AI observability by OpenObserve means two different things depending on which layer you turn on: an AI Assistant that answers plain-English questions about your logs and metrics, and an O2 SRE Agent that correlates alerts across your whole stack into one incident summary. Both sit on top of the same open-source logs-metrics-traces platform, and neither requires you to learn a new query language first.
Short answer: OpenObserve adds AI on top of its open-source observability core: an AI Assistant that turns plain English into SQL or PromQL, and an O2 SRE Agent that correlates alerts into one incident with a root cause. The assistant works on the free cloud trial; the SRE Agent needs Enterprise (self-hosted, free under 50 GB/day, or Cloud). AI features are free during preview with 20 trial credits.
Last updated September 10, 2026.
When I went through OpenObserve's docs, its GitHub repo, and its pricing and platform pages this week rather than taking the marketing copy at face value, what stood out was how much of this is genuinely usable before you pay anything — and how much of the flashier AI SRE Agent demo is still a vendor-reported number, not something I could reproduce on a busy production stack myself. Here's what each AI layer actually does, what it costs, and where the setup gets more involved than the pricing page suggests.
What you'll need
OpenObserve ships as a single Rust binary, so the fastest path is either the 14-day cloud trial (no credit card) at cloud.openobserve.ai or a local Docker/binary install if you want to self-host from the start. The AI Assistant works on the free cloud trial with no extra setup. The O2 SRE Agent is a separate piece: it needs an OpenObserve Enterprise license (self-hosted is free under 50 GB of ingestion per day) running version 0.60.0 or later, plus an API key for an AI provider — Anthropic, OpenAI, or Google Gemini — since the agent calls out to a model you supply. If you're deploying the SRE Agent on Kubernetes, have Helm and kubectl ready; the manual deployment path also works but means writing your own manifests.
Step-by-step: AI Observability by OpenObserve setup
1. Start on the free cloud trial or self-host
Sign up at cloud.openobserve.ai for the 14-day trial, or pull the open-source binary if you'd rather run it locally first. The open-source edition is free forever and is what the entire platform — ingestion, search, dashboards — is built on before any AI layer is added.
2. Send your first logs or metrics
Point an existing agent (Fluent Bit, the OpenTelemetry Collector, or a direct API call) at your OpenObserve instance. The AI Assistant and SRE Agent are both only as useful as the data underneath them — an empty instance gives either one nothing to reason about.
3. Open the AI Assistant and ask a real question
Instead of writing SQL, type something like "show me the error rate for the checkout service in the last hour." The assistant converts that into a ready-to-run SQL or PromQL query against your own data, and can also draft a dashboard panel or alert condition from the same kind of plain-English prompt.
4. Turn on the O2 SRE Agent (Enterprise)
On an Enterprise deployment, set O2_AI_ENABLED: "true" and deploy the agent via the OpenObserve Helm chart (recommended) or the public.ecr.aws/zinclabs/o2-sre-agent Docker image directly. This is the piece that watches alerts across logs, metrics, and traces at once rather than one signal at a time.
5. Pick an AI provider and model
Set O2_AI_PROVIDER to anthropic, openai, or gemini, and O2_AI_MODEL to a specific model — the setup docs list Claude Sonnet 4.5 and Claude 3.5 Sonnet, GPT-4 Turbo and GPT-4o, or Gemini 1.5 Pro, along with your O2_AI_API_KEY. There's no default model baked in; you're paying that provider directly for the agent's reasoning calls.
6. Let the agent run and read the incident summary
Once alerts start firing, the agent groups related ones into a single incident with a timeline, an executive summary, and suggested next steps instead of a wall of individual pages. OpenObserve's own docs set a 30-minute minimum cooldown between automatic root-cause reanalysis runs on the same incident, so don't expect a fresh RCA every time a new related alert lands.
7. Watch your ingestion volume before picking a paid tier
Whether you stay on self-hosted Enterprise's free 50 GB/day or move to Cloud, the meter that matters is daily ingestion, not seats — OpenObserve doesn't charge per user on any plan.
OpenObserve vs. Progress AI Observability at a glance
Both add an AI layer on top of telemetry data, but they start from opposite ends: OpenObserve is an open-source logs/metrics/traces platform that added AI, while Progress AI Observability is an AI-agent tracing tool that never touched infrastructure logs.
| OpenObserve | Progress AI Observability | |
|---|---|---|
| Core product | Open-source logs, metrics, traces, RUM | AI agent tracing and evaluation |
| Free tier | Open source: free forever. Self-hosted Enterprise: free under 50 GB/day | Free plan: 10,000 trace units, 7-day retention |
| Cloud pricing | $0.50/GB ingested (annual-commitment rate) + $0.01/GB queried | Starts at $29/month for 200,000 units |
| AI feature | AI Assistant (NL queries) + O2 SRE Agent (incident correlation) | LLM-as-a-judge evaluation of agent outputs |
| Best for | Teams already centralizing logs/metrics/traces who want less manual querying | Teams building AI agents who need to see why a run failed |
I confirmed OpenObserve's numbers on its own pricing page and GitHub repository this week; Progress's figures come from my separate hands-on writeup linked above. If your actual problem is "why did my AI agent pick the wrong tool," OpenObserve's AI layer isn't built for that — it's watching infrastructure and application telemetry, not agent reasoning steps.
