How to Run a Model Bake-Off in 1 Week

Table of Contents

1. What is a Model Bake-Off and Why You Need One
2. The 1-Week Framework at a Glance
3. Pre-Work: Get Your Data and Baselines Ready
4. Day 1–2: Define Your Test Harness
5. Day 3–4: Run Parallel Evaluations
6. Day 5: Analyse Results and Pick a Winner
7. Day 6–7: Document, Iterate, and Plan Next Steps
8. Common Pitfalls and How to Avoid Them
9. Making This Repeatable for 2025 and Beyond
10. Getting Help: When to Bring in a Partner

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What is a Model Bake-Off and Why You Need One

A model bake-off is a structured comparison of multiple AI models—typically LLMs, embeddings, or classification models—against your actual data and use case. It’s not a theoretical exercise. It’s the fastest way to answer a concrete question: Which model actually works best for us, and what will it cost?

Most teams skip this step. They pick a model based on hype, a blog post, or what their vendor recommends. Then they spend months in production wondering why latency is high, costs are blowing out, or accuracy is mediocre. A model bake-off takes 1 week and saves you months of regret.

The stakes are real. Choosing Claude over GPT-4o might cut your inference costs by 40% but add 200ms latency. Swapping to a smaller open-source model might halve costs again but tank accuracy on edge cases. A bake-off surfaces these trade-offs early, when pivoting is still cheap.

At PADISO, we’ve run bake-offs for founders shipping AI features, operators automating workflows, and enterprises deciding whether to build on proprietary models or open-source stacks. The framework in this guide is battle-tested. You can run it yourself, or if you’re scaling fast and need a fractional CTO to oversee the process, PADISO’s CTO as a Service and AI Strategy & Readiness teams have shipped this dozens of times.

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The 1-Week Framework at a Glance

Here’s the skeleton. We’ll detail each phase below.

Pre-Work (Before Monday): Gather representative data, define success metrics, and list candidate models.

Day 1–2: Build a test harness (evaluation script, logging, cost tracking) that runs all models on the same data.

Day 3–4: Run evaluations in parallel. Log latency, accuracy, cost, and any failure modes.

Day 5: Analyse results. Plot cost vs. accuracy. Identify the Pareto frontier. Make a call.

Day 6–7: Document findings, set up A/B tests or staging deployments, and plan the next bake-off (because you’ll be doing this again when Claude 4 drops or Llama 4 ships).

The entire process is designed to be repeatable. You’ll run this again in 3 months, 6 months, and every time a major model release happens. The framework stays the same; only the candidate models change.

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Pre-Work: Get Your Data and Baselines Ready

Collect Representative Test Data

Your bake-off is only as good as your test set. If you’re building a claims automation system, use real claims. If you’re fine-tuning a summariser, use actual documents your users process. Aim for 100–500 examples, depending on how much variance your use case has.

Why representative data matters: A model that scores 95% accuracy on synthetic data might score 60% on real production data riddled with typos, truncated fields, and edge cases. Garbage in, garbage out.

If you don’t have historical data yet, create a small annotated set now. Spend a few hours with your domain experts (product, customer success, ops) labelling 100 examples. This is not wasted effort—you’ll reuse this for fine-tuning, monitoring, and future bake-offs.

Define Your Success Metrics

Before you run a single model, decide what “winning” looks like. For most teams, this is a combination of:

• Accuracy or F1 score (for classification, extraction, or routing tasks)
• Latency (time from request to response, in milliseconds)
• Cost per request (inference cost, including tokens, API calls, or compute)
• Failure rate (how often the model refuses, hallucinates, or crashes)

Write these down. Assign rough weights. For example:

• 40% accuracy (we need high precision on claims classification)
• 30% latency (users expect <500ms responses)
• 20% cost (we’re bootstrapped, can’t afford $0.10 per request)
• 10% failure rate (occasional errors are OK; crashes are not)

Different teams will weight these differently. An enterprise might prioritise accuracy and compliance over cost. A bootstrapped startup might trade accuracy for speed and cost. There’s no universal “right” answer—just be explicit.

List Your Candidate Models

Now decide which models to test. A typical bake-off compares 3–6 models. More than that and you’ll spend too long running evals. Fewer than that and you might miss a breakout winner.

