Supermetrics Alternatives for Agencies Moving to BigQuery

Supermetrics is where most agencies start. It’s fast to set up, familiar, and great for getting marketing data into Google Sheets or Looker Studio. But once you move past spreadsheets—and especially once you decide BigQuery is your source of truth—what used to feel effortless can start slowing you down.

For years, Supermetrics has been the go-to for ad-hoc reporting and client dashboards. In a warehouse setup, though, you’re not just “pulling a report.” You’re building repeatable data infrastructure: loading raw data, keeping history, handling schema changes, and supporting analysis that spans channels and time.

That’s where spreadsheet-first connectors tend to show their limits. Costs can become hard to predict, refreshes can fail at the worst times, and you may not have the control you assumed you’d get when you committed to a warehouse. If you’re making the BigQuery jump, evaluating a strong supermetrics alternative (or several) isn’t optional—it’s part of protecting your margins and your team’s time.

A BigQuery migration usually comes with a shift in mindset: “warehouse-native” pipelines that load, store, and model data reliably, instead of tools designed to populate cells on demand. Below, we’ll compare five options with an emphasis on predictable pricing, scalability, and control.

Why Agencies Are Outgrowing Supermetrics

For a lot of agencies, Supermetrics is the first “real” data tool they buy—and for spreadsheet reporting, it earns that spot. If your workflow is pulling last month’s performance into a Sheet to assemble a client report, it’s a solid standard.

But client expectations have changed. Attribution questions get more nuanced, historical context matters, and cross-channel analysis is no longer a nice-to-have. Spreadsheets eventually hit a ceiling, which is why many agencies centralize data in a warehouse like Google BigQuery.

That move—from Sheets to BigQuery—is where the cracks show. It’s not a knock on Supermetrics; it’s an architecture mismatch. Spreadsheet-first connectors are built to fetch a defined slice of data for a report. They aren’t designed to replicate full schemas, backfill history cleanly, or sustain the throughput needed for warehouse-grade pipelines.

When agencies try to stretch a spreadsheet connector into a warehouse pipeline, the same issues tend to come up:

1. Unpredictable pricing: Many connectors price on rows pulled, queries run, or usage tiers. In a warehouse workflow, you may need to reload history when attribution shifts or source data changes. Costs can jump quickly—and that hits agency margins.

2. Lack of schema control: Visualization-oriented tools often push you toward pre-aggregated data. In a warehouse, you usually want raw, granular tables so you can model them yourself. If the tool shapes the data before it lands in BigQuery, you lose flexibility.

3. Data ownership issues: Some platforms store data on their infrastructure before forwarding it to your destination. That adds compliance considerations and can introduce lock-in. A clean warehouse strategy typically means the data lives in your own Google Cloud project.

4. Manual refresh limitations: Spreadsheet refreshes and triggers can time out or fail as volume grows. Warehouses need automated pipelines with retries, monitoring, and resilience—without someone babysitting refresh buttons.

Spreadsheet-First vs. Warehouse-Native: What’s the Difference?

Before selecting a Supermetrics alternative, it helps to separate tools into two categories.

Spreadsheet-first connectors (like Supermetrics) act as a bridge. They pull specific metrics/dimensions from an API and deliver them into a destination for reporting. They’re optimized for selection—you choose exactly what you want—more than for ongoing, reliable replication.

Warehouse-native pipelines (like Weavely) are designed around ELT (Extract, Load, Transform). Their job is to load source data into your warehouse (BigQuery) in a consistent, automated way. They prioritize integrity, schema consistency, and operational reliability.

In practice, that difference shows up in day-to-day operations:

The Top 5 Supermetrics Alternatives for BigQuery, Compared

We looked at five common options using criteria that matter in an agency environment:

• BigQuery compatibility: How cleanly does it load into your project?

• Agency-friendly pricing: Does cost stay predictable as you add clients?

• Data control: Do you own the raw data, or does it live in a vendor layer?

• Ease of use: Can an analyst set it up, or do you need engineering support?

1. Weavely: The Warehouse-Native Pipeline for Agencies

Weavely is a Supermetrics alternative built for the gap agencies run into when they outgrow spreadsheet connectors but don’t want a generic ETL tool that’s priced and designed for product analytics or backend databases.

Why it works for agencies:

A lot of enterprise ETL tools price on “Monthly Active Rows” (MAR). Marketing data makes that tricky because platforms routinely revise historical numbers (attribution windows, delayed conversions, restatements). That can trigger large re-syncs and inflate bills. Weavely avoids the MAR dynamic with a predictable, client-based pricing model.

Key features:

• Full data ownership: Weavely streams data directly into your BigQuery project. The data remains yours even if you stop using the platform.

