At this point, you understand the fundamentals. Open-source AI models deliver measurable benefits—namely lower costs, greater deployment control, and freedom from vendor lock-in.

Now comes the hard part: Choosing which model to actually use.

Public repositories contain over 2 million models, each with different capabilities and resource requirements. Teams often spend weeks evaluating options, only to discover licensing restrictions, performance gaps, or infrastructure incompatibilities after development begins.

Luckily, selecting the best model doesn’t have to feel like finding a needle in a haystack. All it takes is the right strategy.

This guide provides a practical framework for narrowing millions of options to a single model that fits your use case and requirements. Follow these six steps to make confident deployment decisions without spending months on research.

Step 1: Start with Use Case Requirements

Before evaluating any models, you need to clearly define what you’re building and what problem it needs to solve.

Start with the business problem. Are you automating customer support responses? Generating code from natural language? Analyzing contracts? Summarizing research documents? Specificity is valuable at this stage, since it will shape every evaluation decision that follows.

Next, define your success criteria. What does “good enough” look like for this application? Your answer to that question will depend on your use case, but it should be concrete and measurable. Consider the examples we mentioned before:

  • Customer service bot: Succeeds when it resolves common queries without human escalation. 
  • Code generator: Succeeds when developers accept its suggestions more often than they reject them. 
  • Document analyzer: Succeeds when it catches compliance issues that manual review would miss.

Document these requirements before looking at models. This prevents the common mistake of choosing a powerful model because it looks impressive, then discovering it’s overkill for your actual use case—or worse, that it can’t actually do the thing you need.

Step 2: Filter by Deployment Constraints

Once you know what you’re building, you can safely start eliminating models that won’t work in your environment. Filtering helps you narrow millions of options to a manageable shortlist before you invest time in detailed evaluation.

Ask yourself about:

Infrastructure Compatibility

Does the model come in a format your systems support? Can your hardware run it? 

A model available only in PyTorch format, for example, won’t help if your security team hasn’t approved PyTorch for production. Similarly, a 70B model requiring multiple GPUs won’t work if you’re deploying to CPU-only infrastructure.

Licensing

Does the licensing permit your use case? 

Some models restrict commercial use, require attribution, or prohibit specific industries. Others impose revenue thresholds: Meta’s Llama license, for example, requires special permission for companies with over 700 million monthly users. Verify the license explicitly allows what you’re building before investing development time.

Security and Provenance

Can you verify who built the model and how it was processed?

Models from unknown sources or processed through unverified toolchains introduce supply chain risks. Check whether the publisher documents their build process and whether you can trust the source.

Step 3: Evaluate Performance Using Your Data

OK, you’ve filtered it down to a handful of potential models. Now comes the critical test: how do they actually perform on your data?

Don’t rely on benchmark scores alone. Models that dominate leaderboards often underperform in production since benchmarks measure performance on standardized datasets under controlled conditions. But they don’t reflect how models behave with your data, or how they might perform under production load. Research on GSM8K math problems revealed some models showed up to 13% accuracy drops on contamination-free tests, which suggests they had memorized answers rather than learned reasoning.

Next, test each candidate against representative samples from your production data. Build a small evaluation dataset—a few hundred examples should do—that reflects common scenarios, challenging edge cases, and domain-specific content you’ll encounter in production. Run each model through these tests at the quantization levels you’ll actually deploy.

This testing reveals what benchmarks can’t. It tells you whether the model maintains acceptable performance after quantization. Keep your shortlist tight: Testing too many models at once creates evaluation sprawl that obscures the decision you’re trying to make.

Step 4: Assess the Total Cost of Ownership

Deploying a model creates ongoing costs you’ll manage for months or years. Exact costs will vary based on your infrastructure choices, usage volume, and operational complexity, but here’s what open-source deployment typically requires:

Infrastructure

Your infrastructure costs will be largely influenced by model size. Smaller 7B models need 16-24GB of VRAM and can run on consumer-grade GPUs, whereas larger 70B+ models require over 80GB of VRAM and typically require enterprise-class GPUs (and/or multi-GPU setups.)