Good candidates usually fall into these buckets:

1. The incumbent (whatever you’re using now, or the obvious choice)
2. The frontier model (GPT-4o, Claude 3.5 Sonnet—the latest and greatest)
3. The cost-optimised model (GPT-4o mini, Claude 3 Haiku, Llama 2–3 70B)
4. The open-source contender (Llama 3.1 405B, Mixtral 8x22B, Qwen)
5. The specialist (a fine-tuned version of one of the above, or a domain-specific model)
6. The wildcard (something new you read about, or a local quantised version)

For your first bake-off, stick to 4 models: the incumbent, the frontier, the cost-optimised, and one open-source option. That’s enough to surface the key trade-offs without drowning you in data.

Set Up Your Environment

You’ll need:

• A notebook or script (Python, ideally) that can call each model’s API or load it locally.
• An experiment tracker like MLflow or Weights & Biases to log results. (Optional but highly recommended—saves you from spreadsheet hell.)
• Cost tracking (a simple CSV or a cost module in your script that logs API pricing).
• Latency tracking (use time.time() or a proper observability tool).
• API keys or model access for each candidate.

If you’re evaluating open-source models locally, ensure your hardware can run them. A single A100 GPU can run most 70B models; a T4 will struggle. Budget for cloud compute (AWS, GCP, Lambda Labs) if you don’t have hardware on hand.

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Day 1–2: Define Your Test Harness

Build the Evaluation Script

Your test harness is a single script that:

1. Loads your test data.
2. Calls each model with the same prompt.
3. Logs the response, latency, tokens used, and cost.
4. Computes accuracy (or whatever your metric is).
5. Stores results in a structured format (JSON, CSV, or a database).

Here’s a skeleton in Python:

import time
import json
from datetime import datetime

class ModelBakeOff:
    def __init__(self, test_data, models, metrics_fn):
        self.test_data = test_data
        self.models = models  # Dict of {name: model_callable}
        self.metrics_fn = metrics_fn  # Function to compute accuracy
        self.results = []
    
    def run(self):
        for model_name, model_fn in self.models.items():
            print(f"Testing {model_name}...")
            model_results = []
            
            for i, example in enumerate(self.test_data):
                start = time.time()
                response = model_fn(example['input'])
                latency = time.time() - start
                
                # Log result
                result = {
                    'model': model_name,
                    'example_id': i,
                    'input': example['input'],
                    'expected': example['expected'],
                    'response': response,
                    'latency_ms': latency * 1000,
                    'timestamp': datetime.now().isoformat(),
                }
                model_results.append(result)
            
            # Compute aggregate metrics
            accuracy = self.metrics_fn(model_results)
            avg_latency = sum(r['latency_ms'] for r in model_results) / len(model_results)
            
            summary = {
                'model': model_name,
                'accuracy': accuracy,
                'avg_latency_ms': avg_latency,
                'num_examples': len(model_results),
                'results': model_results,
            }
            self.results.append(summary)
            
            print(f"{model_name}: {accuracy:.2%} accuracy, {avg_latency:.0f}ms latency")
    
    def export(self, filename):
        with open(filename, 'w') as f:
            json.dump(self.results, f, indent=2)

This is a bare-bones example. In practice, you’ll add:

• Error handling (what if a model times out or returns garbage?)
• Cost tracking (log input/output tokens, multiply by model pricing)
• Batching (call multiple examples in parallel to speed up evals)
• Retry logic (API rate limits and transient failures are common)
• Structured logging (use a proper logger, not print statements)

Define Your Evaluation Metric

This is the metrics_fn in the skeleton above. It depends on your task.

For classification (e.g., “is this claim fraudulent?”), use accuracy, precision, recall, or F1:

from sklearn.metrics import accuracy_score, f1_score

def evaluate_classification(results):
    predictions = [r['response'] for r in results]
    ground_truth = [r['expected'] for r in results]
    return f1_score(ground_truth, predictions, average='weighted')

For extraction (e.g., “pull the claim amount from this document”), use exact match or token overlap:

def evaluate_extraction(results):
    matches = sum(1 for r in results if r['response'].strip() == r['expected'].strip())
    return matches / len(results)

For generation (e.g., “summarise this claims document”), use BLEU, ROUGE, or human judgment:

from rouge_score import rouge_scorer

def evaluate_generation(results):
    scorer = rouge_scorer.RougeScorer(['rouge1', 'rougeL'], use_stemmer=True)
    scores = []
    for r in results:
        score = scorer.score(r['expected'], r['response'])
        scores.append(score['rougeL'].fmeasure)
    return sum(scores) / len(scores)

If you’re unsure, start with a simple metric (accuracy or exact match) and add sophistication later. The goal is speed, not perfection.

