The Lock-In Problem
Every major analytics platform wants to be your everything. Your warehouse, your ETL, your dashboards, your AI, your governance layer — all in one vendor. The pitch is simplicity: one platform, one contract, one support team.
The reality is lock-in. Once your data, transformations, dashboards, and business logic live inside a single vendor's ecosystem, leaving costs 6-18 months of migration work. The vendor knows this. That's the business model.
We've helped clients migrate away from monolithic platforms — Domo, legacy Tableau Server, proprietary data warehouses. The pattern is always the same: the platform was fine when they started, but as the business grew, the platform's limitations became constraints. And by then, leaving was expensive.
What Composable Architecture Looks Like
Composable analytics separates your data stack into independent layers, each connected through standard APIs:
- ✓Ingestion — Fivetran, Airbyte, or custom connectors pull data from source systems. Swappable without touching anything downstream.
- ✓Storage — Snowflake, BigQuery, or PostgreSQL. Your data warehouse is a commodity choice, not a lifetime commitment.
- ✓Transformation — dbt or custom SQL. Business logic lives in version-controlled code, not inside a vendor's proprietary layer.
- ✓Visualization — Apache Superset, Power BI, Tableau, or embedded dashboards. Interchangeable because they all query the same governed warehouse.
- ✓AI & NLQ — Model-agnostic agents that can use OpenAI, Anthropic, or open-source models. Switch providers without rebuilding the application.
Each layer talks to the others through APIs and standard interfaces. Replace any layer and the rest of the stack keeps working.
Why Model-Agnostic Matters
The AI landscape changes quarterly. The best LLM today might be the second-best LLM next quarter. Organizations that hard-code their AI systems to a single model provider face the same lock-in problem that plagued legacy analytics platforms.
We build AI systems that are model-agnostic by design. The application logic — prompts, retrieval, workflow orchestration — is separate from the model layer. Switching from one provider to another is a configuration change, not a rebuild.
This isn't theoretical. We've switched model providers mid-engagement when a newer model offered better accuracy for a specific use case. The client didn't notice a disruption because the architecture was designed for it.
What This Means for Mid-Market Organizations
Mid-market organizations don't have the engineering teams to manage complex migrations. They need architectures that are simple to operate today and flexible enough to evolve without starting over.
Composable architecture delivers this by ensuring that each layer of the stack is a deliberate choice — not a default that came bundled with the platform. When a better tool emerges, you adopt it. When a vendor raises prices, you have options. When your needs outgrow a component, you replace it.
We build this way because we've seen the alternative. Organizations locked into platforms they've outgrown, spending more on workarounds than the original platform costs. The upfront investment in composable architecture pays for itself the first time you need to change something.