AI spend guardrails for n8n and agent workflows

Stop AI workflows from silently burning money.

Track token usage, monitor costs by workflow and node, and catch expensive automation mistakes before they become surprise bills.

Built for n8n teams using OpenAI, Claude, Gemini, and Apify.

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5,000 protected requests every month on the Free plan.

API keys encrypted before storage

GDPR-conscious architecture

Secure provider credential handling

Built for production n8n workflows

n8n Workflow source

Runaway spend

Blocked

Saved this run

$126.80

Budget state

Protected

Built for n8n teams

The hidden failure mode in AI automation.

AI workflows can loop unexpectedly, retry repeatedly, call expensive models, and generate hidden costs before anyone notices. Provider dashboards show usage, but they rarely explain which workflow, node, execution, or customer created the bill. FlowGuard fills that gap for n8n teams.

n8n n8n workflow Request leaves the workflow

FlowGuard Budget check + logging

API Provider Paid API call

Logged by workflow + node

Cache response returned

Budget protected

AI nodes multiply calls

One workflow can fan out across agents, tools, retries, and premium model calls.

Provider usage lacks context

OpenAI, Claude, Gemini, and Apify dashboards show totals, not workflow-level ownership.

Retry loops burn budget

Bad inputs, failed tools, and retry logic can turn automation into a cost incident.

Customer cost is hard to prove

Teams need to know which client, project, or workflow created the spend.

n8n-first

Built specifically for n8n workflows.

No rewrites, no SDK install, and no code changes. Route requests through FlowGuard, keep your existing workflow shape, and start seeing costs by workflow, node, provider, and execution.

OpenAI Claude Gemini Apify

No workflow rewrites

Keep your n8n automations intact and route provider calls through FlowGuard.

No SDK install

Use gateway endpoints and n8n credentials instead of adding application code.

No code changes

Most teams only change the provider endpoint and FlowGuard API key.

Workflow context travels with every request

FlowGuard receives workflow and node names so usage is attributed correctly.

Built for operational clarity

See exactly where AI money is spent.

FlowGuard turns provider usage into workflow visibility: cost anomaly alerts, node-level attribution, request logs, provider usage tracking, and budget monitoring in one place.

Per-workflow cost tracking

See spend, token volume, errors, and latency grouped by workflow, project, customer, or environment.

Per-node token attribution

Find the exact n8n node, agent step, or tool call responsible for a spike.

Execution-level tracing

Inspect the path, prompts, model calls, retries, and outcomes for costly executions.

Budget limits and auto-stop rules

Define daily, monthly, workflow, customer, or execution budgets with hard-stop behavior.

Anomaly detection

Detect unusual spend, token bursts, latency jumps, and error loops before they spread.

n8n-first integration

Use a FlowGuard node or gateway pattern designed for automation builders and agencies.

Dashboard preview

Workflow-level visibility, not just provider usage.

Track cost anomaly alerts, request logs, provider usage, and budget monitoring from the same dashboard your team uses to inspect expensive n8n executions.

Production workspace

Filtered by n8n workflows, agent executions, and customer attribution.

Spend today

$428.17

Tokens

4.8M

P95 latency

1.8s

Blocked runs

Hourly LLM cost

Budget healthy

Top workflows

Last 24 hours

Workflow	Cost	State
Support triage agent Customer ACME, 18 nodes	$142.90	Normal
Invoice extraction Operations, 9 nodes	$87.13	Normal
Research loop Sandbox, 31 retries	$74.02	Blocked
Lead enrichment Growth, 12 nodes	$52.71	Normal

How it works

Four steps from blind spend to enforceable control.

Connect FlowGuard node or gateway

Install the n8n node or point OpenAI-compatible calls at the FlowGuard gateway.

Route LLM calls through FlowGuard

Attach workflow, node, execution, environment, and customer context to each request.

Track cost, tokens, latency, errors

Analyze model usage across teams and troubleshoot expensive executions instantly.

Alert or block abnormal usage

Trigger notifications, soft warnings, or hard stops when budgets or anomaly rules fire.

Simple n8n setup

Protect your workflow in minutes.

Create a project, connect your provider, and create an n8n credential that points model calls through FlowGuard. Your requests stay familiar while FlowGuard adds tracking, cost attribution, and budget visibility.

No workflow changes. No SDKs. No code modifications. Most users can get started in under 5 minutes.

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Video guide

Watch the n8n setup walkthrough.

Follow the setup flow visually, from creating your FlowGuard project to routing n8n model calls through the protected gateway.

Embedded YouTube walkthrough

Create a project

Organize workflows, logs, budgets, providers, and client environments in one protected workspace.

