An AI model vendor bake-off is a structured process for comparing different large language models (LLMs) or prompt variations to find the best fit for a specific task. Instead of relying on generic benchmarks, a bake-off uses a framework to test models against your actual workload, measuring them on the criteria that matter most to your business: quality, cost, and speed.

This process moves you from guessing which model is best to knowing which one performs best for you. It provides a reproducible, data-driven way to make decisions, ensuring you choose the most effective and efficient model from a growing list of providers like OpenAI, Anthropic, Google, and various open-source alternatives.

A successful bake-off is built on a simple, repeatable framework that can be automated. It consists of a few core components that work together to produce a clear, actionable report.

• Seed Tasks: This is a curated dataset of inputs that represent your real-world use case. For a customer support chatbot, seed tasks might include common questions, complex edge cases, and intentionally difficult queries. A good set of seed tasks is diverse and representative of the challenges the model will face in production.
• Models and Prompts: These are the candidates you’re evaluating. You might compare GPT-4o against Claude 3.5 Sonnet and Llama 3, or you might test three different prompt variations against a single model to see which one yields the best results.
• Scoring Functions: This is the heart of the evaluation. Scoring functions are rules that define what “good” looks like for your task. They can be fully automated, require human review, or a mix of both.
• Cost and Latency Tracking: The “best” model isn’t always the one with the highest quality score. The framework must also track the cost per completion and the time it takes for the model to respond (latency), as these are often critical constraints in a production environment.
• One-Command Report: The final output should be a simple, easy-to-read report that summarizes the results for all stakeholders. This typically takes the form of a table or a dashboard that clearly shows how each candidate performed across quality, cost, and latency.

This combination of components gives you a holistic view, allowing for a balanced decision that goes beyond just a quality score.

The bake-off framework is a versatile tool that can be applied at various stages of the development lifecycle. It’s not just for picking a vendor once; it’s a continuous improvement tool.

• Initial Model Selection: When starting a new AI-powered feature, a bake-off helps you choose the right foundational model. You can quickly compare several leading proprietary and open-source models to see which one provides the best starting point for your summarization, classification, or generation task.
• Prompt Engineering and Optimization: Even with a model selected, your work isn’t done. The bake-off template is perfect for iterating on prompts. You can test different phrasing, context inclusion, or chain-of-thought techniques to systematically improve the model’s output without changing the model itself.
• Evaluating Fine-Tuned Models: If you’re considering fine-tuning an open-source model, you need a way to measure its performance against both the base model and leading proprietary APIs. A bake-off provides a standardized benchmark to justify the time and expense of fine-tuning.
• Continuous Regression Testing: AI models and provider APIs change over time. A new model version could cause a performance regression in your specific use case. By integrating your bake-off framework into your CI/CD pipeline, you can automatically test new models to ensure they meet your quality bar before deploying them.

While powerful, the bake-off approach comes with potential pitfalls. Understanding these challenges can help you avoid them and run a more effective evaluation.

Public benchmarks and leaderboards are excellent for gauging a model’s general capabilities, but they are poor predictors of performance on a specific, niche task. Your internal bake-off, using your own data, is the only way to know how a model will truly perform for your unique workload.

Automated metrics like JSON schema validation or keyword matching are fast and scalable, but they can’t capture everything. Nuanced qualities like tone, creativity, or the factual accuracy of a generated response often require human-in-the-loop review. A good bake-off framework incorporates both automated scores and a process for targeted human evaluation.

It’s easy to get fixated on achieving the highest possible quality score. However, a model that is too slow for a real-time chat application or too expensive for your unit economics is not the right choice, no matter how great its output is. The best model is the one that strikes the right balance between quality, speed, and cost for your specific application.

To get the most out of your vendor bake-off, follow a few key best practices that ensure your results are reliable, repeatable, and actionable.

1. Define a Clear Rubric First: Before you run a single test, define what a successful outcome looks like. Is the primary goal accuracy, brevity, or adhering to a specific format? Write down your scoring criteria and get stakeholder buy-in to avoid ambiguity later.
2. Start with a Diverse Test Set: Your seed tasks should cover the breadth of your expected inputs. Include typical examples (the “happy path”), known edge cases, and even adversarial prompts designed to break the system. A small, diverse set of 20-50 tasks is often enough to reveal significant differences between models.
3. Automate Everything You Can: The power of the bake-off framework lies in its repeatability. Use scripts and tools (like open-source libraries promptfoo or LangSmith) to automate the process of running prompts against models, calculating metrics, and generating the final report. This turns a manual, one-off analysis into a rapid, iterative process.
4. Version Control Your Assets: Treat your prompts, seed tasks, and scoring functions like code. Store them in a Git repository to track changes over time. This creates an auditable history of your evaluation process and makes it easy to collaborate with your team.
5. Present a Balanced Scorecard: Don’t just show the quality score. Your final report should present a holistic view. A simple table comparing each model across quality, average latency, and estimated cost per 1,000 tasks gives stakeholders everything they need to make an informed trade-off.

By following these guidelines, you can build a robust evaluation system that empowers your team to make objective, data-driven decisions about one of the most critical components of your tech stack.

After your bake-off helps you select the right models, the next challenge is managing how they’re deployed to users. You might want to offer a high-performance model to “Pro” users while providing a more cost-effective model for the “Free” tier. Or perhaps you want to safely test a new model with a small group of beta testers before a full rollout.

This is where Kinde’s feature flags become essential.

Feature flags allow you to dynamically control which AI model is served to a specific user, organization, or environment, all without changing your code. You can create a feature flag in Kinde—for example, a string flag named ai-model-provider—and set its value to "gpt-4o" for one group of users and "claude-3.5-sonnet" for another. Your application code simply checks the value of this flag and routes the request to the appropriate model API.

This approach lets you:

• Manage different subscription tiers: Easily assign different models or capabilities to users based on their billing plan.
• A/B test models in production: Route a percentage of traffic to a new model and compare its performance and cost against the incumbent in a real-world setting.
• Conduct phased rollouts: Release a new model to internal teams, then beta testers, and finally all users, minimizing risk at each step.

By combining a rigorous bake-off framework to choose your models and Kinde’s feature flags to manage them, you can build a flexible, scalable, and cost-effective AI product.

• About feature flags
• Add a feature flag in Kinde