Evaluating Stripe Ingestion Frameworks: Our Process, Benchmarks, and Findings

At Database Tycoon, we recently set out to determine the most effective way to ingest Stripe data into our analytics stack. With many frameworks available—each with trade-offs in performance, flexibility, cost, and developer experience—we decided to run a practical comparison to help guide our next steps. Here’s a look at the tools we tested, our approach to evaluation, and the findings we discovered.

What We Evaluated

We looked at six tools in total: Fivetran, Airbyte, Meltano, Estuary Flow, dlt, and Stripe Sigma (though Sigma is not an ingestion tool in the traditional sense). Each option was evaluated across several dimensions: Stripe connector availability, support for real-time capabilities, whether transformations or raw tables were supported, open-source licensing, flexibility and customizability, pricing structure, and overall marketing or ecosystem fit.

Fivetran offers a built-in Stripe connector with strong credibility and dbt package support. However, it only supports batch ingestion, is not open-source, and doesn’t allow for custom sync logic. Its pricing is usage-based (based on Monthly Active Rows), and while it’s enterprise-ready, it’s a closed system with limited flexibility.

Airbyte also supports Stripe with batch-only ingestion. It is open-source, supports dbt, and provides raw data access with high flexibility. You can self-host it for free or use their cloud offering. It’s popular among developer-first teams but lacks real-time capabilities.

Meltano, which relies on Singer taps for Stripe, shares similar pros and cons with Airbyte. It’s open-source, dbt-friendly, and highly customizable, with a plugin architecture that suits DIY engineering teams. Real-time support is limited.

Estuary Flow supports both real-time and batch ingestion for Stripe. It’s open-source at its core, focuses on ELT workflows (rather than built-in transformations), and integrates well with dbt downstream. It’s usage-priced and positions itself well for real-time Stripe analytics.

dlt offers real-time and batch ingestion, a verified Stripe source, and native support for Python transformations and dbt. It’s fully open-source, highly customizable, and designed with developer control in mind, making it one of the strongest fits for modern, modular data stacks.

Stripe Sigma is not an ingestion framework. It runs SQL directly within the Stripe interface, making it useful for in-product analysis but not suitable for external pipelines, unified dashboards, or cross-tool workflows. It is proprietary, pay-per-query, and lacks any kind of external customization.

To dive deeper, we also reviewed dlt’s verified Stripe source and Stripe’s API reference documentation. dlt supports a wide range of endpoints, including Subscriptions, Customers, Invoices, Balance Transactions, Products, Prices, Events, Coupons, and Accounts. This gave us confidence in the breadth of Stripe’s model coverage and how flexible it can be if custom endpoint logic is required.

Benchmark Test: TPC-H Dataset

To test ingestion speed and performance, we used the industry-standard TPC-H dataset. The dataset included approximately 6.42 GB of data, 24.6 million rows, and 7 relational tables, representing a realistic ingestion workload for analytics teams.

We tested the following versions: Meltano v3.5.4, Airbyte v1.1.0, dlt v1.2.0, and Sling v1.2.2. These benchmarks weren’t meant to crown a winner, but to give us a sense of how each tool handles large, relational datasets under real-world conditions.

Technical Notes

Some key differences stood out during implementation and testing. Fivetran was by far the easiest to set up, offering a plug-and-play experience, but that came at the cost of customization. Airbyte and Meltano gave us raw data access and flexibility, which we appreciated, but neither supported real-time ingestion. Estuary Flow offered real-time ingestion out of the box and included helpful developer tooling. dlt stood out for being fully open-source, Python-native, and dbt-compatible. It gave us full control over pipeline logic while still feeling approachable to implement.

So, What Do You Think?

We’ve made our comparison, tested ingestion speeds, and explored developer experience across multiple Stripe data tools—but we’d love to hear from the broader community. How are you handling Stripe data ingestion? What trade-offs have mattered most for your team?

Need help setting up the right Stripe ingestion workflow for your team?
At Database Tycoon, we specialize in designing secure, scalable data pipelines- tailored to your stack. Whether you’re looking to integrate with dbt, speed up ingestion, or ditch vendor lock-in, we can help.