Warp is a good terminal. I tried it. The block-based output, the AI command suggestions, the GPU rendering. If you’re a developer writing code, it’s hard to beat. 700,000 developers agree.
But when my pager goes off at 2am and I need to figure out why the payments service is throwing 500s, I don’t open Warp. I open SuperTerminal. They’re built for different moments in the software lifecycle.
What Warp does
Warp is a terminal emulator that’s evolved into a coding agent platform. The terminal itself is fast, modern, and well-designed. Block-based output, syntax highlighting, completions, multi-line editing. Built in Rust.
The bigger story is Oz, their agent orchestration platform. You give Oz a prompt (“refactor the auth module to use JWT”), and it spins up a sandboxed container with your code repo, runs an AI agent against it, and produces a pull request. You can run hundreds of these in parallel. Schedule them on cron. Trigger them from Slack, Linear, or GitHub Actions.
Oz agents are autonomous. You set them loose on a task and they figure out the steps. They support Claude, GPT, and Gemini as backend models. The whole thing is designed for code-level work: writing features, fixing bugs, reviewing PRs, updating docs.
Warp charges $20/month for their Build tier (1,500 AI credits) with enterprise pricing for teams.
What Warp doesn’t do
Warp’s agents run in sandboxed containers. They clone your repo, make changes, open a PR. They never touch production. They never SSH into a server. They never read production logs. That’s by design. It’s a development tool.
Warp has basic SSH support (“Warpify” for SSH sessions) but no connection management, no saved hosts, no credential vault. No integration with PagerDuty, Sentry, or Datadog. No concept of runbooks, diagnostic sequences, or incident response workflows.
If your production database is running out of connections at 3am, Warp can’t help you find why. It can help you write the fix after you’ve found the cause.
What I built SuperTerminal for
I built SuperTerminal for the part that comes after the alert fires and before the fix ships.
SuperTerminal connects to your actual production servers over SSH. You describe the incident, and it runs a diagnostic runbook: check logs, query services, examine resource usage. The AI reads each step’s output and tells you what it found. Not autonomous agent-style “figure it out.” Deterministic steps with AI-supplemented analysis.
The runbook runs the same way every time. Step 1 checks the database connections. Step 2 checks the application logs. Step 3 checks disk usage. The AI interprets the output between steps, but the steps themselves are predictable. You can hand a runbook to a junior engineer at 2am and they’ll get the same diagnostic quality as the senior who wrote it.
The architectural difference
This is the part that matters if you’re evaluating both:
| Warp / Oz | SuperTerminal | |
|---|---|---|
| Execution environment | Sandboxed containers (Docker/k8s) | Live production servers via SSH |
| Agent model | Autonomous (LLM decides what to do) | Deterministic runbook (human defines steps, AI interprets output) |
| Target | Code repos | Running infrastructure |
| State | Stateless (workspace destroyed after each run) | Stateful (server context persists) |
| Integrations | GitHub, Slack, Linear | Sentry, PagerDuty, Datadog (planned), Jira |
| Trigger sources | API, cron, Slack, GitHub Actions | Manual, webhooks, scheduled (Pro) |
| Output | Pull requests, code changes | Root cause reports, diagnostic logs |
| Pricing model | AI credits per run | Flat annual / lifetime deal |
Why determinism matters for ops
Warp’s Oz agents are autonomous. You give them a prompt, they decide what commands to run, what files to read, what changes to make. For coding tasks, this works. The worst case is a bad PR that fails code review.
For production incidents, autonomous agents are dangerous. You don’t want an LLM deciding to restart a service, modify a config, or run a destructive query based on its interpretation of a prompt. Ops needs predictability. The runbook says “step 3: check connection pool count.” That’s what runs. The AI tells you what the count means. It doesn’t decide on its own to drain the pool.
This is the philosophical split between the two products. Warp trusts the AI to drive. SuperTerminal trusts the AI to navigate while the human drives.
When to use Warp
You’re writing code and want an AI assistant in your terminal. You want to run coding agents at scale (batch PR reviews, automated refactoring, docs updates). You want a modern terminal experience with completions and block output. You’re a developer at your desk during business hours.
When to use SuperTerminal
You got paged. Something is broken in production. You need to SSH into servers, pull logs, check services, and find root cause. You want a runbook that runs the same diagnostic sequence every time this failure mode fires. You’re an on-call engineer at 2am.
Can you use both?
Yes, and that’s probably the right answer for most teams. Warp for development. SuperTerminal for incidents. They don’t overlap.
A developer uses Warp all day to write code. When that code ships and something breaks, the on-call engineer uses SuperTerminal to diagnose it. The root cause goes back to the developer, who uses Warp to write the fix. Different tools for different moments.
Try SuperTerminal
Free and open source. Uses your SSH config and your own AI keys. Set it up in 5 minutes.