Openlayer
DevOps
4.0
/5
Toolio reviewer
Suits regulated enterprises, does not fit small startups.
Browse by the job,
not the category
Most people arrive knowing what they need done, not which category it lives in. 65 jobs grouped into 12 kinds of work. Pick the kind on the left, or search.
Tick Compare on any two to four cards to put them side by side.
DevOps engineer
Infrastructure, deployment and monitoring.
All software and it 7
Software developer 434
Web developer 97
Data scientist and ML engineer 119
DevOps engineer 84
API and integrations developer 116
Cybersecurity analyst 186
IT and support technician 8
DevOps engineers run infrastructure, automate deployments, watch systems and respond when something breaks. AI tools fit different parts of that loop: Kubernetes help (K8sGPT, K8sStudio), hosting and deployment platforms (Vercel, Runpod), test automation (testRigor, Applitools) and LLM traffic monitoring (Helicone). Check how much access a tool needs to your cluster or logs, and what it can change without approval.
Questions people ask
Can AI diagnose Kubernetes problems?
It can scan cluster state, explain error messages and suggest likely causes, which K8sGPT is built for. Treat suggestions as hypotheses. Read the proposed fix, check it against your configuration, and avoid giving a tool write access to production resources.
What should I watch for when AI tools touch production infrastructure?
Limit permissions to read-only wherever possible, log every action, and require human approval for changes. Check where logs and configuration are sent, since they may contain secrets, tokens or customer data that should not leave your environment.
What is the difference between LLM observability and normal monitoring?
Normal monitoring tracks servers, uptime and errors. LLM observability, as offered by tools like Helicone, records prompts, responses, latency and token usage for model calls. If your product calls an AI API, you probably need both to debug slow or incorrect responses.