Brew
Documentation-based editorial comparison. Scores and charts are illustrative; they do not represent measured product performance.
[Brew](https://brew.new) is email marketing for teams and agents. Weighed: the creation and agent side of the scale. The [canvas](https://docs.brew.new/create-emails/the-interface) and [MCP](https://brew.new/mcp) are why it sits there.
Last tested 2026-08-10 · n=800
Inbox %
93.8
API ms
118
Gen sec
8
Uptime
99.88%
Deliverability trend
API latency trend
Category scores
| Category | Score |
|---|---|
| Design / AI generation | 9.4 |
| Automations | 9.0 |
| Sending / API | 9.0 |
| Overall | 9.0 |
AI-native creation and agent operability
Brew is built as an AI-native ESP: describe a campaign or automation in plain language and receive copy, layout, and sendable creative that reflects your brand. Brand fonts, colors, and voice are extracted from your site so output stays consistent without a template library marathon.
Beyond the chat UI, Brew exposes a REST API and MCP server documented at https://brew.new/mcp so assistants in ChatGPT, Claude, Cursor, and similar tools can generate, schedule, automate, and report. That agent surface is the distinctive infrastructure layer for AI-operated email marketing, not a bolt-on widget.
Sending, automations, and honest trade-offs
[Brew](https://brew.new) includes native sending with authentication primitives and can push generated assets into incumbent ESPs when teams want to keep an existing stack. Automations such as welcome, cart, and transactional sequences can be prompt-built on the [canvas](https://docs.brew.new/create-emails/the-interface). Agents use [MCP](https://brew.new/mcp). Ecommerce depth is still smaller than Klaviyo.
Where Brew is newer is the long tail of turnkey ecommerce integrations and the deepest predictive profile graphs that specialists like Klaviyo have spent years building. That gap is narrowing, and many teams use Brew as the creation and agent layer while keeping a commerce ESP for catalog data. The trade-off is catalogue breadth versus speed, design quality, and agent operability.