• Agency-centric pricing: Pricing is per client/source, not per row synced, so growth in spend and volume doesn’t automatically punish your unit economics.

• Pre-built schemas: Standardized schemas for major platforms (Google Ads, Meta, TikTok, etc.) so the data lands in BigQuery ready for use in Looker Studio or Power BI.

If your agency is dealing with brittle refreshes or hard-to-forecast data bills, it may be time to tighten up the foundation. See Weavely connectors and request a demo.

2. Funnel: The All-in-One Marketing Data Platform

Funnel takes a more “platform” approach. It ingests data from many sources, lets you map and categorize fields inside its UI, and then exports the result to destinations like BigQuery.

Pros:

• Transformation UI: The visual mapping is useful for non-technical teams (for example, standardizing campaign naming fields across channels).

• Connector library: Strong coverage, including many niche marketing sources.

Cons:

• Vendor lock-in: If your transformation logic lives inside Funnel, that logic doesn’t automatically travel with you if you leave.

• Store-and-forward architecture: Data typically sits in Funnel’s environment before it lands in your warehouse. For some agencies, that extra hop is a compliance or governance concern.

• Pricing: Often positioned at the premium end. If your goal is simply getting raw data into BigQuery so you can model in SQL/dbt, it can be more tooling than you need.

3. Adverity: The Enterprise-Grade Data Integration Platform

Adverity is built for enterprise teams and large organizations that want deep governance and advanced capabilities beyond extraction.

Pros:

• Augmented analytics: Features like anomaly detection and alerts (for example, flagging unexpected CPA spikes).

• Customization: Supports complex transformations and custom API work.

Cons:

• Overkill for mid-sized agencies: Powerful, but the learning curve and implementation effort can be significant.

• Pricing opacity: Often sold as annual contracts with enterprise-level pricing that may not fit independent agencies.

4. Fivetran / Stitch: The General-Purpose ETL Tools

Fivetran and Stitch (part of Talend) are broad ETL platforms. They’re not marketing-specific; they’re designed to move data from many kinds of systems into warehouses.

Pros:

• Reliability: Strong monitoring and engineering-grade operations.

• Breadth: Useful if you also need non-marketing sources (Salesforce, databases, support tools, etc.).

Cons:

• The “MAR” pricing trap: Marketing data volume and frequent historical restatements can drive re-syncs and lead to unexpected bills.

• Limited marketing context: They generally ingest what the API provides, without agency-specific conventions or workflows baked in.

5. Custom Scripts / DIY: The In-House Option

Some agencies consider building pipelines themselves—Python scripts, Singer taps, or bespoke integrations.

Pros:

• Infinite customization: You can build exactly what your agency needs.

• Zero license fees: No SaaS subscription line item.

Cons:

• Hidden maintenance costs: Marketing APIs change constantly. When fields deprecate or endpoints shift, your pipeline breaks and someone has to fix it.

• Hard to scale: Something that works for a handful of clients can collapse at 10x scale unless you build real retry logic, monitoring, logging, and rate-limit handling—basically, a product.

How to Choose the Right Supermetrics Alternative for Your Agency

This choice isn’t only technical. It affects how you staff, how you price services, and how predictable your delivery is.

A practical checklist to evaluate options:

1. Destination & ownership: Do you want data living in a proprietary layer (like Funnel), or do you want raw data stored in your own BigQuery project (like Weavely/Fivetran)? For long-term flexibility, owning the raw data usually wins.

2. Pricing model predictability: Look at your client mix. If you land a high-volume e-commerce account, does your data cost spike with it? If you’re protecting margins, be cautious with row-based pricing.

3. Agency-specific operations: Can you manage multiple clients cleanly—separate datasets, sensible permissions, and straightforward onboarding—without creating a pile of ongoing maintenance work?

4. Support & onboarding: When something breaks during a high-stakes period (like Black Friday), how quickly do you get help? Generalist ETL support queues can be slow for agency timelines.

Building a Stack for the Future

Moving agency reporting into BigQuery isn’t just a tooling upgrade. It’s the shift from manual reporting to building a durable data asset.

General-purpose ETL tools can be excellent, and all-in-one marketing platforms can make transformations feel easy. But they can also introduce two things agencies tend to dislike most: unpredictable costs (often driven by row-based pricing) and dependency on a vendor’s internal transformation layer.

The right supermetrics alternative should let you fully own your raw data while keeping costs stable as you onboard more clients.

At the end of the day, the goal is simple: stop maintaining fragile spreadsheet refreshes and start using your warehouse for what it’s good at—trustworthy history, cross-channel analysis, and repeatable reporting.

Ready to stabilize your data pipelines?

If you’re moving to BigQuery and want predictable costs with warehouse-native ingestion, book a Weavely demo and see what an agency-friendly pipeline looks like.