ZenLLM Description
ZenLLM serves as an AI-driven platform focused on optimizing costs for engineering teams that deploy LLM applications in live environments. By linking provider invoices to the underlying application activities, it identifies which specific prompts, workflows, models, customers, retries, and request paths contribute to financial expenditures. Teams can utilize the ZenLLM SDK to transmit request-level telemetry, allowing them to incorporate relevant business context—such as workflow, owner, customer, team, or product feature—without having to store the content of prompts or responses. In addition, it keeps track of token consumption, model selection, latency, errors, retries, and overall costs, revealing wasteful patterns that provider dashboards often obscure. The platform is capable of recognizing instances of context accumulation when conversations or agents repeatedly send extended histories, excessive use of premium models for low-risk tasks, retry loops that lead to unnecessary expenses, outdated system prompts, routing errors, anomalies, and a lack of accountability regarding costs. Furthermore, ZenLLM empowers teams to make informed decisions that can significantly enhance cost efficiency in their LLM application operations.
ZenLLM Alternatives
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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Around 25 million engineers work across dozens of distinct functions. Engineers are using New Relic as every company is becoming a software company to gather real-time insight and trending data on the performance of their software. This allows them to be more resilient and provide exceptional customer experiences. New Relic is the only platform that offers an all-in one solution. New Relic offers customers a secure cloud for all metrics and events, powerful full-stack analytics tools, and simple, transparent pricing based on usage. New Relic also has curated the largest open source ecosystem in the industry, making it simple for engineers to get started using observability.
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FinOps LLM
FinOps LLM serves as an advanced platform for AI cost management and observability, specifically designed for engineering teams utilizing production GenAI. It enables transparency in token expenditures across a variety of providers such as OpenAI, Anthropic, Amazon Bedrock, Google Gemini, Azure, and Groq, while also aligning internal usage data with invoices from these providers. Users can filter token-level expenses based on provider, model, feature, team, customer, environment, and other custom metrics, ensuring that each dollar spent has a designated owner. Additionally, the platform includes attribution and chargeback functionalities that correlate usage with product interfaces and customer demographics, facilitating showback processes and allowing for data exports to systems like NetSuite, QuickBooks, CSV, or through APIs. Furthermore, real-time anomaly detection features track spending, latency, and quality, comparing them against dynamic feature baselines, and issue alerts via Slack, PagerDuty, email, or webhooks whenever notable changes occur. To further enhance cost control, optional budget enforcement and auto-throttling measures can prevent excessive spending due to runaway agents, excessive retries, or unexpected model shifts. This comprehensive approach ensures that engineering teams can manage their AI resources effectively while maintaining financial oversight.
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AI Cost Board
AI Cost Board serves as a comprehensive platform for monitoring AI API usage and managing associated costs, consolidating important metrics like expenses, requests, tokens, latency, errors, and overall usage from various model providers into a unified real-time dashboard. By directing LLM traffic through a single proxy endpoint, applications can efficiently forward requests to the designated provider while capturing detailed logs that include model information, token usage, status, timing, costs, input, output, and raw JSON context. Typically, teams only need to adjust the base URL of the provider and utilize an AI Cost Board project key, thereby maintaining the integrity of the original request structure. This platform accommodates a variety of providers such as OpenAI, Anthropic, and Google Gemini, offering a standardized setup that harmonizes usage data across different integrations. Cost analytics provide a breakdown of spending categorized by project, provider, model, and timeframe, enabling users to identify trends, calculate cost per request, assess success rates, and evaluate operational performance. Moreover, the searchable request logs empower developers to analyze payloads, address failures, compare various models, and probe into instances of slow or costly API calls. Overall, AI Cost Board enhances transparency and control over AI API expenditures, facilitating informed decision-making for teams utilizing AI technology.
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Pricing
Pricing Starts At:
$49 per month
Free Version:
Yes
Company Details
Company:
ZenLLM
Headquarters:
United States
Website:
www.zenllm.io
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Product Details
Platforms
Web-Based
Types of Training
Training Docs
Customer Support
Online Support
ZenLLM Features and Options
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