Axiom
@AxiomFM
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Logging re-invented for high-scale engineering teams.
🌍
Joined August 2016
Spotlight + AI is here ✨ - Investigate anomalies in seconds—no guesswork, no endless query iteration. Select a region on a chart → Spotlight compares it against baseline behavior → Get a clear, trustworthy summary of what’s different Learn more ↓
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axiom > datadog
Reactive backends like @convex_dev are transforming how we build apps- but they need observability that matches their real-time nature ⚡️ └ Convex provides useful basic logging └ However you need visibility into function performance, errors, and patterns too └ Real-time
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Observability isn't just about fixing issues—it's about understanding your entire system 🔎 └ Make data-driven decisions about function optimization └ Reduce operational costs through better resource management └ Accelerate debugging with detailed execution traces └ Keep your
axiom.co
Stream function executions and console logs from your Convex deployment to Axiom for powerful querying, visualization, and monitoring of your reactive backend.
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Proactive monitoring for reactive backends 🚨 └ Alert on function failures before they impact users └ Catch performance degradation before UX suffers └ Monitor error rates across all your functions └ Set up intelligent alerts via Slack, PagerDuty, or email Stay ahead of
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With Axiom + @convex_dev, you get powerful analytics at your fingertips 📊 └ Identify slow functions with execution time analysis └ Track error rates to catch issues before users notice └ Optimize database reads and cache hit rates └ Understand usage patterns with deep
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Introducing Axiom's @convex_dev integration: Complete observability for your reactive backend 🎯 └ Stream function executions, errors, and metrics automatically └ Track database usage, cache performance, and execution times └ Prebuilt dashboards give instant insights—no setup
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Reactive backends like @convex_dev are transforming how we build apps- but they need observability that matches their real-time nature ⚡️ └ Convex provides useful basic logging └ However you need visibility into function performance, errors, and patterns too └ Real-time
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Btw these stats are queried directly from @AxiomFM - it's amazing how easy it is to just send everything to Axiom and then query in grafana. It's handling 12k events/sec across different projects without missing a beat
Testing @PlanetScale with @CloudflareDev Workers and enabling Hyperdrive caching feels like actual magic - P99 read latency reduced by 97% 🤯
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From the Changelog: Richer audit log context! 📊 └ Privacy-safe query representations for better visibility └ Track query sources—see which monitors triggered each query └ Monitor query costs and storage usage patterns └ Identify slow queries and optimize resource consumption
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Build dashboards faster with improved validation cues and dataset organization. Learn more →
axiom.co
Better chart validation feedback, filter for datasets in dashboards, and upcoming ability to query metrics inside Axiom.
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From the Changelog: Better chart validation & dataset filtering in dashboards! ✨ └ Clear visual feedback: red cross shows chart validation issues └ Filter dashboards by specific datasets for focused insights └ Metrics querying coming this month—unified logs & metrics
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Amp is one of the most loved products engineers at Axiom use. And so we’re especially excited to be a part of bringing it to more developers through the excellent work by the @Sourcegraph team!
We made Amp Free. It's powered by great tokens and tasteful ads. Agentic coding is now free for everyone.
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This is what the entire exported trace looks like in @AxiomFM, showing: 1️⃣ Triggering from your backend 2️⃣ The Trigger task executing 3️⃣ Calls to APIs 4️⃣ Calling from the task out to your backend 5️⃣ Back to the task and the run finishing up
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Also, your observability data isn't locked in, you can export traces wherever you need them. In this example we're exporting to @AxiomFM
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Good observability isn't just a nice to have, we know developers need to be able to easily debug when things go wrong. We use @opentelemetry (OTEL) for our run logs, giving you a lot of detailed info about your tasks. It also allows us to support: ⚗️ Adding additional
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🛠️New engineering blog: Designing MCP servers for wide schemas and large result sets: └ Wide schemas in tight contexts └ Result size vs token budget └ Picking useful fields first └ Binning without losing shape Link below ↓
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From the Changelog: Query improvements and trace enhancements! 🚀 └ Enhanced field interactions: More intuitive workflow in query builder └ Smarter trace correlation: Auto-detect trace IDs in any field containing "trace" └ Better data ingestion with automatic sanitization of
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