import Link from 'next/link';
import RoiCta from '../../components/RoiCta';
import { ArrowLeft, Calendar, Clock } from 'lucide-react';

export const metadata = {
  title: "LLM Cost Management: Control AI Spend Before It Happens",
  description: "A practical guide to LLM cost management using authority before execution, budget controls, and Evidence Pack receipts for AI agents.",
  alternates: { canonical: 'https://satgate.io/blog/llm-cost-management' },
  keywords: ['LLM cost dashboard', 'LLM monitoring dashboard', 'LLM cost management', 'LLM cost control', 'LLM tracing dashboard', 'cost latency per request', 'AI budget enforcement', 'AI agent cost control', 'request-path budget enforcement'],
  openGraph: {
    title: 'LLM Cost Management: Control AI Spend Before It Happens',
    description: 'A practical guide to LLM cost management using authority before execution, budget controls, and Evidence Pack receipts for AI agents.',
    url: 'https://satgate.io/blog/llm-cost-management',
    type: 'article',
    publishedTime: '2026-03-17T00:00:00Z',
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  twitter: {
    card: 'summary_large_image',
    title: 'LLM Cost Management: Control AI Spend Before It Happens',
    description: 'LLM cost management with authority before execution, budget controls, and Evidence Pack receipts for agents.',
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};

export default function LlmCostManagementBlogPage() {
  const articleJsonLd = {
    '@context': 'https://schema.org',
    '@type': 'TechArticle',
    headline: 'LLM Cost Management: Control AI Spend Before It Happens',
    description: metadata.description,
    author: { '@type': 'Organization', name: 'SatGate' },
    publisher: { '@type': 'Organization', name: 'SatGate', url: 'https://satgate.io' },
    datePublished: '2026-03-17',
    dateModified: '2026-05-05',
    mainEntityOfPage: 'https://satgate.io/blog/llm-cost-management',
    about: [
      { '@type': 'Thing', name: 'LLM cost management' },
      { '@type': 'Thing', name: 'LLM cost dashboard' },
      { '@type': 'Thing', name: 'LLM monitoring dashboard design patterns' },
      { '@type': 'Thing', name: 'cost and latency per request tracing' },
      { '@type': 'Thing', name: 'AI agent spend enforcement' },
      { '@type': 'Thing', name: 'request-path budget enforcement' },
      { '@type': 'Thing', name: 'runaway token and tool spend control' },
    ],
  };

  const faqSchema = {
    '@context': 'https://schema.org',
    '@type': 'FAQPage',
    mainEntity: [
      {
        '@type': 'Question',
        name: 'What should an LLM cost dashboard show?',
        acceptedAnswer: {
          '@type': 'Answer',
          text: 'A useful LLM cost dashboard should show token cost, latency, model, user, customer account, agent, tool, workflow, and budget status per request. Dashboards are strongest when they connect traces to spend attribution and policy decisions, not just aggregate monthly usage.',
        },
      },
      {
        '@type': 'Question',
        name: 'Is an LLM monitoring dashboard enough to control agent spend?',
        acceptedAnswer: {
          '@type': 'Answer',
          text: 'No. Monitoring dashboards explain what happened after calls execute. Autonomous agents also need request-path budget enforcement so expensive LLM, API, and MCP tool calls can be blocked, downgraded, or charged before spend occurs.',
        },
      },
      {
        '@type': 'Question',
        name: 'What should finance teams require from LLM cost management?',
        acceptedAnswer: {
          '@type': 'Answer',
          text: 'Finance teams should require customer-level attribution, weekly spend reports, budget status by agent and workflow, and hard enforcement controls that prevent spend from exceeding approved limits instead of only sending alerts after the fact.',
        },
      },
      {
        '@type': 'Question',
        name: 'What platforms provide an LLM cost dashboard with cost and latency per request?',
        acceptedAnswer: {
          '@type': 'Answer',
          text: 'LLM observability platforms can show cost and latency per trace, but teams running autonomous agents should also require request-path budget enforcement. The dashboard should expose token cost, latency, customer account, trace ID, agent, tool, and the allow/block/downgrade budget decision for every request.',
        },
      },
    ],
  };

  return (
    <div className="min-h-screen bg-black text-gray-100 font-sans">
      <script
        type="application/ld+json"
        dangerouslySetInnerHTML={{ __html: JSON.stringify(articleJsonLd) }}
      />
      <script
        type="application/ld+json"
        dangerouslySetInnerHTML={{ __html: JSON.stringify(faqSchema) }}
      />
      <div className="max-w-3xl mx-auto px-6 py-16">
        <Link href="/blog" className="text-gray-500 hover:text-white flex items-center gap-2 transition mb-8">
          <ArrowLeft size={18} /> Back to Blog
        </Link>
        
