VS Code VS SciFig

SciFig's answer:

SciFig is the only AI scientific illustrator that combines six input modes (text, sketch, reference image, PDF, photo, mixed) with editable vector output across eleven scientific disciplines. Unlike template platforms that lock you into a fixed asset library, SciFig generates one-of-a-kind figures from your description or source material. Unlike raw generative AI tools that bake mistakes into pixels you can't fix, every label, arrow, molecule, and layer in a SciFig figure stays editable in a built-in canvas — and exports as layered SVG, native PPTX, or 8K PNG/JPG ready for journal submission. It's the only workflow that takes you from a blank page or a PDF straight to a publication-ready, fully editable scientific figure without an Illustrator round-trip.

SciFig's answer:

1.It works outside biology. Tools like BioRender are excellent for cells and organelles, but break the moment you need a semiconductor band diagram, a catalytic cycle, a satellite orbit, or a battery cross-section. SciFig generates figures from your description across eleven disciplines — biology, chemistry, physics, engineering, materials, energy, ecology, bioengineering, astronomy, agriculture, and computer science.

2.The output is editable, not flat. Every shape, label, and arrow exports as a separate object in SVG and PPTX. When a reviewer asks you to change a wavelength, swap an enzyme, or re-route an arrow, it's a 10-second fix — not a full regeneration.

3.It's affordable and risk-free to try. 200 free credits, no credit card required. Paid plans from $12/month (annual) — a fraction of BioRender's per-seat pricing — with full publication and commercial rights, tax-compliant invoices for grant reimbursement, and zero AI training on your uploads.

SciFig's answer:

Working researchers who need to produce publication-ready scientific figures regularly: PhD candidates, postdocs, principal investigators, lab managers, R&D teams in pharma and biotech, science communicators, journal editors, and university educators. The shared problem is the same across all of them — they have deep domain expertise but limited design time, and a single reviewer comment can cost them an entire day of redrawing in Illustrator. SciFig's secondary audience includes graduate students preparing thesis figures, grant writers building proposal visuals, and conference presenters producing posters and slide decks under deadline pressure.

SciFig's answer:

SciFig started from a frustration anyone who has submitted a paper knows well: you spend weeks on the science and then lose a full day rebuilding a figure because a reviewer wants one label changed. The existing options forced a bad trade-off — template tools like BioRender locked you to a fixed asset library, while general AI image tools produced beautiful but uneditable pixels with hallucinated proteins, mislabeled enzymes, and arrows pointing the wrong way.

We built SciFig around a different premise: AI should get researchers 99% of the way there in seconds, but the final 1% — the labels, the arrows, the scientific accuracy — has to stay in the expert's hands. That's why every figure SciFig generates exports as editable vectors, why we support PDF and sketch inputs alongside text, and why we cover eleven disciplines instead of locking the tool to one. The goal isn't to replace the researcher's judgment. It's to make sure they spend their time on the science, not on Illustrator.

SciFig's answer:

SciFig's web app is built on Next.js 15 (App Router) with React 19 and TypeScript on the frontend, deployed on Vercel. Authentication runs on better-auth, payments through Stripe, and the database is Postgres on Neon with Drizzle ORM. File storage and figure assets live on Cloudflare R2. Internationalization is handled via next-intl. Styling is Tailwind CSS with a custom design system, and observability runs on Sentry. The AI generation pipeline integrates multiple specialized models — Google's Nano Banana family for figure generation.

SciFig's answer:

SciFig is used by individual researchers and labs across leading research institutions worldwide. Users include researchers and groups affiliated with:

Stanford University Massachusetts Institute of Technology (MIT) California Institute of Technology (Caltech) ETH Zürich University College London (UCL) Cell Reports Methods (editorial staff) (Note: SciFig is currently focused on individual researchers and lab-level adoption rather than enterprise contracts. If a directory requires an enterprise-style "logo wall," consider replacing this section with "trusted by researchers at top universities worldwide" — most directories accept this framing for early-stage research SaaS.)