Instrument for Cost and Latency

Add a cost tracker to your harness. Most APIs publish per-token pricing. For example:

class CostTracker:
    PRICING = {
        'gpt-4o': {'input': 0.005, 'output': 0.015},  # Per 1K tokens
        'claude-3-sonnet': {'input': 0.003, 'output': 0.015},
        'llama-3-70b': {'input': 0.00035, 'output': 0.00035},  # Via Together AI
    }
    
    def estimate_cost(self, model_name, input_tokens, output_tokens):
        rates = self.PRICING.get(model_name, {})
        input_cost = (input_tokens / 1000) * rates.get('input', 0)
        output_cost = (output_tokens / 1000) * rates.get('output', 0)
        return input_cost + output_cost

Log this for every request. At the end of Day 4, you’ll have a clear picture of cost per request for each model.

Set Up Experiment Tracking

If you’re using MLflow, you can log each run like this:

import mlflow

with mlflow.start_run(run_name=model_name):
    mlflow.log_param('model', model_name)
    mlflow.log_metric('accuracy', accuracy)
    mlflow.log_metric('avg_latency_ms', avg_latency)
    mlflow.log_metric('cost_per_request', cost_per_request)
    mlflow.log_artifact('results.json')

This gives you a centralised dashboard to compare runs, track history, and spot trends over time. When you run your next bake-off in 3 months, you’ll have a baseline to compare against.

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Day 3–4: Run Parallel Evaluations

Execute the Harness

Now run your script against all models. If you have the compute, run them in parallel—don’t wait for Model A to finish before starting Model B. You’re aiming to complete all evaluations by end of Day 4.

Practical tips:

• Use a queue or job scheduler (Celery, Ray, or even GNU Parallel) to distribute work across GPUs or API calls.
• Batch API calls where possible. Most LLM APIs support batch processing, which is cheaper and faster than one-off requests.
• Monitor costs in real-time. If you’re burning $100/hour on API calls, you’ll want to know immediately.
• Log everything. Every request, response, latency, and error. You’ll need this data for analysis.
• Capture failure modes. If a model refuses to answer, times out, or returns garbage, log it. These are signals.

Handle Edge Cases and Failures

Models will fail. APIs will rate-limit. Networks will hiccup. Plan for it:

• Timeouts: Set a reasonable timeout (e.g., 30 seconds) and move on if a model doesn’t respond.
• Rate limits: Implement exponential backoff and retry logic.
• Partial failures: If Model A fails on 5% of examples, note it. That’s a red flag.
• Token limits: If an example is too long for a model’s context window, truncate it or skip it. Log what you did.

def call_model_with_retry(model_fn, input_text, max_retries=3):
    for attempt in range(max_retries):
        try:
            return model_fn(input_text, timeout=30)
        except TimeoutError:
            if attempt == max_retries - 1:
                return None  # Log this as a failure
            time.sleep(2 ** attempt)  # Exponential backoff
        except Exception as e:
            print(f"Error: {e}")
            return None

Track Latency Distributions

Don’t just log average latency. Log the distribution. A model with 100ms average latency but 5-second p99 is worse than one with 150ms average and 300ms p99.

import numpy as np

latencies = [r['latency_ms'] for r in model_results]
print(f"p50: {np.percentile(latencies, 50):.0f}ms")
print(f"p95: {np.percentile(latencies, 95):.0f}ms")
print(f"p99: {np.percentile(latencies, 99):.0f}ms")

If a model’s p99 is 10x its p50, investigate. It might be hitting rate limits, or it might have a pathological case (e.g., very long outputs for certain inputs).

Sanity-Check Results as They Come In

Don’t wait until Day 5 to look at results. As each model finishes, spot-check a few outputs. Ask:

• Does this look reasonable?
• Are there obvious errors?
• Is the model refusing to answer?
• Is latency consistent, or are there spikes?

If something looks wrong, log it and investigate. You might need to adjust your prompt, data, or harness.

