Open vs. Closed vs. Custom Models: Which Is Right for You?

Choosing the right AI model for your application can feel like a high-stakes decision. Do you go with a closed-source model for its ease of use or an open-source model for more control and lower cost? And what about fine-tuning?

At Novita AI, we believe the best model is the one that fits your specific task(s). We’ve designed a quick breakdown to guide your decision.

Closed Source Models

Closed-source models like GPT-5, Claude, and Gemini are great for rapid prototyping and initial testing because they require almost no setup.

Pros

• Fastest path to prototyping and testing with early users
• No infrastructure setup or specialized hardware is needed: just call the API
• Best when you want something that just works with minimal infra setup

Cons

• Expensive, so best at smaller scale
• No transparency into model internals
• Doesn’t support customization or fine-tuning

When to use it: Most startups begin here. High costs only become an issue once you scale, and when you’re searching for Product-Market Fit (PMF), speed is the priority.

Open Source Models

Open-source models such as Llama, Qwen, and DeepSeek give you flexibility and cost savings, with the ability to run or customize models to your domain further down the line. While setup and optimization can be complex, services like Novita AI hosts and provides API access to open-source models via API, offering the polished experience of closed-source models while retaining the cost-benefits of open-source. You have a wide range of open model options and can find one that fits your specific compute, cost, and specialization requirements. Even though they require setup and configuration, you can skip that by using a model hosting service like Novita AI, which lets you keep the polished experience you expect from a closed source model (api) at open source costs.

Pros

• Lower cost and greater flexibility
• Wide range of options, with tens of thousands of models optimized for different use cases, benchmarks, and metrics (e.g. latency, throughput, cost)

Cons

• Requires technical expertise and specialized hardware if self-hosting
• Setup and tuning can be difficult and time-consuming

When to use it: As you scale, switching to open-source models dramatically reduces costs while maintaining competitive performance

Fine Tuning

Fine-tuning is the process of taking a pre-trained open-source model and training it further on your specific data. This creates a specialized model tailored to your domain or task.

Pros

• Proprietary advantage: your fine-tuned model becomes a key differentiator
• Improved accuracy and relevance on domain-specific tasks
• Improves accuracy and relevance on narrow, domain-specific tasks
• Long-term efficiency: better performance at lower cost once trained

Cons

• Requires significant time, data, and technical investment
• Only possible with open-source models
• More effort upfront, but higher ROI long-term

When to use it: More established companies and startups that have found PMF fine-tune models to strengthen defensibility and increase product differentiation

Custom Model Training

Custom model training means building a model from the ground up or significantly pretraining an existing base model with large-scale proprietary data. This is the most resource-intensive approach but offers unmatched control and ownership.

Most closed- and open-source models come with built-in guardrails and limitations. These work well for general-purpose use, but they can block unique or specialized applications, such as certain regulated industry tools, or unconventional multimodal tasks. In those cases, companies often turn to custom training so they can build models without those restrictions.

Pros

• Complete control over architecture, training data, and performance
• Maximum defensibility: the model is uniquely yours
• Can be optimized for unusual modalities, tasks, or performance requirements
• Freedom from prebuilt guardrails that limit edge-case use cases

Cons

• Requires massive datasets, compute, and ML expertise
• High upfront costs and long training cycles
• Risk of over-investing before market fit is validated

When to use it: Only for companies at significant scale or with highly specialized needs. Custom training is ideal when you have large proprietary datasets, require freedom from built-in restrictions, or want to build foundational IP that competitors cannot replicate.

Putting It All Together:

The right choice depends on your company or project’s lifecycle. You might start with a closed-source model for a quick prototype, then switch to an open-source model like Llama to scale affordably, and eventually fine-tune to increase product differentiation and defensibility.

Novita AI’s platform is built to support you at every stage:

• Library of Hosted Open-Source Models to help you scale efficiently
• Dedicated Hosting for Custom Models to deploy your fine-tuned models with ease
• Unified API to switch between models, so you can find the best model for your application without rewriting your code

Whether you’re validating an idea, scaling fast, or building proprietary solutions, Novita helps you move seamlessly between stages.

Visit our website to get started.

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1. Evaluating, Benchmarking, and A/B Testing LLMs with Novita AI
2. How to Choose the Right Model for Your Application
3. Behind the Scenes: How We Host Models on Novita AI