Should we use open source or proprietary models for our product features?

Summary

• Choose by task, not strategy. Use the right model for each job based on accuracy, latency, cost, and governance, not an exclusive bet on one type.
• Enterprises are blending both. most of organizations use open source alongside proprietary models, with strong preference for smaller, cost-effective models.
• Smaller models often make sense. most of organizations prefer models with 13 billion parameters or fewer because they cost less to run and deliver strong results.
• Lock-in is a real risk. The AI landscape evolves rapidly. A flexible infrastructure lets you swap models as research advances without rewriting code.
• Databricks Model Serving enables independence. Use abstraction layers to support multiple models, and route requests by cost or quality criteria.

Should we use open source or proprietary models for our product features?
Don't choose exclusively. Use the right model for each job, blending open source and proprietary options as needed. This task-driven approach maximizes accuracy, minimizes cost, and keeps your product agile. Among enterprises, many are choosing open source options, often running them alongside proprietary models. Within the open source ecosystem, organizations commonly prefer smaller models with 13 billion parameters or fewer because they cost less and deliver strong results.

What determines model choice

Instead of asking "Which model will win?", ask: Which model is best for this job, with this context, at this cost, and with this governance requirement?
Model selection depends on five factors:

Factor	Implications
Task type	Complex reasoning needs frontier models; content generation or classification can use smaller models.
Latency	Smaller models run faster. Low-latency uses (live chat, assistants) need local or smaller models.
Accuracy threshold	High-stakes tasks (compliance, medical) need strong models; draft generation can use weaker ones.
Cost structure	Token pricing favors smaller models; throughput pricing may shift economics toward larger models.
Privacy and governance	Sensitive data requires on-premise or open source models; regulated industries may require model transparency.

Trade-offs

Proprietary models excel at complex reasoning and multi-step planning. Easy to integrate and cloud-hosted. The trade-off: you send data to providers, lack visibility into internals, and are locked into their pricing.
Open source models offer transparency and run on-premise. You control deployment and can fine-tune for your domain. The trade-off: you own hosting, updates, and security. They excel at code generation, SQL authoring, and domain-specific tasks when fine-tuned on your data.

Market shift toward smaller models

Within open source, most prefer models with 13 billion parameters or fewer. Why? They run on commodity hardware, respond faster, and cost a fraction of larger variants. For many real-world tasks (content workflows, customer support, assistants), smaller models tuned for your domain deliver strong results.

Avoiding lock-in

The model landscape changes rapidly. Meta Llama 3 launched in April 2024 and within weeks reached significant share of open source LLM usage. This velocity means you can't bet long-term on a single provider.
Implement a meta-harness: Create an abstraction layer that surfaces one user experience while routing requests to underlying models (open source and proprietary). This lets you swap models without rewriting code.
Use intelligent routing: Route requests first to cost-optimized models, then escalate to stronger models only when quality requires it. Databricks Model Serving supports 30+ foundation models with auto-fallback routing, letting you choose by cost, quality, and latency without long-term vendor contracts.

Governance and control

A flexible architecture preserves your ability to control data, avoid vendor lock-in, and maintain competitive advantage. Proprietary models are black boxes; open source models let you audit, fine-tune, and adapt them. Many regulated industries increasingly favor open source for its transparency and control.

FAQs

Won't multiple models create operational complexity?

Not if you design the abstraction layer properly. A meta-harness is standard: developers use one API; infrastructure handles selection, fallback, and logging. The overhead is manageable.

Fine-tune open source or use proprietary models as-is?

Both. Fine-tune smaller open source models on your domain-specific data for specialized tasks; use proprietary models for out-of-domain reasoning. Combine through a meta-harness.

How often re-evaluate?

Quarterly. New models appear, benchmarks shift, and costs change. A flexible architecture lets you adopt improvements without disrupting your product.

What if a model gets deprecated?

With a meta-harness, swap models without rewriting code. This is the core benefit of abstraction. Without it, deprecations force expensive rewrites.