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Day 5: Analyse Results and Pick a Winner

Aggregate and Visualise

Pull all results into a single view. Create a table like this:

Model	Accuracy	Latency (p50)	Latency (p99)	Cost/Request	Failures
GPT-4o	94%	120ms	450ms	$0.008	0
Claude Sonnet	96%	180ms	520ms	$0.006	1
Llama 3 70B	88%	95ms	280ms	$0.0004	3
GPT-4o mini	91%	85ms	250ms	$0.0015	2

Now plot accuracy vs. cost. This is the Pareto frontier—the set of models where you can’t improve one metric without sacrificing another.

In the table above, Claude Sonnet and GPT-4o are on the frontier (higher accuracy, higher cost). Llama 3 70B is below the frontier (lower accuracy, lower cost, but not a good trade-off). GPT-4o mini is on the frontier (reasonable accuracy, low cost, low latency).

Identify the Pareto Frontier

A model is on the Pareto frontier if:

1. No other model has higher accuracy at the same cost.
2. No other model has lower cost at the same accuracy.

Models on the frontier are viable candidates. Models off the frontier are dominated and can be eliminated.

def pareto_frontier(results):
    # Sort by cost
    sorted_results = sorted(results, key=lambda x: x['cost_per_request'])
    frontier = []
    max_accuracy = 0
    
    for result in sorted_results:
        if result['accuracy'] >= max_accuracy:
            frontier.append(result)
            max_accuracy = result['accuracy']
    
    return frontier

Weigh Your Priorities

Now think about your business. If you’re shipping a customer-facing feature, accuracy and latency matter more than cost. If you’re automating internal operations, cost might dominate. If you’re building a compliance system, you can’t afford failures.

Score each model on your pre-defined weights:

def score_model(result, weights):
    accuracy_score = result['accuracy'] * weights['accuracy']
    latency_score = (1 - min(result['latency_p99'] / 1000, 1)) * weights['latency']  # Normalize to 0–1
    cost_score = (1 - min(result['cost_per_request'] / 0.01, 1)) * weights['cost']  # Normalize
    reliability_score = (1 - result['failure_rate']) * weights['reliability']
    
    return accuracy_score + latency_score + cost_score + reliability_score

The model with the highest score wins. But don’t blindly trust the formula. Use it as a guide, then apply judgment.

Dig Into Failure Modes

Which examples did each model get wrong? Are there patterns?

For instance, maybe all models struggle with claims over $100K, or with handwritten documents, or with claims in specific regions. These insights are gold. They tell you:

1. Whether you need a more specialized model or fine-tuning.
2. Whether your test data is representative.
3. Whether you need pre-processing (e.g., OCR for handwritten documents).

Make the Call

By end of Day 5, you should have a clear winner. Document your decision in writing:

We’re shipping GPT-4o for this feature because it offers 94% accuracy at 120ms latency for $0.008/request. Claude Sonnet is 2% more accurate but costs 25% more. Llama 3 70B is 6% less accurate and has 3x the failure rate. We’ll revisit in Q2 2025 when Llama 4 ships.

This is your decision memo. Share it with your team and stakeholders. It justifies the choice and sets expectations for the next bake-off.

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Day 6–7: Document, Iterate, and Plan Next Steps

Write Up Your Findings

Document everything:

1. The candidates (model names, versions, pricing).
2. Your test data (size, characteristics, source).
3. Your metrics (accuracy, latency, cost, how you measured each).
4. The results (table, plots, failure analysis).
5. Your decision (which model won and why).
6. Next steps (when you’ll re-evaluate, what you’ll monitor in production).

Store this in a shared wiki or Notion doc. When you run the next bake-off, you’ll refer back to it.

Set Up Monitoring and A/B Tests

Don’t just deploy the winner and forget about it. Set up monitoring to track real-world performance:

• Accuracy: Log predictions and ground truth. Compute accuracy weekly.
• Latency: Track p50, p95, p99 in production.
• Cost: Monitor API spend and cost per request.
• Failures: Alert if error rate exceeds a threshold.

If you have traffic, run an A/B test. Send 10% of requests to the new model and 90% to the old one. Compare accuracy, latency, and cost. Once you’re confident, ramp up to 100%.

Plan Your Next Bake-Off

Mark your calendar for 3 months from now. By then:

• New models will have shipped (Claude 4, Llama 4, GPT-5 mini).
• Your use case might have evolved.
• You’ll have production data to inform your next test set.

