StackScan
BuiltWith
Wappalyzer
W3Techs
TheirStack
WhatRuns
Hunter.io
ZoomInfo
Langfuse
Helicone AI
LangSmith
LangChain
PromptLayer
Braintrust.dev
Portkey
Openlayer
StackScan helps businesses find and analyze websites based on the technologies they use or the keywords they target. Instead of manually researching websites one by one, users can instantly search across 100M+ domains and identify sites using platforms like Shopify, WordPress, WooCommerce, Webflow, and thousands of other technologies.
The platform provides practical filtering tools that allow users to narrow results by country, TLD, industry, or specific technology combinations. This makes it useful for building targeted lead lists, researching competitors, discovering niche markets, or identifying companies using certain software stacks for outreach and partnerships.
StackScan also supports bulk data downloads, keyword-based website discovery, and structured reporting to simplify large-scale research workflows. With continuously refreshed datasets and scalable search capabilities, it enables marketers, agencies, analysts, and growth teams to access actionable web intelligence quickly and efficiently.
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.
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StackScan's answer
StackScan focuses on practical usability, broader stack coverage, advanced filtering, and scalable exports without unnecessary complexity. Users can quickly generate highly targeted datasets using filters like country, TLD, industry, and technology combinations, making research and lead generation faster and more precise.
StackScan's answer
StackScan combines technology stack discovery and keyword-intent research in a single platform, allowing users to find websites not only by the tools they use but also by what they are targeting online. With coverage across 50,000+ technologies and 100M+ domains, it provides scalable, filterable, and export-ready web intelligence.
StackScan's answer
StackScan is built for marketers, growth teams, agencies, sales teams, analysts, SaaS companies, and researchers who need structured web intelligence for prospecting, competitor analysis, market research, or technology adoption tracking.
StackScan's answer
StackScan was created to simplify the process of finding reliable website and technology data at scale. Existing solutions often felt limited, expensive, or difficult to use for targeted workflows, so StackScan was built as a practical and scalable platform that combines technology detection, keyword discovery, and bulk data access into one system.
StackScan's answer
StackScan is built using modern web technologies, large-scale crawling systems, distributed data processing, and technology fingerprinting engines designed to analyze and structure massive amounts of web data efficiently.
StackScan's answer
StackScan is used by agencies, SaaS businesses, growth teams, researchers, and data-driven organizations for lead generation, market intelligence, and competitive analysis across multiple industries.
While itโs still in early stage, its lifetime deal is really a great value. Must get if youโre into lead generation.
Based on our record, Langfuse seems to be more popular. It has been mentiond 29 times since March 2021. 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.
Langfuse and LangSmith exist for this. Use them. The 30 minutes you spend setting up observability saves you the 87 hours you'd spend debugging blind. - Source: dev.to / 9 days ago
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 / about 2 months 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 / 2 months 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 / 3 months 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 / 3 months ago
BuiltWith - Find out the technology behind websites
Helicone AI - Open-source LLM Observability for Developers
Wappalyzer - Wappalyzer is a technology profilers and leads data provider. Create lists of websites and contacts that use certain technologies.
LangSmith - Build and deploy LLM applications with confidence
W3Techs - W3Techs provides information about the usage of various types of technologies on the web.
LangChain - Framework for building applications with LLMs through composability