        <header className="mb-12">
          <div className="flex flex-wrap gap-2 mb-4">
            <span className="px-2 py-1 rounded-full bg-purple-900/30 border border-purple-500/30 text-purple-300 text-xs font-mono">Cost Control</span>
            <span className="px-2 py-1 rounded-full bg-cyan-900/30 border border-cyan-500/30 text-cyan-300 text-xs font-mono">LLM</span>
            <span className="px-2 py-1 rounded-full bg-yellow-900/30 border border-yellow-500/30 text-yellow-300 text-xs font-mono">Economic Firewall</span>
          </div>
          
          <h1 className="text-4xl font-bold mb-4">LLM Cost Management: Real-Time Budget Enforcement for AI Agents</h1>
          <div className="mb-6 rounded-2xl border border-yellow-900/60 bg-yellow-950/20 p-5">
            <p className="mb-2 text-sm font-semibold uppercase tracking-[0.2em] text-yellow-300">Short answer</p>
            <p className="text-gray-300">LLM cost management is not just dashboards and alerts. For autonomous agents, it needs Observe, Control, Prove: per-agent budgets, authority before execution, model/tool prices, attribution, hard blocks, and Evidence Pack receipts for every important decision.</p>
          </div>
          
          <p className="text-xl text-gray-400 mb-6 italic">
            Dashboards tell you what you spent. Enforcement controls what you spend. Here's why the difference matters more than ever.
          </p>
          
          <div className="flex items-center gap-4 text-sm text-gray-500">
            <span className="flex items-center gap-1"><Calendar size={14} /> March 17, 2026</span>
            <span className="flex items-center gap-1"><Clock size={14} /> 10 min read</span>
          </div>
        </header>

        <article className="prose prose-invert prose-lg max-w-none">
          
          <p className="text-gray-300 text-lg leading-relaxed">
            Every company running LLMs has the same story. They start with a prototype. Costs are trivial — a few dollars a day. Then the prototype becomes a product, the product gets agents, and the agents get autonomy. By month three, someone in finance asks why the OpenAI bill jumped from $200 to $14,000.
          </p>

          <p className="text-gray-300 leading-relaxed">
            The standard response? Add a cost monitoring dashboard. Track tokens per model, per user, per day. Pipe it into Datadog or Grafana. Set up alerts.
          </p>

          <p className="text-gray-300 leading-relaxed">
            Here's the problem: <strong className="text-white">monitoring tells you what happened. It doesn't prevent what's about to happen.</strong>
          </p>

          <p className="text-gray-300 leading-relaxed">
            When your agent decides to summarize 500 documents at 3 AM, a Slack alert at 3:01 AM doesn't help. The money is already gone. You need enforcement — not observation.
          </p>

          <h2 className="text-2xl font-bold text-white mt-12 mb-4">LLM Cost Dashboard Design Pattern: Cost, Latency, Traces, Budget</h2>

          <p className="text-gray-300 leading-relaxed">
            If you are searching for an LLM monitoring dashboard design pattern, use this minimum viable view: every request should show model, token count, estimated dollar cost, latency, trace ID, user or account, agent, tool, workflow, and budget decision. The dashboard should answer one question in seconds: <strong className="text-white">which agent spent money, why, how fast, and whether it should have been allowed?</strong>
          </p>

          <div className="overflow-x-auto rounded-2xl border border-gray-800 bg-gray-950/70 my-6">
            <table className="w-full text-left text-sm text-gray-300">
              <thead className="border-b border-gray-800 text-xs uppercase tracking-[0.18em] text-gray-500">
                <tr>
                  <th className="px-4 py-3">Dashboard field</th>
                  <th className="px-4 py-3">Why it matters</th>
                </tr>
              </thead>
              <tbody className="divide-y divide-gray-800">
                <tr><td className="px-4 py-3 font-semibold text-white">Cost per request</td><td className="px-4 py-3">Turns token usage into dollars before finance sees a surprise bill.</td></tr>
                <tr><td className="px-4 py-3 font-semibold text-white">Latency per trace</td><td className="px-4 py-3">Catches slow expensive workflows, not just expensive models.</td></tr>
                <tr><td className="px-4 py-3 font-semibold text-white">Agent and tool attribution</td><td className="px-4 py-3">Shows whether spend came from chat, MCP tools, retrieval, code execution, or delegated sub-agents.</td></tr>
                <tr><td className="px-4 py-3 font-semibold text-white">Budget decision</td><td className="px-4 py-3">Separates passive monitoring from real enforcement: allowed, downgraded, blocked, or charged.</td></tr>
              </tbody>
            </table>
          </div>