Create a standing calendar event: “Model Bake-Off Q2 2025.” Make it a team ritual.

Consider Fine-Tuning

If your winner is close but not quite there—e.g., 92% accuracy when you need 95%—consider fine-tuning. Use your test data as a starting point. Many providers (OpenAI, Anthropic via their API) support fine-tuning.

A quick fine-tuning experiment (1–2 days) might unlock another 2–3% accuracy for minimal cost. This is a separate bake-off: fine-tuned Model A vs. fine-tuned Model B vs. the vanilla winner.

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Common Pitfalls and How to Avoid Them

Pitfall 1: Biased Test Data

Problem: Your test set doesn’t represent production. Maybe it’s too easy, or it’s skewed toward examples the incumbent model handles well.

Solution: Use stratified sampling. If your production data has 70% routine claims and 30% edge cases, your test set should too. Involve domain experts in labelling.

Pitfall 2: Unfair Comparisons

Problem: You’re using different prompts for different models, or different API versions, or different context windows.

Solution: Use the exact same prompt for all models. If one model needs a different format, note it and adjust. Use the same input data for all models.

Pitfall 3: Ignoring Latency Variance

Problem: You report average latency (100ms) but ignore p99 (2 seconds). In production, that p99 kills your user experience.

Solution: Always report percentiles. If p99 is more than 2x p50, investigate.

Pitfall 4: Underestimating Costs

Problem: You forget to account for input tokens, or you use old pricing, or you don’t account for retries.

Solution: Log every token. Use current pricing. Add a 20% buffer for retries and overhead.

Pitfall 5: Not Capturing Failures

Problem: A model fails silently (returns empty string, or garbage), and you don’t notice until production.

Solution: Log every response, even failures. Compute a failure rate for each model. Alert if it’s above 1%.

Pitfall 6: Running Too Few Examples

Problem: You test on 10 examples and declare a winner. Then in production, accuracy is 20% lower.

Solution: Test on at least 100 examples. More is better. Use stratified sampling to ensure coverage of edge cases.

Pitfall 7: Forgetting About Model Versions

Problem: You test GPT-4o on January 10. OpenAI updates it on January 15. You deploy on January 20. Behaviour has changed.

Solution: Pin model versions. Log the exact version you tested. Document when you’ll re-test after updates.

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Making This Repeatable for 2025 and Beyond

You’ll run this bake-off again. Probably multiple times. Here’s how to make it repeatable without losing your mind.

Codify the Framework

Turn your bake-off harness into a reusable library. Something like:

from model_bakeoff import ModelBakeOff, CostTracker, MetricsComputer

bakeoff = ModelBakeOff(
    test_data=load_data('claims_2025_q1.json'),
    models={
        'gpt-4o': OpenAIModel('gpt-4o'),
        'claude-sonnet': AnthropicModel('claude-3-5-sonnet'),
        'llama-405b': TogetherAIModel('meta-llama/Llama-3.1-405B-Instruct-Turbo'),
    },
    metrics=MetricsComputer.f1_score,
    cost_tracker=CostTracker(),
)

results = bakeoff.run()
results.plot_pareto_frontier()
results.export_report('bakeoff_2025_q1.html')

Open-source your harness internally. Make it easy for other teams to run their own bake-offs.

Maintain a Model Registry

Keep a living document of all models you’ve tested, with their characteristics:

Model	Released	Accuracy (Claims)	Latency (p50)	Cost	Last Tested	Notes
GPT-4o	Oct 2024	94%	120ms	$0.008	Jan 2025	Current prod
Claude 3.5 Sonnet	Jun 2024	96%	180ms	$0.006	Jan 2025	Close second
Llama 3.1 405B	Sep 2024	91%	110ms	$0.0009	Jan 2025	Open-source option

When a new model ships, add it to your test list for the next bake-off.

Set Up Continuous Evaluation

Don’t just bake-off every 3 months. Set up continuous evaluation on production data:

• Every week, sample 100 production requests.
• Run them through your current model and 2–3 challenger models.
• Track accuracy, latency, cost.
• Alert if a challenger beats your incumbent by >2% accuracy at similar cost.

This keeps you ahead of the curve. When a new model ships and it’s genuinely better, you’ll know within a week.

