Overview#
AI inference costs scale with usage in ways that catch organisations off guard. A new product feature, a marketing campaign, or simply more analysts using AI assistance can triple monthly spend in a quarter. The AI Cost Optimisation and Analytics platform provides the visibility and control to prevent that from becoming a budget crisis, tracking every token consumed, attributing costs to teams and projects, and surfacing recommendations that reduce spend without degrading output quality.
Purpose-built for enterprise AI operations, FinOps teams, and technology leadership, this system transforms AI spending data into actionable intelligence through real-time analytics, predictive forecasting, and concrete recommendations.
Key Features#
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Real-Time Cost Tracking: Granular visibility into AI spending across all providers, models, users, departments, and projects with sub-second latency. Multi-provider integration tracks costs across major AI platforms at individual request, user, department, project, and application levels with 24-month detailed retention.
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Budget Management and Alerting: Multi-level budget trees with inheritance and allocation rules at organisation, department, project, and user levels. Real-time monitoring, configurable threshold alerts, automated throttling, and forecasting prevent cost overruns. Graduated controls range from soft warnings to hard limits with grace periods.
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Optimisation Recommendations Engine: AI-powered analysis of usage patterns identifies actionable recommendations across model selection, prompt engineering, caching strategies, provider selection, and architectural improvements. Built-in A/B testing validates recommendations before full deployment. One-click implementation with rollback capabilities.
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Usage Analytics and Insights: Deep visibility into AI resource consumption patterns across users, applications, features, and time periods. Behavioural analytics, cohort analysis, feature attribution, anomaly detection, and cost-to-value correlation inform product and investment decisions.
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Cost Forecasting and Predictive Analytics: Machine learning models predict future AI spending up to 90 days in advance with confidence intervals. Scenario modelling supports what-if analysis for business changes. Budget runway analysis calculates time-to-limit and required intervention dates.
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Financial System Integration: Chargeback automation, invoice reconciliation, cost centre mapping, and multi-currency support integrate with ERP and accounting systems for financial compliance.
Use Cases#
Enterprise AI Operations#
Gain visibility into AI spending across departments and projects, implement hierarchical budgets with automated controls, and identify optimisation opportunities worth significant savings through model substitution and caching strategies.
Healthcare AI Deployments#
Manage AI spending across clinical departments with no centralised visibility. Hierarchical budgets, department chargebacks, and compliance controls eliminate budget overruns while ensuring AI resource allocation aligns with clinical priorities.
FinTech SaaS Platforms#
Control AI costs growing with user adoption while maintaining unit economics. Optimisation recommendations enable user growth without proportional cost increases through intelligent model routing and response caching.
Critical Infrastructure Operators#
Organisations running 24/7 AI-assisted monitoring across energy, water, or telecommunications infrastructure use predictive forecasting and automated scaling controls to prevent unexpected cost spikes during incident-response surge periods.
Integration#
Programmable API access is available for cost analytics queries, budget management, optimisation recommendations, usage analytics, cost forecasting, and cost comparison. Real-time subscriptions deliver cost updates, budget alerts, and optimisation discoveries. Supports 15+ AI providers including major cloud AI platforms, with tracking via direct API integration, usage proxy, log analysis, or billing import.
Open Standards#
- GraphQL (June 2018 specification): All cost analytics queries, budget management mutations, usage reporting, and optimisation recommendation retrieval are exposed through a GraphQL API, enabling strongly typed, client-driven queries against the cost and usage data model.
- OAuth 2.0 (RFC 6749): Authorisation flows for third-party integrations and the programmable API use OAuth 2.0, allowing delegated access to cost analytics and budget management endpoints without sharing credentials.
- OpenID Connect 1.0 (OIDC): SSO federation for enterprise identity providers is implemented over OIDC, enabling single sign-on into the analytics dashboards and role-based access control provisioning from corporate identity stores.
- SAML 2.0 (OASIS): Enterprise SSO integration additionally supports SAML 2.0 identity assertions, permitting organisations whose providers do not support OIDC to federate authentication into the cost analytics portal.
- OpenAPI 3.x: The REST management API surface is described using an OpenAPI specification, enabling machine-readable documentation, client SDK generation, and automated integration testing for cost queries and budget operations.
- ISO 4217: Three-letter currency codes from ISO 4217 are used throughout the financial reporting layer, multi-currency cost tracking, and chargeback exports to ensure interoperability with ERP and accounting system integrations.
- OpenTelemetry (CNCF): Distributed tracing and metrics for AI inference request pipelines are instrumented using the OpenTelemetry SDK and OTLP export protocol, providing the per-request latency and token-consumption signals that feed cost attribution analytics.
Security & Compliance#
SOC 2 Type II certified infrastructure. GDPR and CCPA compliant with data residency options. Enterprise-grade encryption for cost and usage data with field-level encryption for sensitive financial information. Role-based access control with 12 predefined roles. Complete audit logging. SSO integration with SAML 2.0, OAuth 2.0, and OpenID Connect. ISO 27001, HIPAA, and PCI DSS Level 1 compliant.
Last Reviewed: 2026-02-23 Last Updated: 2026-04-14