Runtime vs Gumloop: AI Agents vs Visual AI Workflows

Feature	Runtime	Gumloop
What it is	Agent harness for payment and risk ops	Visual AI workflow builder
How work is defined	SOPs in plain English, agents reason through exceptions	Node-based workflows on a canvas
Solved processes become code	Deterministic scripts in your repos	Workflows on the canvas
Runs in your cloud	Yes, or fully self-hosted	Hosted platform
PCI and PII handling	Masking, local open models, data stays in your VPC	General data handling
Approvals on money movement	Built in, from Slack, Teams, or the dashboard	Build into each workflow
Audit trail	Every query, tool call, approval, and cost per run	Workflow run history
Payments domain depth	Processor codes, bank files, network rules	General purpose
Visual workflow canvas	Not the focus	Core product
Forward-deployed engineers	Included for rollouts	Self-serve and enterprise support
Pricing	Free tier, Teams from $99/seat/month	Credits by usage

Gumloop is the better fit for business teams that want to build visual AI workflows across general tools like CRM, docs, and email. Runtime is the better fit for mission-critical payment and risk operations, where agents run inside your cloud, reason through exceptions, and leave an examiner-ready record. Runtime is the AI agent harness for payment and fintech teams.

Gumloop and Runtime at a glance

Gumloop is a visual AI workflow builder. You drag nodes onto a canvas, connect apps, and add AI steps for things like research, enrichment, and content. It is backed by Y Combinator and Benchmark, and priced on credits that cover AI reasoning, workflow steps, and data services.

Runtime is the AI agent harness for payment and fintech 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.

Gumloop is for business teams automating general work on a canvas. Runtime is for payment and risk operations where the data is sensitive and mistakes move money.

How they differ

Canvas versus SOP

In Gumloop you design the flow node by node, so the workflow is only as good as the paths you drew. In Runtime you hand the agent your SOP in plain English. It follows the procedure, and when a case doesn't fit, like a return with an unexpected code or a payout held for an unclear reason, it investigates instead of failing.

From agent to deterministic code

When a case type is solved, a Runtime agent can write the process as a script that runs deterministically as a template. The script lives in your repos, not in a canvas on someone else's platform. If the process becomes mission-critical, your engineers review and own it like the rest of your codebase.

Where the data lives

Gumloop runs as a hosted platform. Runtime agent computers run in your AWS, GCP, or Azure account, or fully self-hosted. Card numbers and SSNs are stripped from prompts and logs, credentials are masked, and you can run open-weight models in your cloud.

Controls built for money movement

Approvals before anything moves money are built in, not added to each workflow by hand. Every run is recorded with the trigger, every query and tool call, the approval, the cost, and the result, so the record your sponsor bank asks for is already there.

Where Runtime enters the picture

Runtime is the AI agent harness for payment and fintech teams. Gumloop leaves the work where data is sensitive and mistakes move money. Teams that want agents there either build a platform themselves (isolated computers, scoped credentials, RBAC, approvals, an audit trail, model routing with fallbacks, often two quarters of engineering) or use a harness built for operations.

Take a reconciliation break between the settlement file and the ledger. On Runtime a finance agent matches the files in your cloud, traces each unmatched item to a refund, fee, or ACH return, and drafts the adjusting entries for a controller to approve. When the pattern is solved, it becomes a deterministic script in your repos.

No single vendor to depend on. Agent computers run in your AWS, GCP, or Azure account or fully self-hosted, on your own sandbox provider with fallbacks. Models route across Claude, GPT, Gemini, and open-weight models, and harnesses across Claude Code, Codex, and OpenCode, so one provider's outage, price change, or model deprecation doesn't stop your agents.
Guardrails you set. Read-only by default, approvals before money moves, RBAC, masked credentials, card numbers and SSNs stripped from prompts and logs, and every run recorded end to end.
Built for teams. Support, payment ops, finance, risk, and compliance share agents, templates, and one memory, so a stuck payment is one investigation with one record.
An engineer, not an account executive. A forward-deployed AI engineer who built payment systems at Hulu's payments team at Disney, Finix, Modern Treasury, or BlackRock maps your processes and builds the first agents with your team.

New agent

Create a banking ops agent that handles RFIs, hold-harmless requests, and returns of funds from our partner bank, verifies against our ledger, and drafts replies for my approval.

Describe the agent you want

Agent ready

bank-ops-agent

Tools picked from your prompt

EmailLedgerSFTPMastercard

Anyone on the team describes the work in plain English and attaches the SOP. Runtime builds the agent and gives it its own computer.

Where Gumloop is stronger

Visual building. The canvas is the core product, and it's quick to learn.
General business workflows. Research, enrichment, and content work across CRM, docs, and email.
Self-serve. Teams can start building without a rollout.
Usage-based credits. Pay for what workflows consume.

Which should you choose

Choose Gumloop if

You want a visual canvas to build AI automations quickly
Your workflows touch general business apps like CRM, docs, and email
The work is well defined and rarely needs judgment on exceptions
You are comfortable with a hosted, credit-based platform

Choose Runtime if

Your queues are payment, risk, or finance exceptions that need investigation
Card data or PII has to stay in your cloud
You need approvals on money movement and an examiner-ready record
You want solved processes turned into code your engineers own

See Runtime on your busiest queue

Bring one SOP. A forward-deployed AI engineer builds the first agent with your team, inside your cloud.

Book a demo

Frequently asked questions

Is Gumloop good for fintech?

Gumloop is a good fit for visual AI workflows across general business apps like CRM, docs, and email. Payment operations usually need data to stay in your cloud, approvals before money moves, and an audit trail a sponsor bank will accept, which is what Runtime is built for.

Can Gumloop run in my own cloud?

Gumloop is a hosted platform. Runtime runs agent computers in your AWS, GCP, or Azure account, or fully self-hosted, and lets you serve open-weight models in your cloud for PCI and PII work.

What is the best Gumloop alternative for payment teams?

For research, enrichment, and content workflows, Gumloop works well. For held payouts, returns, disputes, and recon breaks that need investigation across your ledger and processor, Runtime is the better fit.

How is Gumloop priced compared to Runtime?

Gumloop is priced on credits that cover AI reasoning, workflow steps, and data services. Runtime has a free tier, Teams plans from $99 per seat per month, and custom Enterprise pricing for self-hosting.

Where does Runtime fit next to Gumloop?

Gumloop builds visual AI workflows on a hosted platform. Runtime runs agents on payment and fintech operations cases, like held payouts, ACH returns, and recon breaks, in your own cloud with approvals before money moves and every run recorded. Teams can use Gumloop for general business workflows and Runtime for work that touches money.