of agents and tens of thousands of employees. This internal layer evolved into Revefi AI FinOps and Observability, which the firm made generally available in June. The solution operates across OpenAI, Anthropic, Google Gemini, Amazon Bedrock and Amazon Nova, together with agent platforms such as OpenClaw, delivering enterprises a single unified view rather than separate dashboards for each vendor.
Sanjay says, “We were our own first customer. Before we asked any enterprise to trust our agents with its spend optimization, we had to answer the same questions about our own: what each step cost, whether it worked, and whether it was worth it.”
The Immediate CIO Value
Sanjay highlighted the tangible benefits a CIO receives within the initial weeks. The platform delivers three core outcomes. First, it provides transparent invoice attribution for every dollar of AI spend by provider, model, agent, and user, to the nearest cent, while detecting incomplete metadata that could distort chargeback. Second, you can control rather than reporting through hard or soft spending limits set as policies across all providers and enforced in real time via the client’s gateway or the firm’s own, with approvals and exceptions. Third, an actionable list of improvements covering the slowest and costliest prompts, duplicate calls removable by caching, and runaway agent processes. Insights appear across four perspectives: by outcome, by department, by waste, and by optimization. For a Fortune 200 client with annual AI outlays in the nine figures, the team surfaced seven-figure savings inside three weeks, matching the urgency AI requires as costs accumulate rather than wait for quarter-end.
He adds, “By the time the invoice arrives, the money is already gone. A CIO doesn’t need another report on what AI cost last month. They need the ability to decide what it is allowed to cost before the spend happens.”
Data and AI Unity
Sanjay emphasized that while most AI observability solutions originate from the application layer, a data-focused firm holds an advantage. AI and data expenditure have merged into a single budget. An agent does far more than consume tokens; it queries warehouses, activates pipelines and scans tables in patterns no human would typically pursue. Monitoring only the model captures half the total cost. The company has spent years demonstrating value at the data layer, enabling customers to reduce cloud data expenses by 30 to 70 percent. Verisk lowered its Snowflake costs by 60 percent within three months even as usage expanded 50 percent. AI performance depends entirely on the quality of underlying data. A model drawing from stale or corrupted information proves costly twice: once for the tokens and again for the flawed decision. The platform governs both sides in one place.Sanjay notes, “Every AI agent is also a data workload. You can’t govern one without the other, and enterprises that try will find the bill in the place they weren’t watching.”
Establishing Trust in Autonomous Systems
Trust is arguably the biggest barrier to autonomous enterprise systems. Revefi determines when an AI agent has enough context and confidence to act independently by drawing the line on semantics, not on confidence scores. It is a distinction that has held up better than any other approach. If the change provably preserves the outcome, the agent acts. Right-sizing a warehouse, auto-suspend policies, reclaiming idle compute, consolidating redundant jobs, converting warehouse generations, or a query rewrite where the result set is guaranteed identical all deliver the same business outcome, cheaper or faster. Those are platform problems, and they should be automated.
The moment a change alters what the business receives, it stops being a platform problem and becomes a consulting problem. Adding a filter, narrowing a date range, or dropping twenty years of history from a query may be arithmetically obvious, but the question is not technical. Why does the query exist? Who depends on that history? That requires a human in the loop, and Revefi does not expect that to change soon.
Sanjay adds, “We help build the mechanics of trust. The agent runs controlled experiments before it changes anything.”
The agent requests approval through Slack, Jira, or in‑product, raises pull requests, and reports what it did and what it saved. It remembers every action taken across the organization and whether it worked. ‘Integration is read-only and metadata-only by default, with changes made only through approved actions.
The proof is empirical. One Fortune 250 insurer, during evaluation, turned auto‑manage on across all seven hundred and eleven Snowflake warehouses partly to test the team’s capability. Spend fell roughly fifty percent in under forty‑eight hours with no human intervention. Trust is earned with reversibility and receipts, not with confidence.
Neutral Infrastructure Advantage
Revefi platform is engineered to operate seamlessly across all AI model providers, Agents and cloud data platforms such as Snowflake, Databricks and Google BigQuery. Infrastructure neutrality is critical in a market where enterprises increasingly navigate fragmented and rapidly evolving technology stacks. The importance lies in incentive alignment, not mere convenience. A platform-native optimizer is created by vendors whose revenue depends on consumption. Their engineers deliver impressive efficiency gains, but the incentive structure favors higher usage, not lower costs. The team is aligned with the opposite outcome, and customers can confirm that alignment directly in their billing.