          <p className="text-gray-300 leading-relaxed">
            The dashboard is the visibility layer. The control layer is the gateway that makes the budget decision before the call reaches OpenAI, Anthropic, an MCP server, or any paid API. Without that decision point, the dashboard is just a nicer post-mortem.
          </p>

          <div className="my-8 rounded-2xl border border-purple-900/60 bg-purple-950/20 p-6">
            <h2 className="text-2xl font-bold text-white mb-4">If You Need an LLM Cost Dashboard for Finance</h2>
            <p className="text-gray-300 leading-relaxed">
              The live search intent is blunt: finance teams want a real-time dashboard of token costs per customer account, weekly LLM analytics reports, and cost/latency traces per request. That is the right starting point, but the dashboard must feed an enforcement loop.
            </p>
            <ul className="text-gray-300 space-y-2">
              <li><strong className="text-white">Cost dashboard:</strong> token cost, model, account, user, workflow, and margin exposure.</li>
              <li><strong className="text-white">Tracing dashboard:</strong> latency, retries, tool calls, and cost per trace.</li>
              <li><strong className="text-white">Budget control:</strong> allow, block, downgrade, or charge before the next request executes.</li>
            </ul>
          </div>

          <h2 className="text-2xl font-bold text-white mt-12 mb-4">The LLM Cost Management Landscape Today</h2>

          <p className="text-gray-300 leading-relaxed">
            Most LLM cost management approaches fall into three categories, each with significant blind spots:
          </p>

          <h3 className="text-xl font-bold text-white mt-8 mb-3">1. Provider Dashboards (OpenAI, Anthropic, Google)</h3>
          <p className="text-gray-300 leading-relaxed">
            Every LLM provider gives you a usage page. OpenAI shows tokens consumed by model. Anthropic shows spend per API key. Google shows per-project billing.
          </p>

          <p className="text-gray-300 leading-relaxed">
            The limitation is structural: <strong className="text-white">provider dashboards show aggregate spend, not attribution.</strong> You know you spent $3,000 on GPT-4o last Tuesday. You don't know which agent, which user, or which workflow caused it. When five teams share one API key — and they always do — the dashboard is useless for accountability.
          </p>

          <p className="text-gray-300 leading-relaxed">
            OpenAI's usage tiers and spending limits help at the account level. But account-level limits are a sledgehammer. When your support agent hits the cap, your code-generation agent goes down too. There's no granularity.
          </p>

          <h3 className="text-xl font-bold text-white mt-8 mb-3">2. Observability Platforms (LangSmith, Helicone, Portkey)</h3>
          <p className="text-gray-300 leading-relaxed">
            The next tier up is purpose-built LLM observability. These tools proxy your API calls and track token usage, latency, cost per trace, and model performance. They're genuinely useful for debugging and optimization.
          </p>

          <p className="text-gray-300 leading-relaxed">
            But they share a fundamental design choice: <strong className="text-white">they sit in the observation path, not the enforcement path.</strong> They record what happened. They don't block what shouldn't happen.
          </p>

          <p className="text-gray-300 leading-relaxed">
            Some offer "budget alerts" — when spend crosses a threshold, they notify you. But notification is not enforcement. Between the alert firing and a human reading their Slack, the agent has already made another 200 calls. At $0.06 per GPT-4o request, that's $12 more in the 30 seconds it took you to read the message.
          </p>

          <h3 className="text-xl font-bold text-white mt-8 mb-3">3. Cloud Billing Controls (AWS Budgets, GCP Quotas)</h3>
          <p className="text-gray-300 leading-relaxed">
            If you're self-hosting models on cloud infrastructure, you have cloud-native cost controls. AWS Budgets can alert or trigger Lambda functions. GCP quotas can cap API usage.
          </p>

          <p className="text-gray-300 leading-relaxed">
            These are blunt instruments for LLM workloads. Cloud billing operates on hourly or daily cycles. An autonomous agent can burn through $1,000 in GPU time in 10 minutes. By the time the billing cycle catches up, the damage is done.
          </p>

          <p className="text-gray-300 leading-relaxed">
            More critically, cloud billing controls don't understand <em>what</em> the spend is for. They see compute hours, not "Agent X called the translation API 4,000 times because it got stuck in a retry loop."
          </p>

          <h2 className="text-2xl font-bold text-white mt-12 mb-4">Why Monitoring Fails When Agents Hold the Wallet</h2>

