Your AI System Looks Healthy. It Is Not.
Traditional monitoring will tell you your AI service is up. It won't tell you it's returning confident garbage. Here's what observability actually looks like for AI.
Observability
Definition
Observability coverage in this archive spans 11 posts from Sep 2016 to Mar 2025 and focuses on reliability, delivery speed, and cost discipline as one system, not three separate concerns. The strongest adjacent threads are monitoring, devops, and production. Recurring title motifs include observability, monitoring, enough, and ai.
What the archive argues
- Most posts prioritize predictable operations over feature breadth or stack novelty.
- Early posts lean on monitoring and enough, while newer posts lean on observability and small as constraints shifted.
- This topic repeatedly intersects with monitoring, devops, and production, so design choices here rarely stand alone.
Execution checklist
- Set SLOs first, then choose tooling that keeps deploy, observability, and rollback simple.
- Start with the newest post to calibrate current constraints, then backtrack to older entries for first principles.
- When boundary questions appear, cross-read monitoring and devops before committing implementation details.
Common failure modes
- Adding platform layers faster than the team can operate and debug them.
- Chasing throughput gains without proving they improve end-user reliability.
- Applying guidance from 2016 to 2025 without revisiting assumptions as context changed.
Suggested reading path
- Start here (current state): Your AI System Looks Healthy. It Is Not.
- Then read (operating middle): Observability for Small Distributed Teams (What Actually Works)
- Finish with (foundational context): Log Aggregation at Scale: ELK vs Alternatives
Related posts
- Your AI System Looks Healthy. It Is Not.
- LLM Observability: Your Existing Monitoring Is Not Enough
- OpenTelemetry in Late 2021: What’s Ready and What’s Not
- Observability-Driven Development Is Just Instrumenting Your Code
- eBPF Is Interesting. I Am Not Sold Yet.
- Observability for Small Distributed Teams (What Actually Works)
- Your SLOs Are Probably Useless (Here’s How to Fix Them)
- Why Monitoring Wasn’t Enough and How We Built Observability at a Fintech Startup
References
LLM Observability: Your Existing Monitoring Is Not Enough
Traditional monitoring tells you the service is up. It doesn't tell you the model started confidently returning garbage last Tuesday. Here's how to actually observe LLM systems.
OpenTelemetry in Late 2021: What's Ready and What's Not
Tracing is ready. Metrics are getting there. Logs are not. Here's a practical adoption path and the code to back it up.
Observability-Driven Development Is Just Instrumenting Your Code
ODD sounds fancy. It's not. It means writing logs, metrics, and traces before you ship, not after your first outage.
eBPF Is Interesting. I Am Not Sold Yet.
eBPF promises kernel-level observability without the pain of kernel modules. The tech is real. The hype-to-adoption ratio concerns me.
Observability for Small Distributed Teams (What Actually Works)
Most observability advice is written for 500-engineer orgs. Here's what actually matters when you're a small distributed team trying not to drown in dashboards.
Your SLOs Are Probably Useless (Here's How to Fix Them)
Most SLOs are dashboards nobody acts on. Here's how to pick indicators that reflect real users, set targets grounded in data, and make error budgets actually change how your team ships.
Why Monitoring Wasn't Enough and How We Built Observability at a Fintech Startup
After a mystery outage that our dashboards couldn't explain, I rebuilt the fintech startup's telemetry stack around metrics, logs, and traces. Here's what I learned.
Monitoring Is Not Enough
Your dashboards look green. Your users say the site is broken. That gap is the whole problem.
Why We Deleted 42 Grafana Panels
Most teams monitor too much and alert on the wrong things. Five metrics are enough to run a startup backend.
Log Aggregation at Scale: ELK vs Alternatives
ELK is powerful. It's also a second full-time job. Here's what I learned running it at Dropbyke, and what I'd consider instead.