1
Honeycomb LLM Observability
Shares tags: build, observability & guardrails, cost/latency
Ferramenta de IADead Man Walking
Eleve suas Aplicações de LLM com o LLMonitor
A Solução Definitiva Autossuficiente para Rastreio e Gestão de Custos
shipped 21 de nov. de 2025buildpaid
1Obtenha visibilidade total sobre o desempenho e os custos da sua aplicação LLM.
2A solução autogerida garante a privacidade e segurança dos dados.
3Painéis personalizáveis adaptados às suas necessidades específicas.
Stork Quadrant
Dead Man Walking· 11/100
An LLM can do most of what this tool's UI promises. No moat, no agent presence.
“LLMonitor is a thin observability layer on top of LLM API calls. An LLM plus a logging library plus a BI tool replicates most of this. No proprietary data, no network effects, no regulatory gate. This will get absorbed by the platforms it monitors — OpenAI, Anthropic, and cloud providers are all building native cost dashboards.”
Defensibility · 0/100
- Physical-world coupling
- Regulatory moat
- Network liquidity
- Proprietary refreshing data
- High-trust catastrophic workflows
- Multi-party coordination
- Brand / community / taste
An LLM alone could replace
- Summarize LLM usage costs from API logs
- Generate a dashboard or report of token consumption by model
- Identify slow or expensive LLM calls from structured log data
- Write code to instrument an LLM app with tracing and logging
Agent-Readiness · 25/100
- Verified MCP
- Listed on agent surfaces
- Usage-based pricing
- Headless agent auth— https://llmonitor.com/docs (api-key auth)
- Public OpenAPI— https://llmonitor.com/docs
- Active changelog
- llms.txt
Score history · +4 pts over 2 re-scores
How to defend
Go vertical: pick one high-stakes industry (healthcare, finance) where LLM audit trails carry compliance weight, and own the liability. Or stop being a dashboard and become the SDK that agents call to enforce spend limits and routing rules programmatically.
- Ship an MCP server and list it on Stork — biggest single point gain (+25).
- Get listed in the Anthropic MCP registry, Cursor, or Claude Desktop (+20).
- Add a usage-based or per-call tier; per-seat-only pricing dies when agents replace seats (+15).
- Publish a public changelog and ship in the last 90 days — silence reads as abandonment (+10).
- Ship an /llms.txt file pointing agents to your most important docs (+5, easy win).
LLMonitor at a Glance
Ferramentas similares
Comparar alternativas
Outras ferramentas a considerar
Ver no Stork→
2
Baseten Traces
Shares tags: build, observability & guardrails, cost/latency
3
Log10
Shares tags: build, observability & guardrails, cost/latency
4
Spice.ai Cost Guard
Shares tags: build, observability & guardrails, cost/latency
</>Embed "Featured on Stork" Badge▼
<a href="https://www.stork.ai/en/llmonitor" target="_blank" rel="noopener noreferrer"><img src="https://www.stork.ai/api/badge/llmonitor?style=dark" alt="LLMonitor - Featured on Stork.ai" height="36" /></a>
[](https://www.stork.ai/en/llmonitor)
overview
O que é o LLMonitor?
LLMonitor é a sua ferramenta auto-hospedada de referência para monitoramento completo e painéis de custos adaptados a aplicações de Modelos de Linguagem de Grande Escala. Monitore facilmente o desempenho e os gastos dos seus aplicativos, capacitando você a tomar decisões informadas.
- 1Acompanhamento de desempenho em tempo real
- 2Interface amigável
- 3Ferramentas robustas de análise de dados
features
Recursos Principais
A LLMonitor oferece um conjunto de recursos poderosos projetados para aprimorar suas aplicações de LLM. Experimente uma observabilidade e gestão sem interrupções com ferramentas para monitorar custos e latência de forma eficaz.
- 1Análise detalhada de latência
- 2Ferramentas de visualização de custos
- 3Integração com tecnologias já existentes
use cases
Casos de Uso
Seja você desenvolvendo chatbots, geradores de conteúdo ou outras aplicações impulsionadas por IA, o LLMonitor se adapta às suas necessidades ao fornecer insights críticos sobre desempenho e gastos. Otimize suas aplicações de LLM de forma eficaz.
- 1Monitore chatbots de IA para melhorar o tempo de interação.
- 2Analise os custos associados às diferentes versões do modelo.
- 3Avalie a utilização de recursos em seus aplicativos.
getting started
Introdução ao LLMonitor
Começar a usar o LLMonitor é simples. Instale o aplicativo auto-hospedado, configure suas preferências de monitoramento e comece a visualizar o desempenho de seus modelos de linguagem de grande porte imediatamente.
- 1Guia de instalação fácil
- 2Configuração passo a passo
- 3Suporte ao cliente responsivo
competitors
Alternatives & Competitors
1
Langfuse is a comprehensive open-source LLM engineering platform offering end-to-end visibility with tracing, evaluations, prompt management, and metrics.
Langfuse offers both self-hosting and a managed cloud service, providing flexibility similar to LLMonitor's self-hosted focus but also a cloud option. It extends beyond basic tracing and cost, including prompt management and evaluation features.
2
Helicone provides a simple, gateway-first approach to LLM observability, focusing on monitoring, debugging, and improving LLM applications with minimal code changes.
Helicone is open-source and offers a self-hosted option, directly competing with LLMonitor's self-hosted model. It emphasizes ease of setup and provides unified billing and cost tracking across various LLM providers.
3
OpenLLMetry is an open-source observability product for LLM applications built on OpenTelemetry, allowing data capture from various LLM providers and frameworks to be sent to multiple destinations.
OpenLLMetry is open-source and focuses on leveraging OpenTelemetry for LLM observability, offering flexibility in data destination, which contrasts with LLMonitor's more integrated dashboard approach. Traceloop also provides a backend for accepting these traces.
4
SigNoz↗
SigNoz is a full-stack open-source observability platform that provides correlated traces, logs, and metrics for LLMs alongside traditional application monitoring.
SigNoz offers both self-hosting and a cloud version, aligning with LLMonitor's self-hosted focus. Its strength lies in providing comprehensive observability for the entire application stack, not just LLMs, which can be a broader offering.
5
OpenObserve is an open-source, Rust-based observability platform optimized for cost-efficient storage and SQL-native querying of logs, metrics, and traces, including LLM cost monitoring.
OpenObserve is open-source and self-hostable, directly aligning with LLMonitor's deployment model. Its primary differentiator is its cost-efficiency and SQL-native query capabilities for detailed cost attribution and analysis.
❓
Perguntas frequentes
+É fácil instalar o LLMonitor?
Sim, o LLMonitor é projetado para facilitar a instalação, com um guia abrangente que o ajudará a configurá-lo em poucos minutos.
+Posso personalizar os painéis?
Com certeza! O LLMonitor permite que você crie painéis personalizáveis que atendem especificamente às suas necessidades de acompanhamento de desempenho e custos.
+Há suporte para diferentes estruturas de LLM?
Sim, o LLMonitor é compatível com várias estruturas de LLM, tornando-o versátil para uma diversidade de aplicações.
For builders
This page is doing a job for someone else’s tool.
AI agents read it. Buyers find it. Backlinks accrue. Your tool can have one too — live in 24 hours, indexed by Claude, ChatGPT, and Perplexity, queryable via MCP.