Open models or proprietary AI: which are companies choosing?
Open models, for most companies.
They're cheaper, avoid vendor lock-in, and now match proprietary quality for most use cases. What settles it: how much you need top-tier performance and support.
Checked Sep 8
What's your main use case?
Why?
Cost is the big driver Open models run on your own hardware or cheap APIs, no per-token premium.
Quality has caught up Recent open models rival proprietary ones on most benchmarks, especially for everyday tasks.
Lock-in is a real fear Companies don't want their workflows tied to one vendor's API and pricing changes.
If you need the absolute best reasoning today, proprietary still edges out, but that lead shrinks every quarter.
If your situation is different
What's your main use case: General chat. Open models win. Llama and Mistral handle most chat fine at a fraction of the cost.
What's your main use case: Complex reasoning. Proprietary still leads. For hard math or coding, GPT and Claude are ahead, but the gap is closing.
What's your main use case: Regulated industry. Open, with caveats. You can self-host and control data, but you need the team to secure it.
Who is choosing open models?
Right for
- Startups watching every dollar
- Teams with strong in-house ML engineers
- Companies with strict data privacy rules
- Workloads with predictable, high volume
Wrong for
- Teams needing top-tier reasoning today
- Small shops with no ML staff
- Products where support SLAs are critical
- Use cases at the frontier of capability
What do open vs. proprietary models cost in 2026?
| Figure | Value | Why it matters |
|---|---|---|
| Open model inference cost | Roughly 10 to 50x cheaper per token than leading | Self-hosting or cheap providers make high-volume AI affordable. |
| Proprietary API price | Often $2 to $15 per million output tokens, 2026 | At scale, these add up fast and are hard to predict. |
| Open model benchmark gap | Within 5 to 10% on most general tasks, 2026 | The gap is shrinking; for chat and coding, it's often negligible. |
What's the biggest mistake companies make with open models?
The biggest mistake is assuming open models are always free. You pay for engineering time, GPU infrastructure, and maintenance. Many companies underestimate the total cost of self-hosting and end up spending more than a proprietary API would have cost. Start with a managed open-model API, then move to self-hosting only when volume justifies it.
How do you decide in two minutes?
- List your top three AI use cases and rank them by complexity.
- Benchmark a leading open model against a proprietary one on your own data.
- Estimate your monthly token volume and compare total costs, including engineering.
- Pick open if the quality gap is acceptable and you have or can hire ML support.
People also ask
Which open model should I start with?
Llama 3.1 8B. Start there. It's the most documented, easiest to run, and has the biggest ecosystem. If you have a specific use case, that could change the pick.
How do I handle compliance with open models?
Treat open models like any third-party code. You don't need a new compliance regime. You need the same checks you'd run on any open-source dependency: license, provenance, and data flow.
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