See what everyAI system costs,and whether it delivers value.
Track AI spend by project, team, model, and user. Set hard limits, catch cost anomalies, and connect every dollar to performance and business value.
See cost controls in action
AI spend becomes harder to control as it scales.
A prototype may make a few model calls. A production application can make thousands across users, agents, tools, and workflows. Costs grow across providers and business units, while finance has no consistent way to attribute spend or understand what is driving it.
By the time usage appears on a provider invoice, the money has already been spent. Without budgets and controls at the system level, teams can identify overruns but cannot prevent them.
EU AI Act
ISO/IEC 42001
NIST AI RMF
OSFI E-23
3 to 1
evaluation, observability, and governance tools consolidated into a single contract.
2 weeks
to see a reduction in token spend after budget caps and alerts go live
100%
of AI spend tracked at the project level
AI spend is growing without clear accountability.
Provider dashboards show aggregate token usage, but they do not connect spend to the project, team, owner, performance, or business outcome responsible for it. Finance can see the invoice without knowing what created it, whether the system is performing, or which investments warrant more budget.
No line of sight into AI spend.
Usage accumulates across providers, projects, teams, and business units without consistent ownership or allocation. Finance sees the total only after the spend has occurred.
Spend is disconnected from performance.
Provider dashboards show token usage and model costs, but not whether that spend produced accurate results, completed more tasks, or improved the customer experience.
Underperforming projects keep consuming budget.
AI systems can continue accumulating costs even when adoption, quality, or business performance no longer justifies the spend.
Cap and control costs in the same place where AI runs.
Openlayer connects cost to the same requests, traces, evaluations, and AI systems it already monitors. Teams can see what is driving spend, set budgets at the project or model level, and respond before overruns appear on the next provider invoice.
Finance gets real-time allocation and portfolio visibility. Engineering gets cost controls that operate alongside quality and performance requirements.
Everything you need to control AI spend
01
Token and cost tracking
Track tokens, requests, and costs across every connected model provider, with minimum, median, maximum, and per-model breakdowns.
02
Automated cost controls
Detect cost anomalies, enforce spend thresholds, and trigger alerts or actions when usage exceeds approved limits.
03
Cost by project, team, and org
Allocate spend to the projects, teams, owners, and business units responsible for it, giving finance a complete portfolio-level view.
04
Business-case alerts
Alert owners when project spend exceeds approved budgets, growth assumptions, or expected usage thresholds.
05
Performance and cost thresholds
Set acceptable thresholds for cost, latency, quality, and task performance. Identify systems where higher spend is not producing better results.
06
Finance dashboards
Give finance role-specific visibility into AI spend, trends, budgets, forecasts, and performance without requiring access to engineering tools.
“For the first time, we can see which AI systems are driving spend, who owns them, and whether the cost is justified.”
CFO - Telecom
Trusted by regulated leaders: Sun Life and Gallagher (insurance); Rogers, KPN, and Comcast (telecom and media).
Backed by Y Combinator and Race Capital. SOC 2 Type II.
Founded by ex-Apple/Siri ML engineers.
Jericho Security: 6x deployment frequency and +53% throughput after standardizing on Openlayer.
Named in the 2026 Gartner Market Guide for AI Evaluation and Observability Platforms.
Endorsed by Guillermo Rauch (Vercel CEO) and Max Mullen (Instacart founder).
