Supermaven Teardown — Narrow Tech Wedge to Cursor Acquisition

Last updated: 2026-05-16 · Researched via TechCrunch, Bessemer Venture Partners, Cursor blog, HackerNews launch thread, Crunchbase, PitchBook public data.

TL;DR

A solo-founded, 9-month-old AI code completion tool with a 300K-token context window (15x larger than Copilot at the time) and 250ms latency (3x faster than the leading competitor) — built on custom Babble inference model, not an OpenAI API wrapper — that grew to roughly $80K MRR before being acquired by Cursor (Anysphere) in November 2024. Supermaven is the rare case of a one-person company that wedged into a Microsoft-dominated category by going deeper on a single technical metric, then exited to the category leader rather than fighting Copilot head-on.

Basic Information

Item	Detail
Website	supermaven.com (now redirects users to Cursor; product discontinued Nov 2025)
Position	"The fastest copilot" — code completion with 300K (later 1M) token context window
Founder	Jacob Jackson (solo founder; ex-Tabnine co-founder, ex-OpenAI researcher)
Founded	February 2024 (public launch via Show HN: "Supermaven, the first code completion tool with 300k token context")
Funding	$12M Series A, September 2024 (Bessemer Venture Partners; angels include OpenAI co-founder John Schulman, Perplexity co-founder Denis Yarats)
Acquisition	November 12, 2024 — acquired by Anysphere (Cursor) for undisclosed amount; talent + Babble model
Revenue at exit	~$80K MRR (public estimate; never disclosed officially)
Editor support	VS Code, JetBrains family, Neovim
Pricing (at exit)	Free / Pro $10 mo / Team $10 per seat mo
Tech	Custom inference stack (Babble model), kv-cache engineering, low-latency serving infra

Core Features

1. 300K-token context window at launch (Feb 2024) — at a time when Copilot used roughly 2K tokens of context and Cursor's autocomplete used ~10K. Upgraded to 1M tokens in mid-2024 with the larger Babble v2.
2. 250ms latency — measured against ~783ms for "the leading competitor" (read: Copilot). The latency claim was the public proof point; the kv-cache and serving infra work was the underlying engineering.
3. Babble model — custom-trained, not an OpenAI/Anthropic wrapper. Trained on a larger corpus than the v1 model and tuned specifically for completion (not chat). This is the asset Cursor bought.
4. Style adaptation — learns from edits inside your repo so completions match local conventions.
5. Chat with top models — Pro tier shipped a chat panel with GPT-4o / Claude 3.5 Sonnet routing, $5/mo of chat credits included.
6. IDE integration via extensions — no fork-the-editor strategy (opposite of Cursor's approach). Plain extension install in VS Code / JetBrains / Neovim.

Pricing Strategy

Tier	Price	Context	Chat	Data retention
Free	$0	Basic suggestions	None	7 days
Pro	$10/mo	1M tokens, style adaptation	$5/mo credits	Configurable
Team	$10 per seat/mo	Same as Pro	Same	Central admin

Key pricing insights:

• $10/mo undercuts Copilot's $10/mo by matching it — not a discount play, a parity play. The wedge is "same price, better tech."
• No usage-based pricing on completions — flat rate, like Copilot. Structurally important: a heavy user couldn't 10x the bill, so users with large monorepos (the people most hurt by Copilot's 2K context) had unlimited upside.
• No enterprise tier published — likely deliberate. Selling to enterprise as a single founder requires SOC2 + procurement + legal. Supermaven stayed in Indie / prosumer lane right up to acquisition.
• Implied unit economics: custom inference stack means GPU cost per completion is main variable expense. At ~250ms latency on fine-tuned smaller model, marginal cost w