Parlant vs Intercom Fin
Parlant vs Intercom Fin
Intercom Fin is Intercom's AI agent. You configure it in Fin's interface, and Intercom runs it on Intercom or another supported helpdesk.
With Parlant, you build your own agent by describing it to your coding agent, and everything it's made of is yours: its rules, its connections to your systems, the models it uses, and where it runs. Because your coding agent does most of the work, you get results faster than clicking through a settings screen, with more control and at lower cost.
If you're comfortable working with a coding agent and want more than Fin's settings allow, or want to stop paying per resolution, Parlant fits. If you'd rather not build with one, Fin does.
- Whether you need engineers to build and run a Parlant agent
- How changing and testing the agent works in each
- What each costs to run, with a worked example
- What you'd give up compared to Fin
These are the questions CX teams usually ask when weighing the two:
Fin is set up in an interface. Do I need engineers to build a Parlant agent?
No. You describe what the agent should do, which rules it follows, and which systems it should use, and your coding agent builds it. The docs are written so a coding agent can follow them, from the agent-ready quickstart to taking the agent live.
Fin is built in its interface: you write Procedures in natural language, connect data connectors, and test with simulations. Intercom hosts and operates the agent.
With Parlant, the agent is your own project. You describe what you want, and your coding agent writes its policies, connects your systems, and runs test conversations. Parlant provides the engine, chat widget, handoff to your team, analytics, and optional hosting. You check the result before customers see it.
How do I change the agent's behavior, and check a change before customers see it?
You describe the change to your coding agent, the way you'd brief a teammate: a new rule, a different answer for a situation, an exception. It updates the agent's policies, your test agent picks up the change on its own, and your coding agent can run test conversations against it and tell you what happened.
Then you test it in the Dashboard. A simulation is a test conversation with your real agent, so you can play the customer, including the awkward ones, and read what the agent did. When an answer surprises you, you can see which rules applied and what the agent did, so you know why it happened. When you're happy, you deploy it, and customers get the change.
Fin also lets you test with simulations before going live. The practical question is what you can change: in Parlant, any part of the agent; in Fin, what its configuration allows.
Fin has Procedures. How would I write the same thing in Parlant?
Both support multi-step procedures. Fin Procedures can include conditions, sub-procedures, code, data connector calls, and handoffs. They can also pause for human input or approval, depending on your helpdesk and configuration.
In Parlant, a procedure is a policy written in plain language, step by step, with the actions each step needs. The docs' refund procedure asks for the order number and looks it up, checks the 30-day window, asks the reason, asks where the refund should go, gets explicit confirmation, and only then processes it. It also says never to promise the refund before confirmation.
The difference shows when a conversation doesn't stay on one track. Parlant keeps every rule that's relevant in view at once, so a procedure step and your general rules ("acknowledge frustration first", "verify identity before sharing account details") apply together. When the customer adds a request halfway through or goes back to an earlier topic, the rules for that join in and the procedure picks up where it left off. For the steps that matter most, Parlant can check an action before it runs and check a reply before the customer sees it.
Fin connects to Shopify, Stripe, and our APIs. What can a Parlant agent connect to?
Fin's Procedures call data connectors for apps like Shopify and Stripe, custom systems over API, and webhooks the procedure can wait on. Code steps let you add custom logic.
A Parlant agent can use any system your code can reach: public services, your internal tools, databases, and older systems with no ready-made connector. One action can combine information from several systems, tidy it up before the agent sees it, or ask the customer to confirm before something consequential happens. Your coding agent builds and changes these connections for you.
You still need the right access: credentials, permissions, and someone who checks that each connection does what it should.
What does each cost to run, and what do I take on?
Fin's documentation describes custom models Intercom trained for customer service; it doesn't describe choosing your own. Fin is priced per outcome, such as a resolution or a qualifying procedure handoff. With an existing supported helpdesk, you pay Fin's outcome fees alongside your helpdesk's own costs. If you choose Intercom's helpdesk too, its seat charges are additional.