Connect your provider

Add OpenAI, Claude, Gemini, or Apify credentials. Provider keys are encrypted before storage.

Create an n8n credential

Use the FlowGuard gateway as the provider endpoint and start monitoring requests.

Copy into n8n

Gateway URL patterns

Use Expressions mode

Choose the provider you use in n8n and paste the matching base URL. FlowGuard keeps the provider experience familiar while adding monitoring and budget protection.

Use these base URLs in the n8n chat model credential for OpenAI, Anthropic, or Gemini-compatible nodes.

Open the chat model credential

Choose the matching n8n OpenAI, Anthropic, or Gemini chat model credential.

Paste URL with Expressions

Use Expressions mode so workflow and node names are sent to FlowGuard.

Use your FlowGuard API key

Set the credential key to the FlowGuard API key connected to that provider.

OpenAI


                                    https://flow-guard.io/api/v1/openai/{{ $workflow.name }}/{{ $prevNode.name }}

Anthropic


                                    https://flow-guard.io/api/v1/anthropic/{{ $workflow.name }}/{{ $prevNode.name }}

Gemini


                                    https://flow-guard.io/api/v1/gemini/{{ $workflow.name }}/{{ $prevNode.name }}

Use these URLs in an n8n HTTP Request node with Authorization set to Bearer and your FlowGuard API key.

Actor

Run the selected Actor

Create an Apify Actor key in FlowGuard, then send a POST request from n8n. The JSON body is the Actor input.

Scrape

Fetch the configured scrape URL

Create an Apify Scrape key in FlowGuard, then send a GET request from n8n to return the real Apify response and log it.

Actor run


                                    https://flow-guard.io/api/v1/apify/{{ $workflow.name }}/{{ $prevNode.name }}/actor/run?waitForFinish=60

Scrape fetch


                                    https://flow-guard.io/api/v1/apify/{{ $workflow.name }}/{{ $prevNode.name }}/scrape/fetch

Keys stay protected

n8n calls FlowGuard, and FlowGuard calls the provider with your encrypted credential.

Spend gets attributed

Costs are tied back to the workflow, node, model, project, and API key.

Incidents become visible

Budget warnings and abnormal usage alerts help catch expensive loops early.

Outcomes

Use premium AI models with confidence.

FlowGuard helps teams move from blind provider invoices to clear cost ownership, early warnings, and better automation margins.

See exactly where AI money is spent

Break usage down by workflow, node, execution, project, customer, model, and provider.

Detect unusual spending early

Catch token bursts, retry loops, expensive model switches, and abnormal usage before they become billing surprises.

Use better models safely

Adopt OpenAI, Claude, Gemini, and Apify with budget visibility around each production workflow.

Protect automation margins

Know which client, workflow, or team created cost so pricing and usage stay accountable.

Agency operations

Built for n8n agencies.

One workflow mistake can erase profit. FlowGuard helps agencies understand client AI costs, identify expensive workflows, detect anomalies, monitor usage, and protect margins before a small automation issue becomes a client billing problem.

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Client attribution

See which client workspace, project, or API key generated each model request and cost.

Expensive workflow detection

Find the workflow and node responsible for a spike instead of guessing from a provider invoice.

Anomaly monitoring

Surface sudden cost increases, retry storms, and token bursts while the workflow is still running.

Provider usage tracking

Track OpenAI, Claude, Gemini, and Apify usage without giving every workflow direct provider credentials.

Margin protection

Use clear usage data to price retainers, review client automations, and stop runaway spend.

Case study

A runaway support workflow, stopped before it burned the month.

A support-ticket automation started retrying an LLM reply node after malformed input. FlowGuard attributed the spike to the exact workflow and node, enforced the budget rule, and gave the team a trace they could fix in minutes.

94%

projected overspend avoided

$1,126

estimated provider cost protected

17 min

from spike to blocked execution

1 node

identified as the cost source

Illustrative production-style scenario based on the kind of retry loop FlowGuard is built to detect and block.

Incident reconstruction

Support Ticket Classifier

Budget protected

Without guardrails

$1,284.40

With FlowGuard

$158.20

Protected spend

$1,126.20

1

Normal baseline

The workflow usually processed support tickets with predictable token volume and low model cost.
2

Retry loop detected

The Generate Reply node started repeating calls after a malformed customer payload.
3

Budget rule enforced

FlowGuard blocked the workflow, preserved the request trace, and surfaced the responsible node.
4

Cost recovered

The team patched the prompt input and restarted the workflow without losing the whole monthly budget.

Auto-stop rule

Block workflow when hourly LLM cost increases by 300%.

