Runtime vs Building Your Own Agent Platform
Should a payment or fintech company build its own AI agent platform or buy one? What it takes to run mission-critical agents in-house, how long it takes, and where Runtime fits.
TL;DR: Build in-house if agents are your product and you can staff a senior AI platform team for two quarters. Choose Runtime if agents are how your operations teams get work done, and you need them on real payment data this quarter, in your cloud, with approvals and an audit trail.
| Feature |  Runtime | Building in-house |
|---|---|---|
| Guardrails ready for real data | Day one | Often two quarters |
| Team required | Your ops team plus a Runtime FDE | Several senior AI engineers |
| Isolated agent computers | Built in, bring your own sandbox | You build and operate them |
| RBAC, approvals, audit trail | Built in | You build them |
| Model and harness routing, fallbacks | Built in, any model | You build and maintain it |
| Runs in your cloud | Yes, or fully self-hosted | Yes |
| Full control of the code | Agent scripts live in your repos | Everything is yours |
| Works with agents you already built | Yes | Yes |
Most payment companies already have one internal agent. Someone on the engineering team wired a model to the ticketing system or the ledger, and it works on a good day. What they don't have is a platform for building and running many agents across the company, safely, on real money.
This page is for the COO or head of operations weighing that choice.
What "building it" actually means
One agent is a prompt, a few tools, and a loop. A platform that lets support, payment ops, finance, risk, and compliance run agents on payment data is a different project. It needs:
| Piece | Why payment teams need it |
|---|---|
| Isolated agent computers | Each run gets its own sandbox, so one agent can't see another's data or break production |
| Fallback sandboxes and multi-cloud | Agents keep running when a provider has an outage |
| Scoped, masked credentials | Agents act without ever seeing raw secrets |
| Role-based access | Risk agents and support agents shouldn't reach the same data |
| Approvals | A person signs off before anything moves money |
| Audit trail | Every query, tool call, approval, and cost, exportable for your sponsor bank or examiner |
| Harness and model routing | The right agent and model for each task, with fallbacks when a model provider goes down |
| Evals and cost controls | Know an agent is right before it scales, and what each run costs |
| PII and PCI handling | Card numbers and SSNs stripped from prompts and logs, sensitive work on models in your cloud |
None of these are hard in isolation. Together they're a product, and someone has to keep running it.
How they differ
Time to value
An engineering team typically spends two quarters building approvals, audit trails, and access controls before the first agent touches real data. Operations leaders feel the queue growing now, and the platform work competes with the product roadmap for the same engineers. With Runtime, all of it is there on day one, so the first project is your busiest queue, not the plumbing.
Who does the work
Building in-house needs a team of senior AI engineers who understand both agent infrastructure and payments. Runtime pairs you with a forward-deployed AI engineer who has built payment infrastructure at companies like Finix, Modern Treasury, and Hulu's payments team. They map your processes, build the first agents with your team, set up guardrails with security, and train your admins. Your team keeps using Runtime after the FDE leaves.
One platform, not one agent
Internal builds usually start as one team's agent. A single stuck payment touches four teams: support gets the ticket, payment ops traces it, finance sees a reconciliation break, and risk or compliance may review it. On Runtime, every team builds on the same harness and shares one memory, so that payment is one investigation with one record.
Control
This is where building wins on paper, and where Runtime closes most of the gap. Agent computers run in your cloud account or fully self-hosted. You bring your own models and sandbox provider. When agents solve a process, they write it as deterministic code stored in your repos, so engineers can take it over when it becomes mission-critical.
Where building in-house is stronger
- Agents are your product, not your operations tooling
- You have a dedicated AI platform team with spare quarters
- You need deep customization of the core runtime itself
- Your security policy rules out any vendor software, including self-hosted
Pricing
Building in-house costs the salaries of the platform team, plus cloud, model, and sandbox spend, plus ongoing maintenance as models and providers change.
Runtime has a free tier, Teams from $99 per seat per month, and custom Enterprise pricing that includes self-hosting. Most teams measure it against the analysts they were about to hire, not against other software. Rain replaced $250k in vendor spend with Runtime.
Which should you choose
Build in-house if
- Agents are core to the product you sell
- You can staff and keep a senior AI platform team
- No vendor software can run in your environment
Choose Runtime if
- Your operations queues are growing faster than headcount
- You need agents on real payment data this quarter, not next year
- Approvals and an audit trail your sponsor bank accepts are non-negotiable
- You want every team, not just one, building agents on the same platform
Skip the two quarters of plumbing
Bring one SOP. A forward-deployed AI engineer builds the first agent with your team, inside your cloud.
Frequently asked questions
Should we build or buy an AI agent platform?
Build if agents are your product and you can dedicate a senior AI platform team for several quarters. Buy if agents are how your operations teams get work done. Most fintechs buy the harness and keep their agent logic, scripts, and data in their own repos and cloud.
How long does it take to build an internal agent platform?
The first agent takes days. Making agents safe on payment data takes longer: isolated computers, scoped credentials, role-based access, approvals, an audit trail, evals, model routing, and fallbacks. Engineering teams commonly spend two quarters on that before the first agent touches real data.
We already built an internal agent. Can Runtime work with it?
Yes. Runtime runs alongside agents you already have, and your team can move them onto the harness when they need approvals, an audit trail, or access to more teams.
Do we lose control of our agents with Runtime?
No. Agent computers run in your AWS, GCP, or Azure account, or you self-host the whole platform. When a process is solved, agents write it as code in your repos, so your engineers own it.
Related comparisons
Runtime vs Gumloop
Runtime is the agent harness for payment teams: agents investigate cases in your cloud, with approvals and an audit trail. Gumloop is a visual AI workflow builder for general business automation.
Runtime vs Workato
Runtime is the agent harness for payment teams, built to investigate exceptions in your own cloud. Workato is an enterprise iPaaS adding AI agents on top of its integrations.
Runtime vs Bretton AI
Runtime is one agent harness for every payment team, from compliance to payment ops, finance, and support. Bretton AI builds AI agents and managed services for financial crime compliance.
Runtime vs Footprint
Runtime is one agent harness for every payment team, from risk to payment ops, finance, and support. Footprint is an identity and risk operations platform with AI agents for KYC, KYB, and fraud.