Example prompts you can copy
These are close to what the AI Assistant is actually built to parse, based on OpenObserve's own documented examples:
- "Show me the error rate for the login service over the last 30 minutes."
- "Summarize what changed in the logs right after the 2 PM deployment."
- "Build a dashboard panel showing p95 latency by endpoint for the last 24 hours."
- "Create an alert if the checkout service's 5xx rate goes above 2% for 5 minutes."
- "List every service that logged a new error type in the last hour that wasn't present yesterday."
Keep each prompt scoped to one service or one time window — a vague "what's wrong with my system" question gives the assistant nothing specific to turn into a query, the same problem you'd hit typing that into a search box.
Common mistakes to avoid
The one I'd flag first: treating the 12,400-alerts-to-1-incident example on OpenObserve's own platform page as a guarantee rather than a demonstrated scenario — it's a real illustration of what alert correlation can do, but it's OpenObserve's own case study, not an independent benchmark, and your actual alert-fatigue reduction depends entirely on how noisy your existing alerting already is. Second, enabling the SRE Agent before you've picked and budgeted for an AI provider — the agent doesn't come with a bundled model, you're paying Anthropic, OpenAI, or Google directly for every reasoning call it makes. Third, expecting the AI Assistant to work well on an instance with only a few hours of data; like any natural-language-to-query tool, it does better with enough history to have real patterns to reference. Fourth, missing that the AI features are "free during preview" only up to 20 trial credits — that's for evaluation, not for running the SRE Agent against production alert volume indefinitely. Fifth, confusing the two AI layers: the Assistant is for querying your own data, the SRE Agent is for automated incident correlation, and turning one on doesn't automatically turn on the other.
Tools that make this easier
If your actual traffic is AI agents rather than infrastructure, OpenObserve's AI layer is watching the wrong thing — Progress AI Observability is built specifically to trace agent tool calls and model output. For the coding side that generates the services OpenObserve is meant to monitor, my AI coding assistant beginner’s guide and ChatGPT alternatives for coding roundup cover the tools most teams are already using. If you're running agents in isolated environments before they ever touch production telemetry, Docker sandboxes for AI agents is worth reading first. Teams connecting OpenObserve's MCP server to an existing AI workflow should also look at MCP-Builder.ai, and anyone worried the AI provider bill for the SRE Agent will creep up should read managing AI coding costs at scale before turning the agent loose on a noisy alert feed.
My take
The AI Assistant is the easier win here — it works on the free trial, it's genuinely faster than writing SQL from scratch when you already know roughly what you're looking for, and there's no separate AI bill since it rides on your existing OpenObserve plan. The O2 SRE Agent is a bigger commitment: it needs Enterprise, a model provider you pay separately, and real alert volume before you can judge whether it's actually cutting noise or just repackaging it. I'd trust the mechanism — correlating logs, metrics, and traces in one query engine is a real advantage over stitching together alerts from three separate tools — but I'd want to run it against my own noisy week before trusting the vendor's best-case numbers.
Frequently Asked Questions
Is AI Observability by OpenObserve free?
Partly. The AI Assistant works on OpenObserve's 14-day free cloud trial and on the open-source edition. AI features overall are "free during preview" with 20 trial credits, but the O2 SRE Agent needs an Enterprise license — free self-hosted under 50 GB/day ingestion, paid beyond that or on Cloud.
How long does it take to set up AI Observability by OpenObserve?
Turning on the AI Assistant takes minutes once you're ingesting data — there's no extra install. Setting up the O2 SRE Agent takes longer: you need an Enterprise deployment, a Helm install or manual Kubernetes deployment, and an API key from your chosen AI provider before it can run.
What is the easiest way to try OpenObserve's AI features?
Sign up for the free cloud trial, send a day or two of real logs or metrics, and ask the AI Assistant a specific question about that data. That's enough to judge whether the natural-language querying saves you time before you consider the Enterprise-only SRE Agent.
Which AI models power the O2 SRE Agent?
You choose. OpenObserve's setup docs list Claude Sonnet 4.5 and Claude 3.5 Sonnet from Anthropic, GPT-4 Turbo and GPT-4o from OpenAI, and Gemini 1.5 Pro from Google — you supply the API key and pay that provider directly for the agent's reasoning calls.
Does OpenObserve's AI layer replace agent-specific tracing tools?
No. It's built to correlate infrastructure and application telemetry — logs, metrics, traces — not to evaluate whether an AI agent picked the right tool or hallucinated an answer. For that, a dedicated agent-tracing platform like Progress AI Observability is the closer fit.