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A hosted MCP for analyzing infra costs

Hacker News

A hosted MCP for analyzing infra costs

Hi HN, I’m on the team at www.vantage.sh and we’re releasing our remote MCP hosted on Cloudflare Workers that gives LLMs context about your structured cost and usage data on AWS, Azure, GCP, OpenAI, Datadog and ~20+ other providers. We wanted to share with this community as we know that infra cost management is already a difficult-to-understand, cumbersome task that AI can now help with. Vantage has a set of logic for organizing costs on top of the base infrastructure level data (which we refer to as Virtual Tags) so that you can organize and allocate spend by service, business unit or function. All of that structured cost data is exposed via MCP to popular LLMs to answer specific questions about your organization. We put together a quick explainer video on how this works here: https://www.youtube.com/watch?v=69Zt51OSjC0 Docs here: https://docs.vantage.sh/vantage_mcp

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Product HuntOn track for Day 1 leaderboard · Strong signals: mcp, context, openai · Missing: mac, agents, macos
84%84% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
Hacker NewsStrong engagement from HN community · Strong signals: ide, io · Missing: https docs, excited, just released
59%59% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
TrustMRRFits verified-revenue profile · Strong signals: video · Missing: mobile apps, ios, personal
56%56% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: host · Missing: plus, platform, intuitive
33%33% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Indie HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting
30%30% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
19%19% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto
1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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