Apitally – REST API monitoring made easy

Hacker News

Hey Hackers! I’d like to introduce you to Apitally, a simple API monitoring tool I’ve been solo-building over the past 9 months. It all began with scratching my own itch as I’m technically responsible for maintaining a few API products at work. But it seems that other people find it useful too, enough even to pay money for it! (yay) Apitally provides insights into API traffic, errors, response times and payload sizes, for the whole API and individual endpoints. It also monitors API uptime & availability, alerting users when their API is down. The big monitoring platforms (think Datadog etc.) can be a bit overwhelming & expensive, particularly for simpler use cases. So Apitally’s key differentiators are simplicity & affordability, with the goal to make it as easy as possible for users to start monitoring their APIs. Apitally works by integrating directly with web frameworks through middleware, which captures request & response metadata (never anything sensitive!) and asynchronously ships it to Apitally’s servers in 1 minute intervals. The Python and Node.js client libraries currently support FastAPI, Flask, Django, Starlette, Express, Fastify, Nest.js and Koa. On the server side Apitally leverages NATS JetStream as a message queue, ClickHouse for OLAP, is built in Python using FastAPI and runs on a Kubernetes cluster on DigitalOcean. I’m looking forward to the direct & honest feedback this community is known for! :-)

Visit product View on Hacker NewsFeb 27, 2024

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Product HuntOn track for Day 1 leaderboard · Strong signals: user, using, apis · Missing: mac, agents, macos

87%87% 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.

Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting

78%78% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: ide, clickhouse · Missing: https docs, excited, just released

45%45% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

TrustMRRLess likely to generate early MRR · Strong signals: month, users · Missing: mobile apps, ios, personal

43%43% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Strong signals: platform, users · Missing: plus, intuitive, reviews

32%32% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue

15%15% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Strong signals: introduce · Missing: web3, chat, crypto

0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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