Velane vs Vurge: Features, Pricing & Which Is Better (2026)

A side-by-side comparison of Velane and Vurge — features, pricing, and ideal use cases — to help you decide which AI tool fits your workflow.

Velane

Freemium

Open-source integration infrastructure for AI agents — 800+ OAuth-connected APIs, sandboxed runtimes, and dev/staging/prod promotion via MCP.

800+ OAuth Integrations: One connection lets agents call Salesforce, Stripe, Slack, HubSpot, Notion, GitHub, Linear, Zendesk and hundreds more via the Nango catalog.
MCP-Native Interface: Agents connect to mcp.velane.sh and drive discovery, code generation, execution, and deployment through a single MCP server.
Bun & Python Sandboxes: Every invocation runs in an isolated ephemeral runtime, so agent code can be tested safely without touching production state.
Dev / Staging / Prod Environments: Promote workflows through three environments with agent-issued publish_snippet calls and stable versioned HTTP endpoints.
Shared Credential Store: One OAuth connection per provider is reused across every team member's agent — no secret ever appears in code.
Invocation Logs & Audit Trail: Per-tenant execution logs let agents call get_logs to debug failures and give teams a full audit history.
Role-Based Access: Invoke, manage, and admin scopes control what each teammate's agent is allowed to do.
Self-Host or Hosted: Run Velane on your own infrastructure under AGPL-3.0 or use the managed mcp.velane.sh endpoint.

Agent-Built Stripe→HubSpot Automations: An agent in Cursor writes a Bun workflow that reads Stripe customers and pushes them into HubSpot, tests it in dev, and promotes to prod in one conversation.
Solo Developer Shipping SaaS Integrations: A single developer wires up Slack, Notion, and GitHub actions without maintaining an OAuth backend.
Multi-Tenant B2B Agent Products: A team runs Velane per tenant so each customer's agent has isolated credentials, sandboxes, and audit logs.
Safe Refactors of Live Workflows: Deploy a new version of a workflow to staging, verify with logs, then roll to prod with instant rollback.
MCP-First Prototyping: Prototype an entire integration pipeline from an IDE chat without spinning up backend infrastructure.

Freemium

AI-powered web data extraction that integrates with Google Sheets to simplify and automate web research.

Google Sheets Integration: Operates inside Google Sheets (as an add-on or functions) to populate cells and ranges directly from web sources without leaving the spreadsheet environment.
Structured Extraction: Identifies and converts semi-structured web content (tables, lists, product details) into normalized rows and columns suitable for analysis.
URL-to-Table Parsing: Accepts URLs or page inputs and extracts tabular and key-value information automatically, reducing manual copy-paste and reformatting.
Data Cleaning and Normalization: Applies basic normalization and formatting (dates, numbers, trimming) so imported data is ready for filtering, sorting, and pivoting.
Bulk Processing & Pagination Handling: Processes multiple URLs or paginated content in batch to gather multi-page results into single spreadsheet views.
Workflow Automation: Enables repetitive research tasks to be run programmatically from the sheet (e.g., refresh, scheduled pulls) so data stays up-to-date.
AI-powered extraction of web data into Google Sheets
Direct integration with Google Sheets (marketed as a research buddy in Sheets)
Automates web-research and data-population workflows for spreadsheets
Designed for non-technical users to collect and organize web data inside Sheets

Market Research: Collect product attributes, prices, and availability from multiple e-commerce pages into a single sheet for competitive analysis.
Lead Enrichment: Extract contact details and company metadata from directory listings and import them into CRM-prep spreadsheets.
Content Research & Curation: Pull headlines, summaries, and author info from articles across sites to assemble editorial research lists.
Price Monitoring: Periodically extract pricing and stock data from supplier pages to track changes over time in a sheet.
Data Aggregation for Reports: Combine tabular data from varied web sources into consolidated sheets for visualization and reporting.
Academic or Competitive Intelligence: Gather structured facts and references from public web pages to support research and citations.
Market research and competitor data collection into spreadsheets
Lead generation and contact/public data aggregation
Content research and citations collection for reports
Automating recurring web-data collection tasks into Google Sheets