Enterprises deploy multiple platforms, each with distinct strengths, to avoid lock-in. Optimization varies by platform: BigQuery involves slot allocation, Databricks requires jobs, clusters, and runtime configuration, while Snowflake focuses on warehouse sizing and multi-clustering. These judgments do not transfer across environments. At times, the optimal decision is to identify the workload and see where it exactly belongs. Only a neutral layer can surface that option. With open table formats gaining traction, storage converges, and the competitive question shifts to which engine should serve which workload.
The Revefi same principle applies to models. The organization runs several, evaluates stability across comparable tasks, and switches when price, latency, or predictability change. After observing latency from one provider increase ten-fold overnight, it determined that single-model dependency is an unacceptable risk one would not impose on customers.
Return on Investment
Many enterprises evaluate cloud optimization by the savings achieved. The more meaningful metric is the return generated from the data infrastructure itself. That return can indeed be measured. Savings serve as a useful opening metric but a poor closing one. Taken in isolation, they encourage the wrong instinct. Costs can always be reduced by doing less, but that approach erodes trust in the data. Return is measurable, though it requires discipline. The organization scrutinizes four dimensions:
1. Unit economics:
cost per pipeline, dashboard, resolved task, or agent interaction. This figure reveals whether efficiency is improving or simply shrinking.
2. Consumption ratio:
the share of the estate actively read. Since unused tables rarely stop refreshing, the unused fraction grows. Measuring it makes ROI tangible.
3. Time returned:
hours redirected from firefighting to building. Revefi observes roughly a tenfold improvement in operational efficiency, with reclaimed capacity carrying a market rate.
4. Trust:
quality escalations, SLA adherence, and incident count. Data without trust delivers zero return regardless of cost.
The proof lies in customer outcomes. One client reduced warehouse spend by thirty percent while data adoption rose thirty‑five percent, with zero quality escalations. Spend decreased, usage increased, trust remained intact; that is return, not savings. Another expected five or six percent savings but achieved twenty‑seven. The difference was not a discount; it was capacity redeployed into AI initiatives previously unfunded.
Telemetry-driven Integration
The Revefi organization is effectively uniting three conventionally separate disciplines: data operations, financial governance, and AI operations. This convergence is already occurring in practice, several years ahead of organizational charts. The shift is driven by telemetry. A single workload execution simultaneously represents an operations fact, a financial fact, and increasingly an AI fact, as agents trigger activity. Once signals are inseparable, maintaining three teams with three tools and vocabularies produces argument rather than insight.
The market reflects this movement. The role itself is converging. Classical skills remain vital: query optimization, schema design, execution plan analysis, and understanding storage and compute trade‑offs form the foundation.
Revefi Ceo says, “Two or three years ago, nobody discussed spend, performance, quality, and observability as a single bucket; we were arguing for it. Today it is closer to the norm than the exception and is one reason Gartner named us a Cool Vendor.”
Yet they are no longer sufficient alone. Practitioners who thrive are fluent in the business and cost dimensions of infrastructure decisions and adept at directing AI agents rather than executing every change manually. The transition is from doer to director, elevating rather than diminishing the role. If named, the unified discipline would be accountability for the unit economics of intelligence: cost, correctness, and outcome, governed together.
Human-Centered Autonomy
The Revefi team frames the future of autonomous agents in enterprise data and AI infrastructure around a clear distinction: machines may execute, but humans decide what matters. Agents can identify the cheapest way to run a query. Still, they cannot decide whether that query should exist, whether decades of historical scans reflect genuine business needs, or whether assumptions have gone unchallenged since the original engineer departed. Linking technical artifacts to business goals requires human judgment, stakeholder engagement, and accountability. Agents can retain context; only people can carry consequences.
Sanjay says ceo of Revefi , “The environment I expect within a few years looks like this. Routine operations run themselves, such as tuning, right-sizing, reclamation, incident detection, and remediation happening continuously, most of it never reaching a human at all. The 2 am page becomes rare rather than routine. A single administrator governs four hundred databases instead of forty, because the ceiling was never their capability; it was their hours. Every dollar is attributed to a workload, a team, and eventually a business outcome. Every autonomous action is logged, reversible, and auditable.”
Sanjay mentions at Revefi teams will focus the majority of their day on architecture, strategy, and decisions that advance the business. Such an environment is worth building: not a replacement for data teams, but a restoration of their intended role. Revefi emphasizes a guiding principle in every design choice. The best AI reflects and extends human judgment, rather than forcing humans to conform to machines.
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