
Langfuse
Helicone AI
LangSmith
LangChain
Openlayer
Braintrust.dev
Portkey
PromptLayer
CloudCheckr
VMware Tanzu CloudHealth
Cloudability
Amazon CloudWatch
Nutanix Beam
Turbonomic
AWS Budgets
Duo Security
Langfuse is an open-source LLM engineering platform designed to empower developers by providing insights into user interactions with their LLM applications. We offer tools that help developers understand usage patterns, diagnose issues, and improve application performance based on real user data. By integrating seamlessly into existing workflows, Langfuse streamlines the process of monitoring, debugging, and optimizing LLM applications. Our platform's robust documentation and active community support make it easy for developers to leverage Langfuse for enhancing their LLM projects efficiently. Whether you're troubleshooting interactions or iterating on new features, Langfuse is committed to simplifying your LLM development journey.
Langfuse
CloudCheckrCloudCheckr is particularly recommended for mid to large-sized enterprises that require detailed insights into their cloud expenditures and usage across multiple accounts and services. It is ideal for teams that need to manage cloud resources efficiently, ensure compliance, and maximize cost savings. IT administrators, financial managers, and security teams may find this tool especially beneficial.
Based on our record, Langfuse seems to be a lot more popular than CloudCheckr. While we know about 28 links to Langfuse, we've tracked only 1 mention of CloudCheckr. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.
In this project we will build a Python banking assistant agent using Strands Agents and make it observable and continuously evaluated using Langfuse โ step by step. - Source: dev.to / 5 days ago
Langfuse is the open-source standard for LLM observability. It traces every LLM interaction โ prompts, completions, latency, token usage, cost โ and provides the tooling to debug, evaluate, and optimize LLM applications in production. Think of it as "Datadog for LLM calls" with a focus on prompt engineering workflows. - Source: dev.to / 24 days ago
You're monitoring production traffic. You need Langfuse / Phoenix / Helicone / Braintrust for that. Online eval is a different problem class: implicit feedback, drift detection, hallucination rates on your data, not on HellaSwag. - Source: dev.to / about 1 month ago
Gateway or proxy attribution. A reverse proxy in front of the model-provider API records the request, computes the cost, and exposes per-customer breakdowns. Open-source options include Helicone, LiteLLM, Langfuse, and OpenLLMetry. Hosted equivalents serve as the AI cost observability layer for teams that want centralized visibility: LangSmith, Datadog LLM Observability, Arize Phoenix. Adds a network hop.... - Source: dev.to / about 1 month ago
Same approach works with Langfuse, Phoenix, Braintrust, or your existing OTel pipeline โ the metadata.userId pattern is the universal part. - Source: dev.to / about 2 months ago
Depends a bit on what you understand under 'housekeeping' - but what about: Https://cloudcheckr.com/ Https://cloudhealth.vmware.com/solutions/aws-management.html Https://www.gorillastack.com/. Source: about 3 years ago
Helicone AI - Open-source LLM Observability for Developers
VMware Tanzu CloudHealth - CloudHealth is IT service management for the cloud, enabling policy driven cost, utilization, performance and security optimization.
LangSmith - Build and deploy LLM applications with confidence
Cloudability - Cloudability lets you monitor, manage and communicate your cloud costs with one easy tool.
LangChain - Framework for building applications with LLMs through composability
Amazon CloudWatch - Amazon CloudWatch is a monitoring service for AWS cloud resources and the applications you run on AWS.