Real-time visibility and automated control over
Companies spending tens of thousands a month on LLM APIs often cannot say which teams or features drive the cost. Igris gives finance real-time spend visibility broken down by team, model, and connection, plus automated budget enforcement that stops overruns before they land on the invoice.
Real Time Cost Dashboard
The CFO asks how much the company is spending on AI this month and where the money is going. You do not have a clean answer. Costs are split across OpenAI, Anthropic, and Google dashboards, each with different billing cycles, different export formats, and different ways of slicing the data. Getting a confident answer means downloading CSVs, reconciling numbers in a spreadsheet, and hoping nothing gets missed. By the time you have something presentable, the data is already a day old. AI cost management without a unified view is not cost management, it is estimation.
Igris gives you a single dashboard with total AI spend for the last 24 hours, broken down by provider, by model, and by connection, so you know exactly which team, project, or feature is spending the most. Trend data in 12 hour buckets reveals usage patterns and growth rates across every connection. Latency per connection lets you weigh cost against performance without pulling from a separate monitoring tool.
The CFO's question gets answered from one screen, in real time, with a breakdown that goes all the way down to the team and feature level. No spreadsheet. No manual reconciliation. No day old numbers delivered with a caveat.
Budget Caps and Automated Spend Control
You have visibility into AI spend now. You can watch costs climb in real time. But visibility alone does not stop a project from blowing its budget, it just means you notice sooner. When a connection starts spending faster than expected, someone still has to manually intervene: find the right team, find the right contact, ask them to reduce usage, and hope they respond quickly enough. By the time anything actually changes, the overrun is already done and the explanation to leadership is already awkward.
Igris turns budgets into automated controls. Configure thresholds, alert when any single call costs more than $1.00, or when a session spends more than $5.00 within a minute, and choose the response: alert, throttle, or hard block. Token burn detection adds another layer by flagging unusual output volume before it compounds. Rate limits cap maximum possible spend per minute regardless of what else is running. Every alert reaches the right people in real time through Slack or Discord webhooks.
Budget enforcement stops being a manual process that depends on someone responding to a message fast enough. The threshold fires, the action runs, the spend stops, automatically, before the overrun becomes a line item that needs explaining.
Cost Attribution for Client Billing
You manage AI workloads for multiple clients, accounts, or service lines. Billing is based on estimates, flat rates, or broad usage tiers, because getting precise per client numbers means manually correlating API logs across providers, matching timestamps to client records, and still not being fully confident the figures are accurate. Some clients are almost certainly undercharged. Some costs are being absorbed internally without anyone flagging it. And if a client ever disputes an invoice, there is nothing clean to point to.
Give each client, account, or project its own connection in Igris. Every connection tracks tokens in, tokens out, cost, model, and provider for every single call. Query cost by connection and date range through the audit events API and export the data directly into your billing system for accurate invoicing, whether that is a SaaS platform, an agency billing tool, or a custom financial workflow. Anomaly detection on each connection also flags when a client's usage suddenly multiplies, long before it becomes a billing dispute or an unexpected cost absorption.
Every invoice reflects actual usage, down to the model and token level. Disputes have a clean audit trail behind them. Clients who were previously undercharged start paying for what they actually consumed, and you have the data to prove the number is right.
Model Cost Optimization
You suspect teams are defaulting to the most powerful, and most expensive, model for workloads that do not need it. But suspicion is not a conversation you can have with engineering. Without data showing which calls went to which model, what they cost, and what they were actually doing, the discussion stays vague and nothing changes. You go back to watching the bill grow, knowing a portion of it is avoidable but unable to prove how much.
The cost by model breakdown in Igris shows exactly what each model is costing across every connection. Model shift detection alerts you when traffic drifts toward a more expensive option. Compare cost per token across providers and steer non critical workloads toward cheaper models through allowed model lists, a configuration enforced at the gateway level, without asking every individual developer to change their defaults. The dashboard shows precisely what last month's premium model calls would have cost on the lightweight version instead.
The conversation with engineering stops being vague and starts being specific: here is the model, here is the call volume, here is the exact dollar delta if these workloads shift. That is a conversation that ends with a decision, not another cycle of watching the bill and hoping behavior changes on its own.
Runaway Agent Prevention
An AI agent caught in a loop does not slow down, it accelerates. It can make tens of thousands of LLM calls before a human notices anything is wrong. The first signal is usually the provider invoice: a line item that is ten times what it should be, with no easy way to reconstruct exactly what happened or when it started. By then the spend is done, the quarter is affected, and the explanation to the CFO is uncomfortable, because there was no runaway agent prevention control in place to catch it earlier.
Igris stacks four layers of protection on every connection simultaneously. Cost spike detection fires within seconds when session spend crosses a threshold. Token burn detection fires when output volume explodes within a short window. Error rate monitoring flags an agent that is mostly failing, which almost always signals a loop. Rate limiting puts a hard ceiling on requests per minute even if none of the other three alarms have fired yet. Together, these layers catch and stop the runaway agent before real financial damage is done.
Runaway AI agent spend gets stopped in seconds, not discovered at the end of the billing cycle. The uncomfortable explanation to leadership does not happen, because there is nothing unusual on the invoice to explain.
Forecasting and Trend Analysis
AI spend forecasting today is last month's invoice plus a gut feeling about how fast usage is growing. You know usage patterns vary by team, by feature, and by time of day, but none of that granularity is visible from a provider invoice. The result is a forecast that leadership accepts because there is nothing better, not because it is accurate. When actual spend comes in higher than projected, the explanation is always the same: AI usage is hard to predict. At some point that stops being an acceptable answer.
Igris provides trend data in 12 hour buckets for every connection, revealing peak hours, growth rates, and which teams or features are scaling fastest. Request count trends can be correlated with business metrics: more users mean more AI calls mean higher cost, and that relationship is now visible and quantifiable. Provider and model breakdowns show which cost centers are accelerating. Export everything through the API into your financial planning tools. Latency trend data adds an early warning layer, rising latency often signals approaching rate limits, which matters for both capacity planning and cost projections.
AI spend forecasts are built on actual usage patterns, not assumptions. You can show leadership a trend line with specific drivers behind it, not just a number with a margin of error attached. When the quarter comes in close to forecast, that is credibility that compounds over time.
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