Zapier's Brex integration supports triggers like new transactions and new expenses, and the Google Sheets action for appending rows is rock-solid. For a simple log-every-transaction use case, a Zap can be live in under ten minutes with no technical knowledge required.

The problem surfaces when you need conditional logic — for example, only logging transactions above a certain amount, or routing transactions to different Sheet tabs based on department. Zapier's filter and path features handle simple cases, but multi-branch logic becomes unwieldy fast, and each action step counts against your task quota, which adds up quickly for high-volume card programs.

Make's router module allows a single incoming Brex transaction webhook to fan out into multiple branches — log it, check it against a policy table, send an alert if flagged, and update a budget counter row — all within a single scenario. The iterator and aggregator modules are genuinely useful for the monthly reconciliation use case, where you need to group and sum transaction rows before writing summary data back to a Sheet.

Make's pricing is operation-based rather than task-based, which is meaningfully cheaper for scenarios that process dozens of transactions per day across multiple steps.

Brex's API exposes considerably more data than what Zapier or Make surface through their native integrations — things like detailed budget hierarchy data, card program settings, and granular transaction metadata. n8n's HTTP Request node lets you hit any Brex API endpoint directly, which means you can build vendor spend trend workflows or budget utilization syncs that pull exactly the fields you need. The tradeoff is setup complexity: you'll need to handle OAuth token refresh, pagination for large transaction sets, and error handling yourself.

Self-hosted n8n also requires infrastructure management, though n8n Cloud removes that burden at a cost comparable to Make's paid tiers.

There is no native Brex connector in Power Automate's connector library, which means you're building all Brex interactions through the HTTP connector with manual OAuth configuration. That's a non-trivial setup task.

On the Google Sheets side, the connector exists but is less capable than the native integrations available in Make or Zapier — some advanced row operations require workarounds. If your finance team uses Excel Online rather than Google Sheets, Power Automate becomes a much stronger choice, but for the Google Sheets pairing specifically, it's the weakest of the five platforms.

Pipedream's Brex and Google Sheets integrations are both well-maintained with pre-built actions for common operations, and the ability to drop into Node.js or Python at any step makes complex data transformation straightforward. For the vendor spend trend use case — where you need to aggregate, deduplicate, and reshape transaction data before writing it to Sheets — Pipedream's code steps are cleaner than wrestling with Make's aggregator or Zapier's formatter.

Pipedream's free tier is generous for low-volume use, but the credit-based pricing model on paid plans can be hard to predict for high-frequency transaction triggers.

Brex delivers transaction webhooks in near-real-time, but there can be delays of several minutes for certain transaction types, and webhook delivery is not guaranteed to be exactly-once — your Google Sheet logging automation should be idempotent, ideally checking for a transaction ID before appending to avoid duplicate rows. Google Sheets itself introduces a secondary gotcha: the API has a write quota of 300 requests per minute per project, which is rarely a problem for normal use but can cause failures if you're processing a large batch of transactions simultaneously.

Building in retry logic and staggering batch writes — something Make and n8n handle more gracefully than Zapier — will save you debugging headaches down the line.