AI/LLM Resource Tracking

simplify tco & finops for ai, gpu & llm amberflo helps enterprises track, optimize, and save on total cost of ownership (tco) of their enterprise it footprint, including ai, llm and cloud costs our modern finops for ai platform delivers real time llm usage and cost visibility, automated governance, and built in cost allocation and chargebacks—turning ai roll out and cost management into a driver of efficiency, value and savings what is amberflo ai gateway? integrated llm observability and finops unit costs, access, governance, chargebacks, savings, and more amberflo ai gateway is an llm gateway that streamlines access to 100+ large language models (llms) and distilled llms deployed in private clouds or vpcs, through a single, secure, and unified interface all interactions use the familiar openai api format, eliminating the complexity of managing multiple provider apis, authentication methods, and response formats whether you are using a single llm provider such as amazon bedrock, or multiple ones, amberflo ai gateway delivers centralized access, governance, and control with real time usage, costs allocation, attribution and per unit analytics how is amberflo ai gateway different? amberflo ai gateway is part of the amberflo enterprise finops for ai platform—a modular suite of services designed to work independently or as an integrated whole, depending on your organization’s current and future needs unlike other gateways that primarily function only as api traffic analyzers, amberflo ai gateway comes built in with a full set of total cost of ownership (tco) and finops capabilities, to serve as a comprehensive, one stop, integrated finops for ai platform amberflo finops for ai platform delivers a full featured governance and a cost management engine for ai and llm workloads below are some of the core capabilities out of the box of amberflo finops for ai i) ai gateway centralized access with one standard api for all llms (public cloud, direct, and on prem distilled) manage access by department, application, or user, and track usage and costs in real time across models, versions, providers, and locations ii) ai metering automatic llm usage aggregation by custom attributes built into the amberflo ai gateway is amberflo ai metering which captures and aggregates requests and responses (input and output tokens) in real time and at scale, across all llms, with attribution such as department, application, user, model name, version, and more real time insights are available via built in analytics dashboards or via api for seamless 3rd party integration iii) llm cost and custom rates by app, dept, teams, etc amberflo’s native rating engine applies either published list prices or fully configurable custom rates by business unit or other entities costs are calculated instantly as usage flows through the gateway iv) cost guards and budget tracking set granular rules and thresholds by model, application, or version to keep usage and costs under control alerts trigger in real time with notifications sent via email or webhook v) showbacks and chargebacks amberflo’s billing engine sits on top of the rating layer, delivering advanced chargeback and billing configurations including budgets, commitments, free tiers, overages, and custom pricing plans results can be presented via dashboards, a built in billing portal, or apis with full invoice manifests vi) workload planning and sizing plan new initiatives or optimize existing workloads using a built in cpq style tool access a catalog of all llm models, versions, and public price points, with support for custom or negotiated rates generate detailed manifests of services, rates, and projected usage to guide budgeting and forecasting key features universal model access 100+ model support access models from openai, anthropic, aws bedrock, google vertexai, azure, cohere, hugging face, replicate, groq, and more distilled, private cloud model support unify public and private cloud llm models finops, governance, and control unified interface write code once and run it across any supported model provider consistent format all responses delivered in standardized openai format advanced usage and cost management automatic spend tracking real time cost monitoring across all providers and models budget controls set spending limits per project, team, api key, or individual user custom pricing configure your own pricing models for accurate cost attribution usage analytics detailed reporting on tokens, requests, and costs by user, team, or project enterprise grade reliability load balancing distribute requests across multiple providers automatically fallback support seamlessly switch to backup providers when primary services fail rate limiting control request rates to prevent service overload health monitoring real time status checks for all connected model providers security & access control virtual keys create and manage api keys without exposing provider credentials team based access control which models and features each team can access custom authentication integrate with your existing identity management systems audit trails complete logging of all api requests and responses comprehensive observability multi platform logging send logs to s3, gcs, langfuse, datadog, and more prometheus metrics built in metrics collection for monitoring and alerting real time dashboard web based ui for monitoring usage, costs, and performance custom callbacks integrate with your existing monitoring and analytics tools deployed in your vpc or on prem data privacy complete control over data privacy and security 100% secure deploy in your own cloud or on prem infrastructure full tenant isolation no data mingling business benefits accelerate development scale existing models or deploy new models within a day of their release eliminate months of integration work across different providers focus on business logic instead of api management complexity reduce operational complexity standardize logging, authentication, and api formats across all models centralize model access without distributing individual provider api keys simplify troubleshooting with unified error handling and monitoring optimize costs track granular usage data by user, project, and model set proactive budget controls to prevent overspend compare costs across providers to optimize your model selection scale with confidence handle variable workloads with automatic load balancing and fallbacks a/b test different models easily without code changes build robust llm workflows with built in retry and error management use cases public cloud and frontier llms and distilled llms in private cloud unified and complete finops and governance platform teams provide standardized llm access to development teams without managing individual provider relationships ai applications build chatbots, content generators, and ai assistants that can seamlessly switch between models cost optimization monitor and control ai spending across large organizations with detailed usage analytics multi model workflows create applications that leverage different models for different tasks while maintaining consistent interfaces