Most enterprises can report what AI cost them. Far fewer can say which team owns it, whether it was approved, or what it returned.
FinOpsly closes that gap. The platform governs AI spend on the same cost model that carries the cloud, data platform and SaaS an AI workload consumes, so a business unit sees the full cost of an AI initiative instead of four disconnected bills.
Capabilities include:
Cost estimation before deployment. Model an architecture and get a priced workload across model APIs, GPU capacity, warehouse consumption and storage, with the assumptions on screen. Weigh model choices against consumption you have actually measured.
Attribution that holds up in a chargeback cycle. Spend resolves to owners, teams, applications, business units and customers through hierarchies nine or more levels deep. Tagging is standardized across providers, keys and resources are labeled in bulk from plain-language rules, and whatever remains unattributed is published as a number, not absorbed.
Guardrails that act. Set budgets by project, team or API key. Catch anomalies with root cause and route them to whoever owns the resource. Surface waste that provider tooling misses, using FinOpsly's own detection models. Plan commitments across AWS, Azure and Google Cloud. Park idle compute on approved schedules, reversibly.
Financial results you can defend. Automated chargeback in a single cycle. Savings measured as what reached run-rate against a no-action baseline. Unit economics down to cost per call, per active user and per customer served.
For technology and finance leaders accountable for what AI spend returns.
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LM-Kit.NET is an enterprise-grade toolkit designed for seamlessly integrating generative AI into your .NET applications, fully supporting Windows, Linux, and macOS. Empower your C# and VB.NET projects with a flexible platform that simplifies the creation and orchestration of dynamic AI agents.
Leverage efficient Small Language Models for on‑device inference, reducing computational load, minimizing latency, and enhancing security by processing data locally. Experience the power of Retrieval‑Augmented Generation (RAG) to boost accuracy and relevance, while advanced AI agents simplify complex workflows and accelerate development.
Native SDKs ensure smooth integration and high performance across diverse platforms. With robust support for custom AI agent development and multi‑agent orchestration, LM‑Kit.NET streamlines prototyping, deployment, and scalability—enabling you to build smarter, faster, and more secure solutions trusted by professionals worldwide.
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Cheaper Inference
Cheaper Inference serves as an API gateway compatible with OpenAI, enabling users to access various AI models from different providers through a unified API key, thus eliminating the need for any changes in request formatting. Developers have the flexibility to switch providers simply by updating the base URL and API key while retaining the same model, messages, tools, streaming configurations, and response management. This service accommodates both text and image models, facilitates vision-enabled chat requests, offers streaming capabilities, includes prompt caching, provides reasoning controls, and allows temporary image uploads for more extensive vision data. Each request can have its model selected individually, and users can filter the catalog based on model type, vision capabilities, reasoning options, streaming availability, or provider identity. The system includes automatic retries to manage network disruptions and provider errors, with fallback routes available for eligible requests to prevent failures. Additionally, every request is documented in the History section, allowing teams to track request volume, token consumption, and overall operational activity, ensuring comprehensive oversight and management of AI interactions. This transparency assists in optimizing usage and understanding patterns over time.
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LLMetrics
LLMetrics serves as a comprehensive cost tracking solution for teams involved in the development of AI products, integrating model expenses, token consumption, feature attribution, and usage notifications into a single, interactive dashboard. This powerful tool accommodates over 100 models from various providers, including OpenAI, Anthropic, Google Gemini, Mistral, Cohere, Together AI, and Groq, with pricing information updated on a daily basis. Teams can label each model interaction with details such as feature name, provider, model type, input tokens, and output tokens, enabling them to pinpoint which specific functionalities—be it a chatbot, summarizer, search tool, or lesson creator—are contributing to their expenditures. The platform offers real-time updates and daily trend visualizations, illustrating how costs fluctuate in response to software releases, modifications to prompts, increases in traffic, or transitions between models. Additionally, it includes spend thresholds and spike-detection features that can alert teams via email or Slack when unusual usage patterns are identified, aiding them in preventing runaway loops and unforeseen cost surges prior to receiving the provider invoice. By leveraging these insights, teams can make informed decisions regarding their AI product strategies and budget management.
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