Introducing Arcade Deploy: Instant Hosting for your Custom AI Tools

Introducing Arcade Deploy: Instant Hosting for your Custom AI Tools

Jamie-Lee Salazar's avatar
Jamie-Lee Salazar
MARCH 25, 2025
2 MIN READ
COMPANY NEWS
Rays decoration image
Ghost Icon

Today we're launching Arcade Deploy, solving a critical challenge in AI development: how to quickly build, deploy, and iterate on custom tools that expand what your AI can do.

With Arcade Deploy, you use our SDK to create specialized tools, then deploy them instantly to our cloud with a single command: arcade deploy. Your tools become immediately available to your AI models in your agent or application—no servers to manage, no complex infrastructure to configure, no deployment pipelines to build.

Real-world implementation, not just demos

We’ve built a quick demo showing how to build a couple of custom tools on top of the Star Wars API that can look up details on Star Wars characters by planet or by name. If you’re working for Disney, that might be really helpful, but for most of our customers, what they really want to do is to connect to their own business systems.

Imagine creating tools that:

  • Connect to custom Salesforce objects to retrieve specific customer details during support calls
  • Access PostgreSQL databases to generate real-time inventory forecasts
  • Execute authenticated API calls to update records in internal systems
  • Extract structured data from unstructured documents in your knowledge base

Arcade Deploy hosts these integrations in a single command—your tools are instantly available in production without managing servers, containers, API gateways, or load balancers.

Practical advantages for AI tool developers

Rapid iteration

  • Deploy changes in seconds instead of hours
  • Test without managing infrastructure
  • Share instantly with teammates

Simplified testing

  • Automatic tool registration in the AI engine
  • Generated documentation in your dashboard
  • Managed message handling between tools and LLMs

Enterprise-grade infrastructure

  • Automatic scaling as usage increases
  • Load balancing across instances
  • Reliable uptime and monitoring

Getting started

Ready to transform how you build AI tools? Install the Arcade CLI, create your toolkit using our SDK, configure your workers, and run arcade deploy. That's it.

For full documentation and examples, visit our Arcade Deploy documentation.

Skip the DevOps, build tools that matter

Arcade Deploy lets you build what matters—the actual functionality your AI needs—without wasting time on deployment infrastructure. You'll spend more time coding useful features and less time fighting with cloud configuration.

Visit arcade.dev to sign up and try Arcade Deploy today.

SHARE THIS POST

RECENT ARTICLES

Rays decoration image
THOUGHT LEADERSHIP

Enterprise MCP Guide For Clinical Research Organizations (CROs): Use Cases, Best Practices, and Trends

Clinical Research Organizations face a critical infrastructure challenge: connecting AI systems to clinical trial data, regulatory platforms, and research databases without building custom integrations for every single connection. Model Context Protocol (MCP), introduced by Anthropic in late 2024, provides the standardized framework CROs need—but only when paired with an MCP runtime and production-grade multi-user authorization platform like Arcade.dev that handles the complex token and secret m

Rays decoration image
THOUGHT LEADERSHIP

Enterprise MCP Guide For Medical Devices: Use Cases, Best Practices, and Trends

Medical device manufacturers face a critical challenge: connecting AI agents to regulated systems without breaking HIPAA, FDA, or GxP compliance. Model Context Protocol (MCP) offers a standardized solution—but only when implemented with enterprise-grade security and multi-user authorization. Arcade's MCP runtime provides the MCP-compatible infrastructure that enables medical device companies to deploy AI agents with production-grade multi-user authorization, token and secret management, and the

Rays decoration image
THOUGHT LEADERSHIP

Enterprise MCP Guide For Biotech: Use Cases, Best Practices, and Trends

Your scientists spend significant time searching PubMed, patent databases, and internal documentation manually. Your AI agents can't access proprietary compound data. Every new AI integration requires weeks of custom development. Model Context Protocol (MCP) solves all three challenges by giving AI agents secure, governed access to the specialized data sources biotech R&D relies on—from literature databases to LIMS systems—through one standardized protocol instead of dozens of fragile custom con

Blog CTA Icon

Get early access to Arcade, and start building now.