Runtime vs Factory
Runtime is the agent harness for payment teams beyond engineering: ops, risk, compliance, and support. Factory builds Droids, AI agents for enterprise software engineering.
TL;DR: Runtime is the stronger choice for the operations teams around the code: payment ops, risk, compliance, and support, with engineering keeping control. Factory is a leading platform for autonomous software engineering.
| Feature |  Runtime |  Factory |
|---|---|---|
| What it is | Agent harness for payment and risk ops | AI agents for software engineering |
| Users | Ops, risk, compliance, support, plus engineering | Software engineers |
| Software engineering depth | Runs Claude Code, Codex, and OpenCode | Purpose-built Droids across the SDLC |
| Operations workflows | Core use case | Not the focus |
| Non-engineers build agents | Yes, from plain English and SOPs | Engineer-facing |
| Runs in your cloud | Yes, or fully self-hosted | Enterprise deployment options |
| Approvals on money movement | Built in | Not applicable |
| Payments domain depth | Processor codes, bank files, network rules | General software |
Two different jobs
Runtime is the AI agent harness for payment teams. From payment ops to risk, compliance, and support, anyone on the team can build agents that work their queues on their own computers inside your cloud, called from Slack, Teams, email, SMS, or voice, with approvals and a full audit trail.
Factory builds Droids, AI agents that implement, test, review, and maintain software across the development lifecycle. Large engineering organizations use them for migrations, code review, documentation, and incident response, from the IDE, CLI, Slack, or Linear.
Both are good products. The right one depends on the work you're trying to hand off.
Why payment teams choose Runtime
Built for money movement
Payment work is mostly exceptions: a held payout, an ACH return with an odd code, a dispute where the evidence contradicts the claim. Runtime agents reason through those cases on their own computers, across your ledger, processor, bank files, and tickets, and stop for a person before anything moves money.
Your data stays yours
Agent computers run in your AWS, GCP, or Azure account, or you self-host the whole platform. Card numbers, SSNs, and secrets are masked before any prompt or log, and you can serve open-weight models locally for PCI and PII work.
One harness for every team
Support, payment ops, finance, risk, and compliance all build on the same harness, so one stuck payment is one investigation, not four. Every run is stored with its evidence, approvals, and cost, ready when an auditor or sponsor bank asks.
An engineer, not an account executive
A forward-deployed AI engineer who has built payment infrastructure maps your processes, builds the first agents with your team, and sets up guardrails.
When Factory makes sense
- Your users are software engineers
- The work is code: features, migrations, reviews, incidents
- You want agents across the full development lifecycle
- Engineering productivity is the primary goal
When Runtime makes sense
- You run payments, banking, or lending operations and the queue keeps growing
- The work needs judgment on exceptions, not just a fixed sequence of steps
- Card data or PII has to stay in your environment
- You need approvals on money movement and an audit trail your sponsor bank will accept
- You want one platform for every team that touches a transaction
See Runtime on your busiest queue
Bring one SOP. A forward-deployed AI engineer builds the first agent with your team, inside your cloud.