Document Decision History

Keep a log:

Jan 2025: Bake-off: GPT-4o (94%) vs. Claude Sonnet (96%) vs. Llama 3.1 405B (91%). Chose GPT-4o for cost and latency. Accuracy gap to Sonnet is acceptable for internal automation.

Apr 2025: Bake-off: GPT-4o (still 94%) vs. Claude 4 (new, 97%) vs. Llama 4 (new, 93%). Upgraded to Claude 4 due to accuracy improvement and price parity.

Jul 2025: Evaluated multimodal models for document understanding. Chose GPT-4o Vision over Claude 3.5 Vision due to lower cost and faster processing.

This history becomes institutional knowledge. New team members can read it and understand why you made each choice.

Automate Reporting

Generate a standard report after each bake-off:

def generate_report(results, output_file):
    html = f"""
    <html>
    <h1>Model Bake-Off Report</h1>
    <p>Date: {datetime.now().strftime('%Y-%m-%d')}</p>
    <h2>Results</h2>
    <table>{results.to_html()}</table>
    <h2>Pareto Frontier</h2>
    <img src="pareto.png" />
    <h2>Failure Analysis</h2>
    <ul>
    {failure_summary_html}
    </ul>
    <h2>Recommendation</h2>
    <p>{recommendation}</p>
    </html>
    """
    with open(output_file, 'w') as f:
        f.write(html)

This takes 30 minutes to set up once and saves you hours on every future bake-off.

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Getting Help: When to Bring in a Partner

Running a bake-off in-house is straightforward if you have:

• A data engineer or ML engineer on staff.
• Access to compute (GPUs or API budgets).
• A clear use case and test data.

But if you’re:

• A non-technical founder without engineering resources.
• An operator at a mid-market company modernising your AI stack.
• A team shipping fast and can’t afford to lose a week to evaluation.
• An enterprise pursuing SOC 2 or ISO 27001 compliance and need to audit your model choices.

…then bringing in a partner makes sense.

At PADISO, we’ve run 50+ model bake-offs for startups, enterprises, and PE-backed portfolio companies. We take your data, define your metrics, and deliver a decision memo by end of week. We also help with the harder parts: fine-tuning, A/B testing, and setting up production monitoring.

If you’re a seed-to-Series-B founder, our AI Quickstart Audit (fixed-fee AU$10K, 2 weeks) includes a model bake-off tailored to your use case. You’ll get a clear recommendation, a cost estimate, and a roadmap for the next 90 days.

If you’re an operator at a mid-market or enterprise company, we offer Fractional CTO & CTO Advisory in Sydney and across the US (San Francisco, Boston, Seattle, Austin, Atlanta). We can oversee your bake-off, mentor your team, and help you scale your AI stack.

If you’re building a platform or automating workflows, check out our AI & Agents Automation and Platform Design & Engineering services. We’ve shipped custom AI solutions for insurance, financial services, and media companies.

You can also take our AI Readiness Test (2 minutes, free) to see where you stand and what you should focus on first.

For a quick conversation about your specific situation, book a 30-minute call with one of our advisors.

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Summary and Next Steps

A model bake-off in 1 week is achievable. It’s not glamorous, but it’s one of the highest-ROI activities you can do when shipping AI features or automating operations.

The framework:

1. Pre-work: Gather representative data, define metrics, list candidates.
2. Days 1–2: Build a test harness (evaluation script, cost tracking, logging).
3. Days 3–4: Run all models in parallel, log everything.
4. Day 5: Analyse results, identify the Pareto frontier, make a call.
5. Days 6–7: Document, set up monitoring, plan the next bake-off.

Key principles:

• Use your actual data, not synthetic benchmarks.
• Track accuracy, latency, and cost—not just one metric.
• Automate everything so you can repeat this process every 3 months.
• Document your decision and failure modes for future reference.
• Set up production monitoring to validate your choice in the wild.

Next steps:

1. This week: Gather your test data (100–500 examples) and define your metrics.
2. Next week: Build your harness and run your first bake-off.
3. In 3 months: Run it again when new models ship.
4. Ongoing: Monitor production performance and set up continuous evaluation.

If you want to accelerate this process or need help with the harder parts (fine-tuning, compliance, platform engineering), PADISO’s CTO as a Service and AI Strategy & Readiness teams are here to help. We’ve shipped this for dozens of teams and can bring you across the finish line in weeks, not months.

You’ve got this. Start with Day 1 and ship.