Article

Buy vs Build: When to Choose a Data Movement Platform Over Custom Pipelines

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June 6, 2025

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4 min

Summarize this article with:

Data integration seems deceptively simple at first. Move data from System A to System B – how hard could it be? Yet as organizations scale, this "simple" problem reveals layers of complexity that can overwhelm engineering teams. Understanding when to buy a data movement platform versus building custom pipelines can save your organization significant time, resources, and technical debt.

The Hidden Complexity of Data Integration at Scale

Consider a real-world scenario: A data engineering team successfully deployed an open-source data integration solution, scaling it to support 600 production datasets across 15 teams. Impressive, right? Yet this represented only a fraction of their organization's tens of petabytes of data. As usage grew, two critical challenges emerged:

1. The Connector Support Challenge

When building custom pipelines, you're responsible for creating and maintaining every single connector. Even supporting just 21 connectors (as one organization discovered) means managing unique logic, independent release cycles, and platform compatibility for each one. Every time a source or destination API changes, you need to update your custom code. The complexity formula (Connectors × Releases × Platform versions) creates an overwhelming maintenance burden that only grows over time.

2. Security and Compliance Gaps

Open-source solutions often lack enterprise-grade security features. Teams resort to workarounds like custom SSO proxies and creating separate deployments for each team needing unique access controls. This multiplies operational complexity and creates a maintenance nightmare.

3. Timeline Management

Building custom integrations for new data sources can take weeks or months, especially when dealing with legacy infrastructure. Consider the complex steps involved for a custom legacy system integration:

Discovery phase: Understanding authentication methods, data schemas, and API limitations
Development: Writing custom code to handle connections, data extraction, and error handling
Testing and debugging: Legacy systems often have undocumented quirks requiring extensive troubleshooting
Production deployment: Ensuring reliability, monitoring, and performance at scale

What should be a simple "connect and sync" operation becomes a multiweek or even multi-month project. When business teams need data from multiple legacy sources for urgent AI initiatives or analytics projects, these extended timelines can derail strategic objectives. Meanwhile, a pre-built connector can often be configured and deployed in hours or days.

When to Build Custom Pipelines

Building makes sense when:

Your data sources are highly proprietary with no existing connectors
You have very specific, unchanging requirements that won't evolve
You operate at a small scale with limited data sources and destinations
You have dedicated engineering resources willing to maintain pipelines long-term
Your compliance requirements are minimal or already met by existing infrastructure

When to Buy a Data Movement Platform

The scales tip toward buying when:

1. You Need to Connect Legacy Systems to Cloud or Vector Databases

Modern AI and analytics initiatives often require bridging decades-old systems with cutting-edge platforms. Connecting legacy databases like Oracle, SAP, or Informatica to modern cloud warehouses (Snowflake, Databricks) or vector databases (Pinecone, Weaviate) presents unique challenges:

Protocol mismatches: Legacy systems use older connection protocols that don't play nicely with cloud-native APIs
Data type conversions: Translating legacy data formats to modern schemas requires deep knowledge of both systems
Authentication complexity: Enterprise legacy systems often have complex authentication mechanisms that are difficult to implement from scratch
Change data capture (CDC): Efficiently syncing incremental updates from legacy systems requires specialized knowledge and tooling

A data movement platform abstracts these complexities with pre-built connectors that handle authentication, data transformation, and incremental syncing – work that could take months to build and debug internally.

2. You Need True Data Sovereignty: Enterprise Security and Compliance

If you require features like:

Role-based access control (RBAC)
API token management for CI/CD
SOX or GLBA compliance
Workspace isolation for different teams

Building these features from scratch requires significant investment – estimated at 20 development weeks initially plus 8 weeks annually for maintenance.

2. Your Integration Needs Are Growing

When your weekly active users jump from dozens to hundreds (or thousands), and data integration requests scale proportionally, a platform's multi-tenancy capabilities become crucial. Managing 12 separate deployments across multiple environments quickly becomes unsustainable.

3. Connector Support Is Draining Resources

Supporting common connectors like PostgreSQL, MySQL, SQL Server, and modern platforms like Databricks requires continuous updates and debugging. One organization estimated needing 0.3 FTE just for connector support – resources better spent on core business objectives.

4. You Need Predictable Support and SLAs

When data pipelines break at 2 AM, having enterprise support with defined SLAs can mean the difference between a quick resolution and a prolonged outage. Community support and GitHub issues searches don't cut it for production systems.

The Real ROI of Buying

The immediate financial returns are astonishing:

Development cost avoidance: Save approximately 1 headcount worth of development in the initial year
Ongoing maintenance savings: 0.6 headcount equivalent in subsequent years
Capacity based pricing: Better than volume based pricing because the costs are more predictable, especially for businesses that have cyclical surges in transactions or data volume.
Hidden benefits: Professional training, migration assistance, and direct access to product teams

In the long term, your ROI scales exponentially with your business. As your business and customer base grow, so do your data movement needs. Investing early saves you major costs in the future. One customer reported $900,000 annual savings after they replaced legacy infrastructure and custom pipelines with Airbyte’s data movement platform.

Making the Decision

Ask yourself these key questions:

Scale: Are we managing dozens or hundreds of data integrations?
Sovereignty: Do we need complete control of our first party data, with enterprise-grade access controls and compliance features?
Resources: Can we dedicate engineering time to building and maintaining custom solutions?
Growth: Will our data integration needs expand significantly in the next 12-24 months?
Support: Do we need guaranteed SLAs and professional support?

If you answered "yes" to three or more of these questions, buying a data movement platform likely offers better ROI than building custom pipelines.

The Strategic Partnership Advantage

Beyond features and cost, consider the strategic value of partnering with a data movement platform vendor. This relationship provides:

Influence over product roadmap and feature prioritization
Access to architectural reviews and best practices
Knowledge transfer to upskill your team
Migration assistance for complex transitions

Conclusion

The build versus buy decision for data movement isn't just about immediate costs – it's about sustainable scaling. While building custom pipelines might seem appealing for simple use cases, the hidden complexities of security, compliance, support, and maintenance often make buying a platform the smarter long-term investment.

As one data platform team discovered, what starts as a "simple" data movement problem can quickly evolve into a complex ecosystem requiring dedicated resources. By choosing an enterprise data movement platform, they transformed a growing operational burden into a strategic capability, freeing their team to focus on delivering value rather than maintaining infrastructure.

The question isn't whether you can build custom data pipelines – it's whether you should, given the total cost of ownership and the opportunity cost of your engineering resources. For most growing organizations, the answer increasingly points toward buying a purpose-built platform that can scale with your ambitions.

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