Triggered

Team

Built by people who run automation in the real world.

FlowGuard is shaped by hands-on n8n usage, backend engineering, data science, and machine learning work. The goal is simple: protect teams from expensive API mistakes without slowing down automation builders.

Small team, operator mindset

The first version focuses on the problems teams feel immediately: safe provider credentials, request attribution, cost visibility, and early warnings before a workflow burns money.

Founder-led

Amir Baghdadi

CTO & Founder of FlowGuard

Location Germany

n8n user LinkedIn

Amir is building FlowGuard from the same problems he sees as an n8n user and backend engineer: AI automations can become expensive, unclear, and hard to control once they reach production. He brings 10+ years of experience in scalable backend systems, monitoring, payments, clean architecture, data science, and machine learning to make FlowGuard a secure, practical gateway teams can trust with their provider keys, usage data, and budget protection.

Backend

10+ years

Automation

n8n + LLMs

DS / ML

Ehsan Afsharinejad

Team Lead

Location Germany

Execution LinkedIn

Ehsan keeps FlowGuard focused on the user experience behind the infrastructure. His role is to help turn complex gateway, logging, and cost-control workflows into clear setup paths, reliable product behavior, and practical safeguards that teams can understand before they route real production traffic through FlowGuard.

Focus

Delivery

Team

Coordination

Product

Operations

Amin Alizadeh

Senior DevOps Engineer

Location Portugal

Infrastructure LinkedIn

Amin brings senior DevOps experience across cloud infrastructure, deployment automation, observability, and production operations. Based in Portugal, he helps keep the platform reliable, secure, and ready for real automation workloads.

DevOps

Senior

Cloud

Infrastructure

Ops

Reliability

Trust

Production-minded security for provider credentials.

FlowGuard is built by Amir Hossein Baghdadi for teams that route expensive provider calls through production n8n workflows. The trust model is practical: encrypted keys, TLS-secured communication, clear legal pages, and a direct contact path.

Security page Privacy policy Terms Contact

Contact email: support@flow-guard.io

Built by Amir Hossein Baghdadi

Founder-led development with a focus on secure backend systems, n8n usage, payments, monitoring, and AI automation.

API keys encrypted before storage

Provider credentials are encrypted before they are stored, and workflows use FlowGuard API keys instead of direct provider secrets.

TLS-secured communication

Requests to FlowGuard are sent over TLS-secured connections before being routed to supported providers.

GDPR-conscious architecture

FlowGuard focuses on operational metadata, usage, cost, latency, status, and attribution so teams can minimize unnecessary prompt retention.

Pricing teaser

Start small, protect serious production workflows.

Start with 5,000 protected requests every month, then grow into higher limits and team controls as your n8n usage expands.

Free plan available

Free

Start protecting n8n AI workflows with the core monitoring layer included.

5,000 requests/month included

5,000 protected requests each month
Cache protection with 1 month expiration
Anomaly protection for runaway usage
Node, workflow, and log monitoring

Start Free

Pro

Everything in Free, plus higher monthly volume and stronger protection controls.

$20

50,000 requests/month

50,000 protected requests each month
Everything in Free is included
Unlimited cache retention for stable LLM responses
Auto-stop protection for runaway workflows

Start Pro

Team

For teams that need shared access, higher request volume, and faster support.

$50

150,000 requests/month included

150,000 protected requests each month
Everything in Pro is included
User management for team access
Priority support
Need more than 150,000 requests? Contact us

Start Team

Enterprise

For organizations that want a stable self-hosted FlowGuard version with long-term security coverage.

One-time

stable version plus 5 years of security updates

One-time payment for a stable release
Self-hosted deployment with support
Security updates included for 5 years
New feature work possible by agreement

FAQ

Questions teams ask before routing production AI through FlowGuard.

Does FlowGuard work with n8n?

Yes. FlowGuard is designed around n8n workflows and supports both a dedicated node approach and an LLM-compatible gateway approach.

Do we need to rewrite our LLM calls?

No. The gateway is designed for familiar provider-compatible requests, so existing clients can usually change the base URL and pass context metadata.

Can we avoid storing prompt content?

Yes. FlowGuard can focus on metadata, cost, token counts, latency, error state, and attribution while minimizing or disabling prompt retention.

Can it stop runaway workflows automatically?

Yes. Budget and anomaly rules can trigger alerts, soft limits, or hard blocks for workflows, customers, executions, or environments.

Protect the workflow layer

Start protecting your AI workflows today.

Get visibility into costs, workflows, nodes, and provider usage before small mistakes become expensive problems.

Start Free