Key outcomes:

• Reduced BigQuery cloud compute costs by up to 90%, ensuring they pay only for the data they actually use.
• Eliminated Supermetrics’ account-based pricing constraints to enable scalable and cost-efficient client growth.
• Saved up to 40 hours per week by automating historical data merging with upserts.
• Built an AI-ready infrastructure feeding clean data into LLMs for advanced client reporting.

Jared Skwiersky

Media & Analytics Innovation Lead, Grain Group

“We initially chose Windsor.ai to escape the Superemetrics’ cycle, where every new data source and account added another line item to our bill, but we renewed because of the platform’s sheer efficiency. Windsor’s pricing model is built to scale with us rather than at our expense, and it adapts to our technical stack with unique SQL-based automation tools that streamline the setup and maintenance of many simultaneous client accounts. Practically speaking, these in-platform workflows helped us cut our monthly BigQuery costs by up to 90%, further proving we can scale our data infrastructure without the usual friction or financial penalty.”

About the client

Grain Group is the top digital marketing agency in NYC, founded in 2010, operating at the intersection of technology, commerce, and performance media. Serving global brands across CPG, travel, luxury, wellness, and lead generation, the firm offers marketing strategy, digital media, analytics, CRM, and data solutions to help clients maximize measurable outcomes.

As certified partners of leading martech platforms, including Meta, Google, TikTok, LinkedIn, and The Trade Desk, Grain Group blends creativity with rigorous data discipline to build scalable, performance-driven media ecosystems for ambitious brands.

Challenges

For a high-growth agency like Grain Group, data is the lifeblood of client success. Managing a massive ecosystem, including Facebook, Google Ads, TikTok, Shopify, Amazon SP, HubSpot, Reddit, and other platforms, requires a pipeline that is as agile as their strategy.

Like many marketing agencies, Grain Group began its data integration journey using Supermetrics. However, as their client list and data complexity expanded, they realized they had outgrown the standard connector model. 

Particularly, the team encountered several structural constraints:

• Connector-based pricing friction: Adding new accounts or destinations meant navigating per-account billing, connector limitations, and additional fees.
• Manual BigQuery overhead: Reporting on dozens of accounts required time-consuming monitoring, merging datasets, and maintaining data integrity across historical records.
• Cloud compute inefficiencies: Data was being written and queried in ways that increased unnecessary BigQuery read costs.
• Scalability bottlenecks: The growing ecosystem required tighter SQL integration and warehouse-level control that traditional Supermetrics connector tools weren’t designed to support.

All in all, the manual overhead of managing dozens of accounts became an operational bottleneck, and the team needed a more automated, SQL-integrated way to handle their BigQuery environment without the friction of “per-account” pricing.

🏆 Learn more: Why marketing and data teams are switching from Funnel.io to Supermetrics.

Solutions

Grain Group migrated from Supermetrics to Windsor.ai to move away from rigid, per-connector billing and toward a platform built for high-volume automation. The switch allowed them to re-engineer their data flow through these strategic shifts:

1. A pricing model built for growth

In their previous setup, scaling often meant navigating a complex web of “connector taxes” and destination fees. Windsor.ai’s flat-rate model provided immediate financial clarity.

Grain Group can now add new clients, experiment with different data sources, or switch their destination from BigQuery to Snowflake at no extra cost. This predictability transformed their data stack from a variable expense into a stable, scalable asset.

2. From manual monitoring to automated upserts

Previously, the team had to spend hours manually organizing and merging new data into their old records to keep everything accurate. With Windsor.ai, this is now automated.

Grain Group now uses the Columns to Match feature to automatically blend new data into historical records. This creates a single, clean source of truth without the team having to do all the hard work manually.

3. 90% lower cloud compute costs

By implementing date-based partitioning within the Windsor platform, Grain Group fundamentally changed how data is written to its warehouse. They stopped paying for massive, inefficient data reads and started paying only for the data they actually used. Instead of reprocessing massive tables, only relevant partitions are updated.

4. The new technological partner: support built for agencies

The most significant shift following the migration was the level of technical collaboration. Grain Group got a partner that acts as an extension of their own team. Instead of navigating a standard support queue, they have direct access to the engineers building the connectors. This high-touch support allows them to resolve API nuances or request custom features in minutes, not days.

5. Future-proof data stack powered by AI

After a successful year of streamlined operations, the decision to renew for 2026 was a “no-brainer.” By moving beyond the limitations of their previous setup, Grain Group has built a future-proof foundation. They are now utilizing Windsor’s connectors to feed clean, structured data directly into LLMs like ChatGPT for advanced client reporting, staying ahead of the curve in an AI-driven market.

Modernised architecture

Here’s what Grain Group’s updated data architecture looks like:

• Data sources: Meta, Google Ads, TikTok, Shopify, Amazon SP, HubSpot, Reddit, and other marketing platforms.
• Windsor.ai ELT connectors: Automated extraction and schema-aware syncing across all client accounts.
• Destination → BigQuery: Partitioned, incremental data loads directly into warehouse tables.
• Automated upserts: Historical and new data are merged automatically using SQL-based matching rules.
• AI integrations: Structured data feeds directly into ChatGPT for advanced reporting and insight generation.

This architecture eliminates manual monitoring while optimizing both compute efficiency and scalability, and bringing in advanced AI-powered analytics.

Results

By migrating from Supermetrics to Windsor.ai, Grain Group achieved measurable improvements across cost, efficiency, and scalability:

• 90% reduction in BigQuery compute costs through partition-aware data loading.
• Eliminated connector-based pricing escalation, enabling client growth without increasing integration overhead.
• Automated multi-account warehouse workflows, reducing manual reconciliation and saving around 40 hrs/week.
• AI-ready infrastructure, enabling structured data delivery into LLMs for next-gen reporting and insights.

Final takeaway

By introducing Windsor.ai’s automated ELT platform into its workflows, Grain Group re-engineered its data engine for scale and innovation. The new architecture significantly reduced BigQuery costs, eliminated connector-based pricing friction, and introduced SQL-driven automation across dozens of client accounts.

Today, their infrastructure not only supports warehouse efficiency but also powers AI-driven reporting, positioning the agency for long-term, data-led growth in an increasingly AI-centric landscape.

🚀 Curious what Windsor.ai can do for your team?

Start your free 30-day trial: https://onboard.windsor.ai/, or book a demo: https://calendly.com/windsorai/guided-onboarding.