FinOps LLM
FinOps LLM is an AI cost management and LLM observability platform for engineering teams running production GenAI. It makes token spend visible across OpenAI, Anthropic, Amazon Bedrock, Google Gemini, Azure, Groq, and other providers and reconciles internal usage data against provider invoices. Token-level costs can be filtered by provider, model, feature, team, customer, environment, and custom dimensions, giving every dollar a clear owner. Attribution and chargeback tools map usage to product surfaces and customer cohorts, support showback, and export data to NetSuite, QuickBooks, CSV, or APIs. Real-time anomaly detection monitors spend, latency, and quality against rolling feature baselines, sending alerts through Slack, PagerDuty, email, or webhooks when behavior changes. Optional budget enforcement and auto-throttling can stop runaway agents, retries, or model shifts before they become expensive.
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ZenLLM
ZenLLM is an AI cost optimization platform for engineering teams running LLM applications in production. It connects provider invoices to the application behavior behind them, showing which prompts, workflows, models, customers, retries, and request paths are driving spend. Teams send request-level telemetry through the ZenLLM SDK and can attach business context such as workflow, owner, customer, team, or product feature without storing prompt or response content. It monitors token usage, model selection, latency, errors, retries, and cost, then surfaces the waste patterns hidden by aggregate provider dashboards. It detects context accumulation when conversations or agents resend growing histories, premium-model overuse on low-risk work, retry loops that repeat expensive context, stale system prompts, routing mistakes, anomalies, and weak cost ownership.
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LLMetrics
LLMetrics is LLM cost tracking software for teams shipping AI products, bringing model spend, token usage, feature attribution, and usage alerts into one live dashboard. It supports more than 100 models across OpenAI, Anthropic, Google Gemini, Mistral, Cohere, Together AI, Groq, and other providers, with pricing data synchronized daily. Teams tag each model call with a feature name, provider, model, input tokens, and output tokens, allowing them to see exactly whether a chatbot, summarizer, search feature, lesson generator, or other workflow is driving spend. Real-time updates and daily trend charts reveal how costs change after releases, prompt edits, traffic growth, or model swaps. Spend thresholds and spike-detection rules can alert teams through email or Slack when usage patterns look wrong, helping them catch runaway loops and unexpected cost increases before the provider invoice arrives.
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Mavvrik
Mavvrik is an AI and hybrid infrastructure cost management platform that gives finance, FinOps, IT, and engineering teams one control center for GenAI, autonomous agents, GPUs, cloud, on-premises systems, Kubernetes, data platforms, and SaaS. It unifies cost, usage, and telemetry signals from AWS, Azure, Google Cloud, Oracle, VMware, NVIDIA, OpenAI, Anthropic, Gemini, Snowflake, Databricks, and LiteLLM, creating a single source of truth across the technology stack. Teams can track every model call, agent interaction, GPU hour, workload, service, and resource, then allocate spending by customer, product, feature, project, application, environment, team, or cost center. Cost-to-serve and unit-economics analysis reveal margin drains, expensive workloads, and the true cost of delivering each offering. Real-time anomaly detection and alerts identify usage before it becomes a budget surprise, while predictive forecasting helps organizations model cloud, GPU, and AI expenses.
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