The AI Evals PLG Illusion: Why Deployment Blindness Kills Accuracy

The AI Evals PLG Illusion: Why Deployment Blindness Kills Accuracy

Most AI evals companies built product-led growth products that can't see how companies actually deploy AI, leading to evaluations that are dangerously wrong.

They sell you a dashboard. You upload your model. They give you a score. You feel confident.

But that score is bullshit.

The PLG Trap: Evals Without Context

Product-led growth worked for tools like Slack and Notion. Users sign up, try the product, expand usage, convert to paid.

AI evals companies copied this playbook. "Upload your model, get instant evaluation!" they promised.

The problem? AI evaluation isn't like project management software. The quality of your evaluation depends entirely on understanding how that AI system will be deployed.

PLG evals are blind to deployment reality.

The Deployment Methodology Gap

Here's what most evals companies miss:

1. The Inference Environment

• What they test: Model accuracy on clean benchmark data
• Reality: Your model runs on user-generated data with edge cases, noise, and adversarial inputs
• The gap: A model that scores 95% on benchmarks might drop to 60% in production

2. The Latency Constraints

• What they test: Accuracy with unlimited time
• Reality: Your users expect responses in <500ms
• The gap: You trade 20% accuracy for 10x faster inference

3. The Cost Trade-offs

• What they test: Pure accuracy metrics
• Reality: Every token costs money, every API call has limits
• The gap: Your "best" model might cost 5x more than your "good enough" model

4. The Integration Complexity

• What they test: Standalone model performance
• Reality: Your AI is part of a larger system with caching, fallbacks, and error handling
• The gap: Individual model accuracy doesn't predict system-level reliability

Real Examples of Deployment Blindness

The Recommendation Engine Disaster

A major e-commerce company evaluated three recommendation models:

• Model A: 92% accuracy on benchmark data, scored highest in evals
• Model B: 87% accuracy on benchmark data
• Model C: 85% accuracy on benchmark data

They deployed Model A. It crashed their entire product catalog page.

What the evals missed: Model A required 2GB of memory and 3-second inference time. Their infrastructure could only handle 500MB and 200ms responses.

Model B worked perfectly in production, improving conversion by 15%.

The Chatbot Accuracy Myth

A SaaS company tested chatbot models for customer support:

• Model X: 94% accuracy on test conversations
• Model Y: 89% accuracy on test conversations

They chose Model X. Customer satisfaction dropped 25%.

What the evals missed: Model X was trained on formal, grammatically correct conversations. Their customers used slang, abbreviations, and industry jargon.

Model Y was trained on real customer data and handled the messiness of actual human communication.

The Cost Optimization Blind Spot

A content generation startup evaluated language models:

• Model P: 96% quality score, highest rated
• Model Q: 91% quality score
• Model R: 88% quality score

They deployed Model P. Their cloud costs tripled, burning through runway.

What the evals missed: Model P was a 70B parameter model requiring A100 GPUs. Model Q was a fine-tuned 7B model that ran on cheaper hardware.

The quality difference was imperceptible to users, but the cost difference was existential.

The PLG Product Design Problem

PLG products are designed for self-service adoption. This creates fundamental limitations for AI evaluation:

1. No Deployment Context Collection

PLG tools ask: "Upload your model, get results." They don't ask: "How will you deploy this? What's your infrastructure? What's your latency budget?"

2. Generic Benchmark Data

PLG tools use public benchmarks because they're easy to implement. They don't use domain-specific data because they don't know your domain.

3. Accuracy-Only Metrics

PLG tools focus on accuracy because it's easy to measure and understand. They ignore latency, cost, and reliability because those require deployment context.

4. No Longitudinal Evaluation

PLG tools give you a point-in-time score. They don't track how your model performs as data distributions shift, as usage patterns change, as you optimize for cost.

