Vertical integration in data infrastructure: Risks and alternatives

On paper, a vertical integration agreement seems ideal: one contract, pre-built integrations, and out-of-the-box capabilities that promise simplicity. But when you zoom out and look at the long-term cost implications and architectural limits, the cracks start to show.

See how vertical integration can restrict innovation and growth, and explore alternative models, how to assess your environment, and ways to avoid vendor lock-in.

What is vertical integration in data infrastructure?

Vertical integration occurs when a business uses the same vendor for multiple sequential layers of its data stack. It creates an architectural dependency where one provider handles everything from ingestion through storage, compute, transformation, and access.

When a company is completely entrenched in one ecosystem, adapting or extending outside that infrastructure becomes technically — and sometimes even contractually — difficult. In contrast, a horizontal or modular architecture lets you pick the best tool for each layer and build an ecosystem tailored to your company’s needs.

Concerns around vertical integration are especially relevant right now, with Salesforce acquiring Informatica and SAP reducing interoperability with external AI systems. These moves, along with restrictions on ETL tools or tighter control over ERP data APIs, point to a broader movement toward closed, vertically integrated ecosystems.

The initial appeal of vertical integration

Vertical integration offers benefits that seem extremely attractive, especially to teams that want to go to market quickly. Working with a single vendor simplifies architectural decisions, allowing you to sign contracts and start building in a fraction of the time it would take with a multi-vendor stack. 

Even after systems are up and running, pre-built integration between layers of your data architecture makes it easier for engineers to construct a working ecosystem. The same goes for implementing new tools from the same vendor, as pre-built connectors or integration systems often provide low/no-code options.

It’s only when you examine these benefits over the long term that they start to seem less appealing. Whether that’s high switching costs or the inability to move away from the vertical stack, the limitations become clear only after you’re fully invested in the ecosystem.

The hidden costs and long-term risks of vertical integration

The real costs of vertical integration emerge over time, often when you attempt to either move away from a partner or branch out. The dependency created by hosting all infrastructure with a single provider creates barriers that lock you in, making it expensive and technically challenging to leave.

Here are the main long-term risks of vertical integration: 

• Pricing leverage: Once you’re locked into a vendor’s ecosystem, you have no leverage when the vendor decides to increase prices. Switching stacks and building from zero becomes costly and disruptive to all data-led processes. What looks like a low-cost, out-of-the-box integration solution initially quickly becomes a financial drain. 
• API restrictions and tool limitations: Some platforms actively limit the ingestion tools that can interact with their infrastructure, forcing you toward first-party solutions. Others impose usage limits by capping API calls or raising the cost of API access. For AI use cases that require constant, rapid data access, these limits stall pipelines or become prohibitively expensive.
• Innovation strategy: If your strategy requires new features that go beyond the current tech stack, the vendor lock-in might block you from adopting them. If you need specific data access that’s not on the vendor’s roadmap, your ability to innovate stalls.
• AI acceleration cost problem: AI workloads depend on high-frequency, low-latency data access. In a system where data and compute costs are tightly coupled, introducing AI can amplify spending significantly — sometimes high enough to make AI pipelines prohibitively expensive.

Vertical integration vs. Open Data Infrastructure 

Open Data Infrastructure (ODI) is an approach to data architecture that allows you to store data in open formats and use it across all parts of your stack.

An open structure avoids “walled garden” data and most data infrastructure vendor risks, as you have full control over how you store, process, and access data. Because you’re not tied to a single provider, it’s easy to mix and match tools to find the best stack for the business.

Here’s how vertical integration and ODI differ.

Pricing model

In a vertical integration stack, every layer — storage, compute, ingestion, and more — falls under one pricing umbrella. It feels simple at first, but costs rise fast because you can’t optimize each layer independently.

ODI decouples individual layers, allowing you to optimize each service based on business needs. While it takes more upfront effort, it’s significantly more cost-effective in the long run. 

Data portability

Vertically integrated platforms typically store your data within proprietary data formats, making them compatible solely with the vendor’s query engines.

‍Open infrastructure uses table formats like Delta Lake and Apache Iceberg™, which are queryable by any standards-compliant engine. That means teams can query with a range of systems without having to extract and reupload data to a separate platform.

Compute engine optionality

A vertical integration strategy forces you to use the vendor’s compute engine, often blocking any external tools in this layer. With an open approach, you’re free to choose any engine, such as Spark, Trino, Presto, Flink, or something else without re-extracting data or converting formats.

Flexibility over time

A data platform consolidation brings every layer into one system. If the vendor doesn’t support a new capability you want to build, you’re stuck. You won’t be able to add new tools without an architectural rebuild or potentially incurring data egress or switching costs.

An open approach is completely modular. You can replace individual components with better options anytime. ODI offers full flexibility in terms of evolving tech and scalability.

How to assess vertical integration risk in your current stack

Vertical integration often isn’t obvious from the outside. Pricing can be opaque and engaging with your data may require using the vendor’s prescribed tools. If you suspect your stack relies too heavily on a single supplier, it’s time to investigate.

Here are some questions to ask your team:

• Are you limited to proprietary data formats, or can you export in standard, open formats like Parquet or ORC?
• Do you have to use first-party tools, or can you use third-party APIs for ingestion or other parts of the data stack?
• If your data volume or query workload increases by 5 times or 10 times, how does the pricing change?

The answers will reveal whether you’re currently inside a vertical stack. Other signs include dependence on a proprietary query language, egress fees for moving data off-platform, restrictions on using certified ETL tools, or limited transparency into pricing for compute usage.

Building a non-vertically-integrated data stack

Vertical integration is often a result of hasty decision-making early on in data development life cycles. Choosing a single provider may feel convenient at the start, but that decision can spiral into long-term dependency and architectural lock-in.

To avoid this, look for modular, standards-based architecture. Prioritizing data infrastructure that separates storage from compute enables data access without vendor-specific conversions. This becomes especially important as you begin to add downstream analytic tools or ML/AI agents.

Modularity also gives you the flexibility to change suppliers as business needs evolve. You can adopt new tools without any replication overhead or costly migrations. 

The foundation of any non-vertically-integrated data stack is open standards. Look for Apache Iceberg for table formats, REST catalogs for metadata, and vendor-neutral data movement pipelines. Consult the ODI: Data Access Scorecard for a high-level overview of your options.

How Fivetran supports non-vertically-integrated architectures

Effective, reliable data transfer is the foundation of any data system. If you tightly couple ingestion methods to vendor-controlled storage or compute, you start to lose out on ecosystem flexibility from day one.

Fivetran provides a vendor-neutral data movement layer to make sure you have complete control over your data architecture. Its 750+ connectors bring data from any source into your destination environment without proprietary dependencies or format conversions. 

Fivetran’s Managed Data Lake Service decouples ingestion from compute and natively supports open table formats (such as Delta Lake and Apache Iceberg) as well as downstream tools (such as Flink, Trino, and Spark). Your teams are free to query data without re-extraction and optimize compute independently — no changes required to your data layer.

Explore vendor-neutral ingestion that doesn’t compromise on performance or enterprise needs. Request a Fivetran demo today.

FAQ

How can organizations avoid lock-in from vertical integration in their data stack?

Start by looking for modular, standards-based architecture and working with vendor-neutral data movement tools. When assessing a platform vendor, check whether or not they use open standards and how they certify third-party integrations. ODI is a direct antidote to vendor lock-in.

Apache Iceberg is a trademark of the Apache Software foundation.

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