The use case: how LaunchDarkly deploys Speakeasy
LaunchDarkly used Speakeasy to generate and maintain a production-ready MCP server from its existing OpenAPI specification, so customers' AI agents can take part in feature management workflows. Its own engineers also use the MCP server internally, for example to find and remove stale feature flags.
In the customer’s words
We realized that building and maintaining a production-grade MCP server could detract from our ability to focus on delivering our core business value. We wanted to partner with someone who could accelerate our time to market and minimize our overhead costs.Other AI tools used by LaunchDarkly
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Speakeasy users in DevTools & Infrastructure
Companies in the same sector, with public evidence.
KongConfirmed
PlanetScaleConfirmed
SolarWindsConfirmed
ClerkObserved
CloudinaryObserved
FireHydrantObservedGo straight to the source
Based on Speakeasy’s published case study. We check the public evidence; the results are reported by the vendor.
speakeasy.com- Last checked
- Nov 12, 2026
Open source capture What the story claims, and what we checked
What we compared with the page.
- The passage quoted aboveChecked
Copied word for word from the page, near the name of LaunchDarkly.
- LaunchDarkly uses SpeakeasyChecked
Confirmed line.
- The result itselfNot checked
We quote it; we did not measure it.
Speakeasy fit that model. LaunchDarkly could point Speakeasy at their existing OpenAPI specification and get a production-ready MCP server in return. When LaunchDarkly’s APIs change, the MCP server updates automatically.