Table of contents
The question "which LLM should we use" doesn't have a single right answer, because the right model depends on what you're actually optimizing for — and most teams pick based on general reputation rather than the specific tradeoffs that matter for their use case. A proprietary API model from a major provider and a self-hosted open-source model solve the same broad problem in fundamentally different ways, with different cost structures, latency profiles, data handling and operational burden.
This guide gives a practical framework for choosing: the real tradeoffs between proprietary and open-source models, the dimensions worth evaluating beyond a general capability reputation, and why running your own evaluation on your own task matters more than any public benchmark leaderboard.
- Start with
- The task's actual requirements, not a leaderboard ranking
- Proprietary API models win on
- Capability ceiling, speed to integrate, no infra to run
- Self-hosted open-source wins on
- Data control, per-request cost at scale, no vendor lock-in
- Most reliable evaluation
- Your own task, your own data — not a public benchmark
Start with the task, not the model
The most common mistake in LLM selection is picking a model first and figuring out the use case second. The right starting point is the opposite: define what the task actually needs — how complex the reasoning is, how much latency the user experience can tolerate, what data the model will see, and what happens if it's occasionally wrong — and let those requirements narrow the model choice. A simple classification or extraction task and a complex multi-step reasoning task have genuinely different requirements, and the model that's overkill (and expensive) for one can be underpowered for the other.
Proprietary API models vs. self-hosted open-source
This is the first real fork in the decision, and it's more consequential than which specific model you pick within either category.
| Dimension | Proprietary API (OpenAI, Anthropic, Google, etc.) | Self-hosted open-source |
|---|---|---|
| Time to integrate | Fast — an API key and a request format | Slower — requires hosting infrastructure and MLOps capability |
| Capability ceiling | Generally leads on complex reasoning tasks | Competitive on many tasks, can trail on the hardest reasoning |
| Cost structure | Pay-per-token, scales with usage, no infra cost | Upfront and ongoing infra cost, cheaper per-request at real scale |
| Data handling | Data leaves your infrastructure to the provider (check their retention/training policy) | Data can stay entirely within your own infrastructure |
| Vendor lock-in | Higher — tied to that provider's API, pricing and availability | Lower — you control the model and where it runs |
| Operational burden | Minimal — the provider handles scaling and uptime | Real — your team owns hosting, scaling and model updates |
Data sensitivity can decide this before cost does
If the task involves data you're contractually or regulatorily required to keep within your own infrastructure — certain healthcare, financial or government use cases — that constraint can rule out a proprietary API model entirely before cost or capability enter the discussion. Check this first; it's a harder constraint than either of the other two.
Don't trust the leaderboard — run your own evaluation
Public benchmark leaderboards measure general capability on tasks that are almost never exactly your task, and model rankings shift often enough that a leaderboard snapshot is stale within months. The evaluation that actually predicts how a model will perform for you is one built on your own representative examples, scored against criteria that reflect what "good" actually means for your specific use case — not a generic accuracy percentage on someone else's benchmark.
A minimal but genuinely useful evaluation setup: 20-50 real (or realistic) examples of your actual task, a scoring rubric specific to what matters for your use case (correctness, tone, format adherence, latency), and a way to run multiple candidate models or prompts against the same set and compare. This doesn't need to be elaborate to be far more predictive than a leaderboard ranking — see our LLM observability guide for how to extend this into ongoing production monitoring once a model is live, not just a one-time pre-launch evaluation.
Practical decision framework
- Define the task's actual requirements — reasoning complexity, latency tolerance, and the real cost of an occasional wrong answer.
- Check for a hard data-residency constraint that rules out proprietary APIs before anything else is evaluated.
- Shortlist 2-3 candidate models spanning proprietary and open-source, based on the task requirements — not general reputation.
- Run your own evaluation on real examples with a use-case-specific rubric, not a public benchmark.
- Model total cost at your expected volume, including infrastructure if self-hosting — proprietary API pricing that looks cheap at prototype volume can be the more expensive option at real production scale, and vice versa.
- Plan for model changes — providers deprecate and update models; a self-hosted model gives you control over when that happens, an API model doesn't.
- Task requirements (reasoning complexity, latency, error tolerance) defined before evaluating models
- Checked for a hard data-residency or regulatory constraint that rules out proprietary APIs
- Ran an evaluation on your own representative examples, not just a public benchmark score
- Modeled total cost at expected production volume, including infrastructure if self-hosting
- Considered a mixed approach — different models for different sub-tasks — before assuming one model for everything
- Have a plan for handling provider model deprecations or version changes
Choosing a model for an AI product or feature?
Talk to our AI engineering team about evaluating LLM options for your specific use case, data constraints and scale.
Frequently asked questions
Should I use a proprietary API model or a self-hosted open-source model?+
It depends on your task's requirements: proprietary APIs are faster to integrate and generally lead on complex reasoning; self-hosted open-source models give you full data control and can be cheaper per-request at real scale, at the cost of operational burden. A hard data-residency requirement can decide this before cost or capability do.
Are public LLM benchmark leaderboards reliable for choosing a model?+
Not on their own — they measure general capability on tasks that rarely match your specific use case, and rankings shift frequently. An evaluation on your own representative examples with a use-case-specific rubric is far more predictive.
Can I use different models for different parts of the same product?+
Yes, and many production systems do — a stronger model for the hardest reasoning steps, a smaller or self-hosted model for high-volume, simpler tasks where cost and data control matter more.
What data privacy questions should I ask before choosing an API-based LLM provider?+
Whether your data is used to train the provider's models, how long it's retained, where it's processed and stored, and what contractual guarantees exist around data handling — get these in writing before committing.
How many examples do I need for a useful LLM evaluation?+
20-50 real or realistic examples of your actual task, scored against a rubric specific to what matters for your use case, is enough to meaningfully compare candidate models — far more predictive than relying on a public benchmark alone.
Does choosing a proprietary API model create vendor lock-in?+
It creates more lock-in than a self-hosted model, since you're tied to that provider's API, pricing and availability. This is a real tradeoff against the API model's faster integration and lower operational burden, worth weighing explicitly rather than ignoring.
Written by
CodeSurge AI Engineering Team
The CodeSurge AI team designs and builds AI systems, SaaS products and enterprise integrations for clients in India, the UAE and beyond — this section shares the architecture patterns, cost drivers and implementation tradeoffs we work through on real projects.