What Deployment-Aware Evals Look Like

Here's what a real AI evaluation system would include:

1. Deployment Environment Simulation

interface DeploymentConfig {
  latencyBudget: number;      // Max response time in ms
  costBudget: number;         // Max cost per request
  memoryLimit: number;        // Available memory
  concurrency: number;        // Expected concurrent requests
  dataDistribution: string;   // Type of input data
}

2. Multi-Dimensional Scoring

Instead of single accuracy score:

interface EvalScore {
  accuracy: number;           // Traditional accuracy
  productionAccuracy: number; // Accuracy with real data
  latencyScore: number;       // Performance within budget
  costEfficiency: number;     // Cost per useful output
  reliabilityScore: number;   // Uptime and error handling
  overall: number;           // Weighted combination
}

3. Deployment Scenario Testing

• Cold start performance: How long to load the model
• Memory pressure: Performance under memory constraints
• Concurrent load: How it handles multiple requests
• Error recovery: Behavior when things go wrong
• Data drift: Performance as input distribution changes

4. Cost-Accuracy Trade-off Analysis

// Instead of "best model", show trade-off curves
const tradeOffs = {
  maxAccuracy: { accuracy: 0.95, cost: 0.10, latency: 2000 },
  balanced: { accuracy: 0.88, cost: 0.03, latency: 500 },
  costOptimized: { accuracy: 0.82, cost: 0.01, latency: 200 }
};

The Implementation Framework

Here's how to build deployment-aware AI evaluation:

Phase 1: Context Collection

function collectDeploymentContext(modelId: string) {
  return {
    infrastructure: getInfrastructureDetails(),
    usage: getUsagePatterns(),
    constraints: getBusinessConstraints(),
    data: getDataDistribution()
  };
}

Phase 2: Scenario-Based Testing

function runDeploymentScenarios(model: Model, context: Context) {
  return {
    coldStart: testColdStart(model, context),
    peakLoad: testPeakLoad(model, context),
    dataDrift: testDataDrift(model, context),
    errorRecovery: testErrorRecovery(model, context)
  };
}

Phase 3: Production Simulation

function simulateProduction(model: Model, context: Context) {
  const simulation = new ProductionSimulator(context);
  return simulation.run(model, {
    duration: '7d',
    loadPattern: context.usage.pattern,
    failureInjection: true
  });
}

Phase 4: Recommendation Engine

function generateRecommendations(results: EvalResults) {
  return {
    primary: selectBestModel(results),
    alternatives: generateAlternatives(results),
    optimizations: suggestOptimizations(results),
    monitoring: setupMonitoringAlerts(results)
  };
}

The Business Impact of Better Evals

Companies that understand deployment get different results:

Startup Survival

• PLG evals: "Our model scores 94% accuracy!"
• Deployment-aware: "Our model costs $0.02/request, responds in 300ms, and maintains 89% accuracy with real user data"

Enterprise Adoption

• PLG evals: Generic benchmarks that don't match enterprise use cases
• Deployment-aware: Evaluations that account for enterprise security, compliance, and integration requirements

Product Strategy

• PLG evals: Focus on model improvement
• Deployment-aware: Focus on system optimization, cost reduction, and reliability

The Path Forward

The AI evals market needs to evolve beyond PLG. Here are the steps:

1. Build Deployment Context Collection

Stop asking for model uploads. Start asking about deployment environments.

2. Create Domain-Specific Benchmarks

Public benchmarks are useful, but domain-specific evaluation is essential.

3. Implement Multi-Dimensional Scoring

Accuracy is important, but it's not the only metric that matters.

4. Enable Longitudinal Evaluation

Evaluation isn't a one-time event. It's an ongoing process.

5. Focus on Business Outcomes

The goal isn't better model scores. It's better business results.

What You Should Do Today

1. Audit your current evals: What deployment context are you missing?
2. Document your constraints: Latency budgets, cost limits, infrastructure details
3. Test with real data: Don't rely on synthetic benchmarks
4. Monitor production performance: Track how your models actually perform
5. Build evaluation into deployment: Make evaluation part of your release process

The Bottom Line

PLG worked for collaboration tools because context didn't matter. You can evaluate a project management tool without knowing how the team works.

AI is different. Context is everything.

The evals companies that win will be the ones that understand deployment reality, not just model accuracy.

Your AI systems deserve better than blind evaluation. Your business depends on it.

Stop using PLG evals that can't see your deployment reality. Start evaluating AI systems the way you'll actually deploy them.