The cost difference is substantial: As of 2024, running a 70B model on AWS costs approximately $2,700 per month in GPU compute alone.

Fine-Tuning

If you need to customize the model for your domain, budget for data preparation ($5,000-$10,000 for production-ready datasets) and compute costs. Fine-tuning on AWS infrastructure runs about $67 per day. 

Most teams need multiple training iterations to reach production quality, so you should expect these costs to compound over time.

Ongoing Operations

Plan for annual maintenance costs covering infrastructure updates, security patches, and monitoring. You’ll also need to budget for periodic model retraining as your data and business requirements evolve, as well as infrastructure scaling costs as your usage grows.

Running models in your own infrastructure—whether on-premises or in your private cloud—can reduce costs by over 90% compared to pay-per-token API services, but only at scale. So, be sure to calculate costs at your projected volume before committing.

Step 5: Verify Governance and Documentation

Now that you’ve assessed the costs and confirmed the model fits your budget, you need to verify it has the documentation you need to actually deploy it.

Legal, infrastructure, and security teams each require specific information before they can move forward. Check whether the model provides:

    • Licensing terms: Can you use it commercially? Does it require attribution? Are there any industry restrictions?
    • Security provenance: Who built it? What toolchains processed it?
    • Technical specifications: What are the resource requirements (VRAM, CPU, GPU configuration)? How does it perform at the quantization levels you’ll deploy?
  • Documented limitations: Does the model card specify known failure modes or scenarios where performance degrades?

An AI Bill of Materials (AIBOM) provides this information in a structured format. AIBOMs document model components, training data sources, dependencies, performance metrics, security validation, and licensing terms. They can also support compliance with NIST AI RMF, the EU AI Act, and ISO/IEC 42001.

While AIBOMs are the gold standard, they’re still relatively rare. If the models you’ve shortlisted don’t come with AIBOMs, look for detailed documentation about licensing, provenance, technical specs, and documented limitations.

Step 6: Make the Decision

Congrats! You’ve completed your evaluation, and you have your finalists. There’s just one thing left to do: Choose your model.

The good news is, you can’t really go wrong here. Your finalists all meet your baseline requirements—they work with your data, fit your infrastructure, and fall within your budget.

The bad news is, there’s no formula to help you pick the “best” model. It’s a subjective decision, and your choice should take into consideration your organization’s unique priorities and use cases. But if you’re having trouble ABC, consider these XYZ to help narrow the playing field:

  • Does one model deliver better accuracy at the cost of higher latency? If you’re building a real-time application, speed might matter more than marginal accuracy gains. If you’re analyzing documents overnight, you might prioritize quality over response time.
  • How much fine-tuning will each model need? Smaller models are easier to deploy and maintain, but larger models might reduce the need for extensive customization. A 7B model that needs significant fine-tuning could end up more expensive than a 70B model that works well out of the box.
  • Are there any remaining reg flags? Even at this stage, you should eliminate candidates with unclear licensing, missing security provenance, incompatible formats, or anonymous sources you can’t verify.

Remember, the right model for you is the one that best aligns with your priorities—be it accuracy, cost efficiency, deployment simplicity, or any number of other goals.

The Bottom Line

These six steps transform model selection from an overwhelming research project into a systematic evaluation process. By starting with clear requirements and working through each decision methodically, you can confidently choose models that fit your needs.

But even a systematic process takes time. Teams that follow these steps still spend weeks researching models, validating security, benchmarking performance, and documenting their findings before deployment begins.

Anaconda AI Catalyst reduces this overhead. Every model in our catalog has already been curated, security-validated, quantized, and benchmarked, with complete AIBOMs documenting the information your teams need to know. What would normally take weeks of research is available immediately, allowing you to move seamlessly from requirements to deployment decisions.

Explore the model catalog in AI Catalyst to see how curated, pre-validated models can accelerate your AI development without sacrificing security or governance.