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Apitally – A simple API monitoring and analytics tool for Go

Hacker News

Apitally – A simple API monitoring and analytics tool for Go

G’day Hacker News, I’m Simon Gurcke, the sole founder of Apitally ( https://apitally.io ). I’m building a simple API monitoring and analytics tool, which is helping users understand API usage and performance, spot issues early and troubleshoot effectively when something goes wrong. Today I'm showing off the new Apitally Go SDK ( https://github.com/apitally/apitally-go ) with support for the frameworks Echo, Fiber, Gin and chi. Each framework is integrated via specific middleware, while most of the processing happens in a separate goroutine. I had never built anything in Go before, so this was a great learning opportunity for me. By now the SDK has been through a few iterations, and I feel comfortable that it's ready for widespread production use. It has good test coverage and an extensive test matrix for different versions of Go and the supported frameworks. Noteworthy features of Apitally include: - Metrics & insights into API usage, errors and performance, for the whole API, each endpoint and individual API consumers. - Request logging, which is opt-in and highly configurable in terms of what data is included in the logs. Users can drill down from aggregated metrics to individual requests, which has proven to be super helpful when troubleshooting issues. - Uptime monitoring & custom alerts based on various API traffic, error and performance metrics with notifications delivered via email, Slack or Microsoft Teams. I've actually posted about Apitally before (in February). At the time it only supported Python and Node.js. Now I'm hoping to reach the Go community as well. The motivation for building Apitally came from my frustration with existing monitoring tools, which were too complex for my API-centric use cases, and often a pain to use. Consequently, I put a lot of emphasis on keeping Apitally as simple as possible and fully focussed on REST APIs. Apitally is a paid SaaS now (I've dropped the free tier to become more sustainable), but with very affordable pricing starting at $9 per month. There's a free 14-day trial, and the dashboard has a demo mode, so users can explore it without having to set up their own app. One of the next big items on my roadmap is to support OpenTelemetry for application logs and traces.

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Actual performance

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Did not reach leaderboard

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Indie HackersFits the IH revenue-focused audience · Strong signals: para · Missing: supports, reddit linkedin, podcasting
92%92% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
Product HuntOn track for Day 1 leaderboard · Strong signals: slack, user, new · Missing: mac, agents, macos
82%82% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
TrustMRRFits verified-revenue profile · Strong signals: month, users, para · Missing: mobile apps, ios, personal
56%56% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: exist, existing, hacker news · Missing: https docs, excited, just released
54%54% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
AppSumoMay struggle as an AppSumo deal · Strong signals: users · Missing: plus, platform, intuitive
36%36% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Strong signals: saas · 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: paid · Missing: web3, chat, crypto
0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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