Everything you need.
Nothing you don't.

A grid of features. Read top-to-bottom — pricing tiers labeled inline.

GPU-rendered grid

Virtualized, 1M rows, transform-only scroll. The grid is a Metal/DirectX/Vulkan surface, not a DOM table.

scroll fps · last 3 s ● 60.0 fps

rows

1,000,000

frame

16.6 ms

mem

184 MB

Native on every desktop OS

Same Rust binary class on macOS, Windows, Linux. Identical shortcuts. No 'we ported it' second-class citizens.

macOS

12+ · arm64 · x86_64

→ Metal

Windows

10+ · x86_64

→ DirectX 12

Linux

GNOME · KDE · Wayland

→ Vulkan

Bring-your-own AI

PRO

Pro unlocks the integration layer. Plug in Ollama, the claude CLI, an Anthropic / OpenAI key, or any MCP server. AI usage is billed by your provider — never by zolt.

top 10 repeat customers in q1 via ollama · llama3.1

-- preview · press ⌘⏎ to insert
select u.email,
       count(*) as orders
from orders o
join users u on u.id = o.user_id
where o.placed_at >= '2026-01-01'
group by 1
order by orders desc limit 10;

SSH tunneling, built-in

Define a bastion once. zolt opens, holds, and tears down the tunnel per session. No PuTTY, no autossh dance.

your laptop

bastion

postgres

SSH (key-auth, ed25519) TLS 1.3

Keychain-only credentials

Passwords live in macOS Keychain, Windows DPAPI, or libsecret. Never on disk in plaintext. Ever.

analytics-prod

postgres · admin@db-1.acme.io

password = ••••••••••••

● keychain · macOS

E2E-encrypted team sharing

TEAM

Connection blobs are sealed with age to each member's Ed25519 key. The sync server holds ciphertext and nothing else.

you

teammate

age-encrypt(blob, ed25519:rae)

→ -----BEGIN AGE ENCRYPTED…

Schema diff & DDL gen

Diff two schemas, generate the migration, paste into your repo. No external diff tool.

+ ALTER TABLE users ADD COLUMN tier text;

CREATE INDEX users_email_lower

~ ALTER COLUMN orders.total TYPE numeric(12,2);

− DROP TABLE legacy_sessions;

4 changes · postgres · ready to copy

MCP server mode

PRO

Run zolt as a Model Context Protocol server. Claude Desktop calls query_database and describe_schema directly. Tool-use trace, not a chat about your data.

→ tool_use: describe_schema(connection: "analytics-prod")

← 14 tables, 86 columns

→ tool_use: query_database(sql: "select count(*)…")

← 1 row · 12 ms · returned to claude

● mcp.local:7841 · 2 tools registered

Three things you couldn't do this morning.

zolt — capture 1/3 · palette

monthly active users by week ⌘K

Generate SQL · MAU by week ollama · llama3.1

Open dashboard

Run last query again

-- generated · review before run
select date_trunc('week', started_at) as wk,
       count(distinct user_id) as maus
from sessions
where started_at >= '2026-01-01'
group by 1
order by wk;

events · 1,000,000 rows scanned · scroll position 423,118 ● 60 fps

event

payload

user

423,118

page_view

{"path":"/p/0"}

14:00:0

u_23s6

423,119

click

{"path":"/p/73"}

14:01:7

u_23s7

423,120

sign_up

{"path":"/p/146"}