Go Fig vs. Custom Data Pipelines (Fivetran, dbt, Snowflake)

The Comparison at a Glance

Factor	Custom Data Pipelines	Go Fig
What you end up with	A warehouse and a dbt project	Connected, reconciled data in Excel with Celeste on top
Who does the finance work	Still you, on top of the warehouse	Celeste, on demand
Time to first reconciled answer	3-6 months plus ongoing model work	Under 30 days
Ongoing maintenance	20-40% of build effort, forever	Managed as a service
Technical requirement	Data engineers, analytics engineers	Senior analyst onboarding, no data team
Cost structure	Fivetran + warehouse + dbt + headcount	Predictable subscription
Knowledge risk	Concentrated in the person who built it	Owned and maintained by Go Fig

What a Custom Pipeline Stack Looks Like

The modern data stack typically means:

• Ingestion: Fivetran, Airbyte, Stitch, or custom scripts
• Storage: Snowflake, BigQuery, Redshift, or Databricks
• Transformation: dbt, Dataform, or custom SQL
• Orchestration: Airflow, Dagster, Prefect
• Visualization: Tableau, Looker, Power BI

Assembled well, it can move a lot of data. What it does not produce is one governed, reconciled model loaded into the Excel your team uses, or an AI analyst on top of it. Those are still the finance team’s problem to build or staff.

Where Custom Pipelines Make Sense

A build makes sense when:

• Data is a core competency: Infrastructure is strategic to the business itself
• You already have the team: Dedicated data engineers and analytics engineers on staff
• Requirements are unusual: Sources or transformations that don’t fit finance-shaped patterns
• Scale demands it: Volume or performance needs that are genuinely unusual
• You’re already invested: Existing infrastructure you want to extend

For most mid-market finance teams, none of these are true.

The Hidden Costs of the Build

Teams consistently underestimate total cost of ownership:

Initial build (30% of effort):

• Design the data model
• Build and test integrations
• Write transformation logic
• Stand up monitoring

Ongoing maintenance (40% of effort):

• Fix pipelines when source systems change
• Scale infrastructure as data grows
• Evolve dbt models as the business changes
• Handle edge cases and data quality drift

Knowledge management (30% of effort):

• Document pipelines and models
• Onboard new team members
• Manage key-person dependencies
• Absorb turnover in the data team

A common pattern: the engineer who built it leaves, institutional knowledge goes with them, and the finance team ends up with a brittle warehouse and a shrinking pool of people who understand it.

What a Finance Team Still Doesn’t Have After the Build

Even a well-run modern data stack produces:

• Clean tables
• Dashboards
• Ad-hoc SQL capacity

It does not produce:

• A governed entity, account, and segment model that finance trusts, loaded into the Excel they already use
• Segmentation of margin on demand
• Reconciled numbers that trace back to the source transaction
• An AI analyst that actually does the analysis

That layer still has to be built, staffed, or abandoned. Most finance teams end up with a warehouse and a backlog.

How Go Fig Differs

Go Fig is a managed layer purpose-built for finance teams:

Purpose-built for finance: The model understands entities, accounts, segments, and the relationships between them, reconciled and traceable to source. It isn’t a generic warehouse.

White-glove setup: A senior analyst stands up integrations, mappings, and the first reports. You don’t hire engineers.

Celeste on top: Segmentation, reconciliation, variance diagnosis, and reporting run on demand, grounded in your connected data. Your financials stay in your environment, never pasted into a public chatbot, never used to train an outside model.

Managed service: Integrations, mappings, and connections stay current as source systems change.

Where your team already works: Results land in Excel, Slack, and AI surfaces, not locked in a warehouse UI.

The Build-vs-Buy Decision

Build a custom stack if:

• Data engineering is strategic to your business
• You have unusual requirements off-the-shelf can’t handle
• You already have 2-3 data engineers you want to keep utilized
• Long-term infrastructure optimization matters more than time-to-value

Choose Go Fig if:

• You’re a finance practitioner, not a data platform owner
• You need reconciled, margin, and segmentation answers in weeks
• You don’t want to hire or retain data engineers
• You want connected, reconciled data in Excel with Celeste on top, not a warehouse and a backlog
• Predictable cost and managed maintenance matter

Hybrid Approaches

Some organizations run both:

• Go Fig for the connected, reconciled data and Celeste the finance team relies on
• Custom pipelines for product analytics, data science, or engineering use cases

This lets finance move fast on a managed layer while engineering builds specialized infrastructure for its own needs.

Total Cost of Ownership

Custom stack (illustrative):

• Fivetran: $2,000-$10,000/month
• Snowflake: $3,000-$20,000/month
• dbt Cloud: $1,000-$5,000/month
• Data engineer(s): $150,000-$200,000/year each
• Opportunity cost while the build is in flight

Go Fig:

• Predictable subscription
• Implementation included
• Ongoing maintenance included
• No additional headcount
• Connected, reconciled data and Celeste, not just pipelines

The Bottom Line

A custom modern data stack produces a warehouse. A warehouse does not answer margin or segmentation questions, and it doesn’t reconcile your numbers or load them into the Excel your team uses. Go Fig connects and reconciles your systems and gives you Celeste, an AI analyst, that does, in weeks, without a data team.