LLM Gateway Verified Customer Reviews (2026)

Ask a CFO what the company spent on AI last quarter and you will get a number. Ask which product line it belonged to, whether anyone approved it, or what it earned, and the room goes quiet. FinOpsly was built for that second set of questions. It is an AI Cost Governance platform. AI does not run in isolation, so FinOpsly does not price it in isolation either. A model call pulls warehouse queries, GPU time and storage behind it, and the engineers building the feature are burning licensed seats the whole time. All of that lands in one cost model, mapped to the company's own structure: owner, team, product, business unit, customer. What teams use it for: Pricing a workload before anyone provisions anything. Describe the architecture, get a cost estimate across the stack, and see which assumptions drove it. Compare model options using consumption you have already paid for. Making chargeback something finance trusts. Hierarchies run nine levels or deeper. Tags get standardized across providers that never agreed on a convention. API keys and resources are labeled in bulk from instructions written in ordinary English. Anything still unowned shows up as a dollar figure. Holding the line during the month. Budgets by team, project or key. Anomalies flagged with a root cause and sent to the person responsible. Waste that provider consoles do not catch, found by FinOpsly's own detection models. Idle compute parked on schedules the customer approved, and reversible. Proving the outcome. One chargeback run covering AI, cloud, data and SaaS together. Savings measured against the base-line along with cost-to-serve metrics: cost per active user, per customer served. Customers have moved attributable spend from 68% to 99% inside 90 days and taken a chargeback cycle from 12.4 days down to under one. Built for CIOs, CTOs, FinOps practitioners and the finance teams who sign off on the bill.

Gemini Enterprise Agent Platform is an advanced AI infrastructure from Google Cloud that enables organizations to build and manage intelligent agents at scale. As the evolution of Vertex AI, it consolidates model development, agent creation, and deployment into a unified platform. The system provides access to a diverse library of over 200 AI models, including cutting-edge Gemini models and leading third-party solutions. It supports both low-code and full-code development, giving teams flexibility in how they design and deploy agents. With capabilities like Agent Runtime, organizations can run high-performance agents that handle long-duration tasks and complex workflows. The Memory Bank feature allows agents to retain long-term context, improving personalization and decision-making. Security is a core focus, with tools like Agent Identity, Registry, and Gateway ensuring compliance, traceability, and controlled access. The platform also integrates seamlessly with enterprise systems, enabling agents to connect with data sources, applications, and operational tools. Real-time monitoring and observability features provide visibility into agent reasoning and execution. Simulation and evaluation tools allow teams to test and refine agents before and after deployment. Automated optimization further enhances agent performance by identifying issues and suggesting improvements. The platform supports multi-agent orchestration, enabling agents to collaborate and complete complex tasks efficiently. Overall, it transforms AI from a productivity tool into a fully autonomous operational capability for modern enterprises.

TrustedRouter acts as an AI gateway focused on privacy, allowing developers to access over 600 AI models from more than 90 providers using a single API that aligns with OpenAI standards. It guarantees privacy by directing requests through a validated gateway that does not log any prompt or output data, thereby ensuring a distinct separation between the production prompt pathway and the management dashboard, which means even the engineers are barred from accessing user requests. Developers can effortlessly continue utilizing the OpenAI SDK by simply updating one base URL, while also having the option to choose between direct model identifiers or routing aliases, which support smooth transitions between providers, implement zero-retention policies, ensure secure processing, emphasize EU-centric routing, and allow for multi-model synthesis. Additionally, the system includes features such as provider failover, regional routing, and continuous model health monitoring to safeguard against service interruptions from any single upstream failure. TrustedRouter runs on major cloud platforms like GCP, AWS, and Azure, and it additionally offers metrics related to latency, availability, source code, deployment infrastructure, SDKs, and trust verification for comprehensive evaluation, thereby enhancing transparency and reliability in its offerings. This dedication to openness and security fosters confidence among developers who place a high value on privacy within their applications, ultimately leading to a more robust ecosystem of trusted AI solutions. As a result, TrustedRouter not only meets the technical needs of developers but also aligns with their ethical standards regarding data privacy.