Your customers are already in Claude and ChatGPT. The real question is which of three integration paths fits your company, and what each one costs you in engineering quarters.
Before Bridge I was CPO at an enterprise SaaS. We had auth, subscriptions, RBAC, all of it working cleanly for the case we'd built for: human users logging into a dashboard.
Then customers started asking if their AI agents could use our product on their behalf. And nothing we had fit.
Agents don't navigate UIs. They don't read your docs. They don't know which endpoint to call for what. They need a discoverable surface, machine-readable schemas, agent-scoped auth, and rate limits that make sense when one customer's agent fires a thousand calls in a minute. That's a whole second stack sitting on top of the one we already had.
I scoped it honestly and the answer was depressing: months of infra work for something that wasn't our core product. So I left and built the layer I kept needing.
Since starting Bridge I've now had a version of that same conversation with dozens of founders and heads of product. Same surprise, same Jira ticket, same quarter that gets eaten. If you're staring at it right now, here's how I'd think about the decision.
For most of the last decade "AI integration" meant putting a chatbot on your marketing site. That's not what your customers want anymore. They want to use your product from inside the chat tool they already have open, which is usually Claude or ChatGPT.
The protocol question used to be hard. Now it isn't. OpenAI and Anthropic both shipped native support for the Model Context Protocol (MCP) in 2024 and 2025. Google followed. Cursor and the rest of the dev-tool ecosystem followed. MCP is "USB-C for AI": one open spec, one server, every client.
So the which protocol question has an answer: MCP. The harder question is how to expose your product through it. That's where the three paths split.
You write an MCP server in TypeScript, Python, or Go. You implement the JSON-RPC transport, define your tools, wire them to your API handlers, deploy it. On paper, a normal engineering project.
It is not a normal engineering project. Our CTO Tomer wrote a long post on what we learned shipping production MCP infrastructure, and the headline lesson is the one I keep watching other founders bounce off: OpenAPI-to-MCP conversion takes a day. The auth takes a quarter.
OAuth 2.1 plus PKCE plus the RFC 9728 metadata spec. Per-tenant credential vaults so the LLM never sees your upstream keys. Per-customer scoping so a tool call by Customer A never returns Customer B's data. Tool descriptions tuned for the model, because if you just auto-generate them from your OpenAPI summaries the LLM picks the wrong tool roughly half the time. Tool grouping done correctly, which sounds easy and isn't (Tomer wrote a on why the obvious approach fails).
And then the part nobody warns you about: MCP is a young protocol. The spec keeps moving. Anthropic and OpenAI implement it slightly differently. Whatever ships next will have its own quirks. You're committing to ongoing maintenance on infrastructure that isn't your product.
I'm not telling you don't do this. If your security team rules out third-party infrastructure, you build it. If the MCP server is your product because you're a dev-tools company and customers will inspect or extend it, you build it. We open-sourced our CLI for exactly those teams.
For everyone else the build costs ~2 to 4 engineer-months for v1 and a permanent line item on someone's roadmap after that. The question to answer honestly is whether that's the best use of your team's next quarter.
Before MCP existed, OpenAI shipped Actions, a way to bolt your API onto a Custom GPT using an OpenAPI spec. It still works. For a stretch in 2024 it was the fastest way to ship anything.
In 2026 it's a worse choice than it used to be, because it only works in ChatGPT. Build it and every customer on Claude, Gemini, or Cursor is locked out. The multi-tenant auth story is thinner than MCP's (you'll do glue work the protocol should have done for you), and you're committing to a closed platform where OpenAI controls distribution and the rules.
The one case where Actions still makes sense: your customers are 100% on ChatGPT Enterprise, your board demo is next Tuesday, and you treat it as a stopgap you'll replace with MCP in Q2. Otherwise skip it.
This is what most teams I talk to end up on after they price out Path 1. You bring the OpenAPI spec you already maintain. The platform brings the protocol plumbing: generated tools, tuned descriptions, OAuth, multi-tenant credential handling, hosting, per-customer observability.
I'll be straight about Bridge: this is the bet we made the company on, and we made it because I'd already lived Path 1 at my old job and didn't want anyone else to spend a quarter the way we did. The trade is straightforward. You give up some control over the implementation details, you get back the 2-4 engineer-months. For a B2B SaaS company where the AI integration is a feature of your product rather than the product itself, that trade is easy.
There are several vendors in this space now: us, Truto, MintMCP, viaSocket, a few others. The pitches sound similar. Where they actually differ, in my experience watching customers evaluate them: how well the tools are tuned for LLMs out of the box (default OpenAPI summaries aren't enough), whether each customer gets their own scoped OAuth flow or you're sharing a key, whether you can see per-customer tool calls and debug what your customers' agents are actually doing, and how the pricing is shaped (per call, per customer, per server). Pick on those. Every vendor in the space supports the protocol; that part isn't where the differences live.
One thing we got wrong early at Bridge: I underestimated how much tool description quality affects whether the integration actually gets used. We launched with descriptions auto-generated from OpenAPI summaries, and the LLM picked the wrong tool often enough that customers gave up after a few tries. Rewriting the descriptions for the model (not for the developer who wrote the endpoint) was the single biggest change we made in the first quarter. It's the difference between "they tried it once" and "they use it every week," and it's the thing no one talks about when they say "we'll just build the MCP server ourselves."
If you have hard compliance constraints, or your MCP server itself is the product, build it.
If you're ChatGPT-only and need a demo next week, ship Actions as a stopgap.
Everyone else, which is the majority of B2B SaaS, should evaluate managed gateways. The build-vs-buy math has tilted the same way it tilted for payments toward Stripe and identity toward Auth0 in the previous decade. The right question isn't "can our team build this?" Of course they can. The question is whether your next quarter is better spent on protocol infrastructure or on the product your customers actually pay for.
If Bridge fits, start free. The free tier covers 3 connected customers and 1k tool calls a month, which is enough to ship something real and put it in front of a customer this week. If it doesn't fit, our open-source CLI will at least save you the OpenAPI-to-MCP plumbing on your own infrastructure. We'd rather you ship the right thing on the right path than the wrong thing on ours.
If I'd had Bridge a year ago at my old job I wouldn't have left to build it. That's the bar we're holding ourselves to.
