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<title>AgentShield vs Weights & Biases: Spend Control vs ML Experiment Tracking</title>
<meta name="description" content="W&B tracks ML experiments. AgentShield blocks runaway AI agent spending. Experiment tracking vs production guardrails, different stages, complementary tools.">
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<a href="https://agentshield.fly.dev">🛡️ AgentShield</a>
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<h1>AgentShield vs Weights & Biases: Spend Control vs ML Experiment Tracking</h1>
<p style="font-size:1.1em;color:var(--muted);margin-bottom:32px">W&B tracks ML experiments. AgentShield blocks runaway AI agent spending. Experiment tracking vs production guardrails, different stages, complementary tools.</p>

<h2>What Weights & Biases Does</h2>
<p>Weights & Biases is the leading ML experiment tracking and model monitoring platform. It logs training runs, tracks hyperparameters, visualizes metrics, and provides model registry and evaluation tools.</p>

<h2>What AgentShield Does</h2>
<p>AgentShield operates at a different layer: production API call enforcement. When your trained model or agent is making real API calls that cost real money, AgentShield ensures costs stay within budget.</p>

<h2>Feature Comparison</h2>
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<thead><tr><th>Capability</th><th>AgentShield</th><th>Weights & Biases</th></tr></thead>
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<tr><td>Blocks transactions before execution</td><td><span class="feature-check">✅ <1ms eval</span></td><td><span class="feature-x">❌ Not a firewall</span></td></tr>
<tr><td>Transaction limit rules</td><td><span class="feature-check">✅ Per-call caps</span></td><td><span class="feature-x">❌ Not applicable</span></td></tr>
<tr><td>Daily spend caps</td><td><span class="feature-check">✅ Per-agent caps</span></td><td><span class="feature-x">❌ Usage tracking only</span></td></tr>
<tr><td>Experiment tracking</td><td><span class="feature-check">❌ Runtime enforcement</span></td><td><span class="feature-x">✅ Core feature</span></td></tr>
<tr><td>Model registry</td><td><span class="feature-check">❌ Not an ML platform</span></td><td><span class="feature-x">✅ Full registry</span></td></tr>
<tr><td>Training visualization</td><td><span class="feature-check">❌ Not a viz tool</span></td><td><span class="feature-x">✅ Rich dashboards</span></td></tr>
<tr><td>API cost enforcement</td><td><span class="feature-check">✅ Core feature</span></td><td><span class="feature-x">❌ Not available</span></td></tr>
<tr><td>Zero dependencies</td><td><span class="feature-check">✅ Python stdlib only</span></td><td><span class="feature-x">❌ Requires SDK</span></td></tr>

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<h2>When to Use Weights & Biases</h2>
<ul style="color:#ccc;margin-bottom:20px;padding-left:20px">
<li>You are training models and need experiment tracking</li><li>You want hyperparameter optimization and visualization</li><li>Model registry and versioning is your priority</li>
</ul>

<h2>When to Use AgentShield</h2>
<ul style="color:#ccc;margin-bottom:20px;padding-left:20px">
<li>Your AI agents are in production incurring API costs</li><li>You need spend limits that block, not just track</li><li>You want per-agent, per-merchant budget controls</li>
</ul>

<h2>Use Both Together</h2>
<p>Track training experiments with W&B. Protect production spend with AgentShield. Different stages of the ML lifecycle.</p>
<div class="callout">
<h2 style="margin-top:0">The $2,800 Test</h2>
<p>If an AI agent makes 21 API calls at $133 each in 60 seconds at 3 AM:</p>
<p><strong style="color:var(--danger)">Weights & Biases</strong> logs it. You see the damage in the morning dashboard.</p>
<p><strong style="color:var(--accent)">AgentShield</strong> blocks it in under 1ms. The agent never spends the money.</p>
<p style="margin-top:12px"><strong>Prevention costs $19/month. A single runaway agent costs $2,800 in 60 seconds.</strong></p>
<a href="/dashboard" class="cta">Start 14-Day Free Trial →</a>
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