AI SRE compared: Tares, vendor AI, and one MCP server per tool
Tares fits when your signals live in several tools, you want to keep the alerting you already run, and you want an agent woken the moment an alert fires. One read returns the correlated timeline of a service across every source, a trigger wakes the agent, and the agent writes its finding back where the next investigation starts. Tares runs on your machine or your server, in one local DuckDB file.
There are three common ways to give an agent your production data. This page compares them. To build the Tares version, see Build an AI SRE.
How they compare
The columns are an agent with one MCP server per tool, the AI built into an observability vendor, and Tares. The vendor column describes vendors in general and names none; it varies by vendor, so check the one you use.
| Agent + one MCP server per tool | Observability vendor AI | Tares | |
|---|---|---|---|
| One correlated read across sources | No, one call per tool | Within that vendor’s data | Yes |
| Wakes an agent on a condition | No, the agent pulls from each server | Vendor workflows | Yes, a Tares agent or your own agent service over a webhook |
| Findings written back onto the timeline | No, unless you build it | Varies by vendor | Yes |
| Runs locally with no external database | Depends on each server | Varies by vendor | Yes, one DuckDB file |
| Works with any MCP client | Yes | Varies by vendor | Yes |
| Open source | Mixed | Varies by vendor | Yes, MIT |
| You keep your alerting | Yes | Varies by vendor | Yes |
Measured result
On the same incident with the same agent, an agent with one MCP server per tool took 6 reads and 3 turns per diagnosis. With Tares it took 1 read and 2 turns, and 0 reads when the trigger pushed the timeline to the agent. These are single runs, so treat them as directional. The code and the details are in the tares-cookbooks repository.
When something else fits better
- One MCP server per tool. If your data sits in one or two tools that already ship an MCP server, and you investigate by hand when you are paged, adding a server per tool is the least setup. You give up the single correlated read and the wake-up on a condition.
- An observability vendor’s AI. If you already run most of your telemetry in one vendor and its data covers your incidents, the AI built into that product works inside the data it already holds, with no extra system to run. It is the more natural choice when you do not want to host anything yourself.
Tares earns its place when evidence is spread across metrics, logs, alerts and deploys in different tools, or when you want your own agent, such as Claude Code, to read the same timeline that a built-in agent writes its findings to.
Next
- Build an AI SRE: cause an incident and watch an agent diagnose it.
- Use AI to monitor your systems: what to connect and how an alert becomes a diagnosis.