Parlant itself is free. You choose your model provider and pay it directly, with no markup, and you can switch models whenever a better or cheaper one comes out. The Dashboard, where you host, monitor, and analyze your agents, is a flat monthly fee per workspace, free for development; see pricing. Parlant also tracks what each conversation costs and lets you cap it. Extra checks on replies add model work and response time, only on the turns where you turn them on.
Choose your Dashboard plan for the projects, hosting, and conversation history you need. Session limits control how much activity you can view and analyze, and how long it stays available. They do not limit how many conversations your agent can handle. Your model provider continues to charge for usage beyond those limits.
A worked example. Suppose you handle 10,000 support conversations a month, and 8,000 result in a billable Fin resolution. At $0.99 per resolution, Fin's outcome fees would be $7,920.
To illustrate Parlant's model costs, suppose each conversation uses 5,000 input tokens and 1,000 output tokens across all model calls, including matching and reviews. At hypothetical rates of $1 per million input tokens and $5 per million output tokens:
| Text-model usage | Calculation | Cost |
|---|---|---|
| Input | 10,000 conversations × 5,000 tokens × $1 per million | $50 |
| Output | 10,000 conversations × 1,000 tokens × $5 per million | $50 |
| Total | $100 |
That $100 is a model-usage estimate, not a complete monthly bill or a measured workload. Add your chosen Dashboard subscription or self-hosting costs, plus any separate helpdesk and other services. Neither the conversation count nor this calculation determines which Dashboard plan you need.
For your own estimate, test representative conversations and use the actual billable token counts and provider rates, including caching and any non-text usage. Check Fin's minimum commitments and add-ons too, and test how well each agent resolves your requests before comparing their bills.
What you take on differs too. With Fin, someone configures Procedures and Intercom runs the agent. With Parlant, someone on your team owns the agent and checks its behavior, the coding agent fits Parlant to your business, and the Dashboard runs it with managed hosting. If you self-host instead, your team runs the servers.
Who owns the agent and our conversations, and what if we want to leave?
A Parlant agent is your own project under the open-source Apache 2 license. Its rules, connections, and approved wording are files you keep, and you can move them, host them yourself, or change providers without asking anyone. Conversations are stored on the Dashboard when it hosts your agent, or on your own servers when you self-host.
Because you pick the model, you can switch providers or models as prices and quality change, without rebuilding the agent.
With any closed platform, including Fin, ask how you'd export your procedures and conversation history if you moved, and what you'd need to rebuild.
What would I give up compared to Fin?
Mostly, what you give up is building without a coding agent. Fin is set up in Intercom's interface, uses models Intercom trained for customer service, and Intercom operates it. If you don't feel ready to build with a coding agent, that's the right trade, and Fin is a good choice.
If you are comfortable with one, you give up very little. Your team owns the agent: someone describes what it should do, checks its behavior, and keeps it current as the business changes. The coding agent fits Parlant to your business, connects your systems, and tests the result, which usually takes less time than configuring the same behavior screen by screen. You also choose your own models. In return, you're not limited to what a configuration screen offers, and you pay model providers directly instead of a fee per resolution.
How can I try Parlant on our service without a big commitment?
The Dashboard is free for development, and the quickstart has a prompt that walks your coding agent through setup with you. You can start from the example project or set up your own agent straight away.
Take one procedure your team already runs and build it in Parlant. Then test it with real conversations: a request that crosses two topics, a policy exception, a customer who changes their mind halfway through, and an action that fails. Read what the agent did and what it told the customer. If you can, run the same conversations against the same procedure in Fin.
Then ask for something neither supports out of the box, such as a lookup in an internal system or a step that depends on your own approval rules. Note what each one lets you do, who does the work, and how long it takes.
Fin's pricing and capabilities were checked against the linked official documentation on October 21, 2026. The token-cost calculation uses explicit hypothetical assumptions, not a measured workload. The capabilities described here are not a measured reliability ranking.