Chargify
Billing & Revenue Management for B2B SaaS
Integrate the Chargify API with the Go API
Setup the Chargify API trigger to run a workflow which integrates with the Go API. Pipedream's integration platform allows you to integrate Chargify and Go remarkably fast. Free for developers.
Popular Chargify and Go Actions#
Run any Go code and use any Go package available with a simple import. Refer to the Pipedream Go docs to learn more.
Overview of Chargify#
The Chargify API enables seamless integration of subscription billing, management, and reporting functionalities. With Chargify, you can automate the creation and management of customer subscriptions, handle invoicing, apply taxes, and track analytics related to your billing processes. It's a powerful tool for businesses with recurring revenue models to keep their billing systems in sync with other business operations, reducing manual workload and increasing efficiency.
Connect Chargify#
import { axios } from "@pipedream/platform"
export default defineComponent({
props: {
chargify: {
type: "app",
app: "chargify",
}
},
async run({steps, $}) {
return await axios($, {
url: `https://${this.chargify.$auth.subdomain}.chargify.com/subscriptions.json`,
headers: {
"content-type": `application/json`,
"accept": `application/json`,
},
auth: {
username: `${this.chargify.$auth.api_key}`,
password: ``,
},
})
},
})
Overview of Go#
You can execute custom Go scripts on-demand or in response to various triggers and integrate with thousands of apps supported by Pipedream. Writing with Go on Pipedream enables backend operations like data processing, automation, or invoking other APIs, all within the Pipedream ecosystem. By leveraging Go's performance and efficiency, you can design powerful and fast workflows to streamline complex tasks.
Connect Go#
package main
import (
"fmt"
pd "github.com/PipedreamHQ/pipedream-go"
)
func main() {
// Access previous step data using pd.Steps
fmt.Println(pd.Steps)
// Export data using pd.Export
data := make(map[string]interface{})
data["name"] = "Luke"
pd.Export("data", data)
}