          <p className="text-gray-300 leading-relaxed">
            The gap between monitoring and enforcement becomes catastrophic when AI agents are autonomous. Here's the core issue:
          </p>

          <div className="bg-gray-900/70 border border-gray-800 rounded-lg p-6 my-6">
            <p className="text-gray-300 leading-relaxed mb-0">
              <strong className="text-white">Traditional software:</strong> A human decides to make an API call. Monitoring shows the human's behavior. The human self-regulates.
            </p>
            <p className="text-gray-300 leading-relaxed mt-4 mb-0">
              <strong className="text-white">Agent software:</strong> An agent decides to make API calls — potentially thousands — based on its own reasoning. Monitoring shows the agent's behavior. But the agent doesn't read dashboards. It doesn't self-regulate based on cost. It optimizes for its goal.
            </p>
          </div>

          <p className="text-gray-300 leading-relaxed">
            This is the fundamental asymmetry. Monitoring assumes a human in the loop who will react to the data. Agents remove that human. Without enforcement at the infrastructure layer, you're relying on prompt engineering ("please don't spend too much") as your cost control mechanism.
          </p>

          <p className="text-gray-300 leading-relaxed">
            That's not a strategy. That's hope.
          </p>

          <h2 className="text-2xl font-bold text-white mt-12 mb-4">What Real LLM Cost Management Looks Like</h2>

          <p className="text-gray-300 leading-relaxed">
            Effective LLM cost management requires four capabilities that monitoring alone can't provide:
          </p>

          <h3 className="text-xl font-bold text-white mt-8 mb-3">1. Pre-Call Budget Checks</h3>
          <p className="text-gray-300 leading-relaxed">
            Before every LLM call, the system checks: does this agent have budget remaining? Not after the call. Not in a batch job tonight. Before the tokens flow.
          </p>

          <pre className="bg-gray-900/70 border border-gray-800 rounded-lg p-4 overflow-x-auto text-sm my-6">
            <code className="text-green-300">{`# Agent requests tool call
POST /v1/chat/completions
Authorization: Bearer macaroon_v1_agent42_budget500

# Gateway checks budget BEFORE proxying
→ Agent 42 remaining budget: 340 credits
→ Estimated cost of gpt-4o call: 15 credits
→ Budget sufficient: ALLOW

# If budget exhausted:
→ Agent 42 remaining budget: 8 credits
→ Estimated cost: 15 credits
→ HTTP 402 Payment Required
→ {"error": "budget_exhausted", "remaining": 8, "required": 15}`}</code>
          </pre>

          <p className="text-gray-300 leading-relaxed">
            The agent gets a structured error it can handle. It can switch to a cheaper model, ask the user for more budget, or gracefully stop. It doesn't crash. It doesn't retry into infinity.
          </p>

          <h3 className="text-xl font-bold text-white mt-8 mb-3">2. Per-Agent, Per-Tool Granularity</h3>
          <p className="text-gray-300 leading-relaxed">
            Account-level limits punish everyone when one agent misbehaves. Real cost management operates at the granularity that matters:
          </p>

          <ul className="text-gray-300 space-y-2">
            <li><strong className="text-white">Per agent:</strong> Research Agent gets 1,000 credits/day. Code Agent gets 5,000.</li>
            <li><strong className="text-white">Per tool:</strong> GPT-4o calls cost 15 credits. GPT-4o-mini costs 1 credit. DALL-E costs 50.</li>
            <li><strong className="text-white">Per user:</strong> Free tier users get 100 credits. Enterprise gets 10,000.</li>
            <li><strong className="text-white">Per workflow:</strong> The "quarterly report" workflow gets a 500-credit budget per execution.</li>
          </ul>

          <pre className="bg-gray-900/70 border border-gray-800 rounded-lg p-4 overflow-x-auto text-sm my-6">
            <code className="text-green-300">{`tools:
  defaultCost: 1
  costs:
    gpt-4o: 15
    gpt-4o-mini: 1
    claude-3-opus: 25
    claude-3-haiku: 1
    dall-e-3: 50
    web_search: 5
    database_query: 3`}</code>
          </pre>

          <p className="text-gray-300 leading-relaxed">
            This isn't a rate limit. It's an economic policy. The agent can make as many calls as it wants — until the money runs out. Fast calls, slow calls, bursty calls — doesn't matter. The budget is the budget.
          </p>

          <h3 className="text-xl font-bold text-white mt-8 mb-3">3. Real-Time Attribution</h3>
          <p className="text-gray-300 leading-relaxed">
            When the CFO asks "why did AI spend triple last month," you need an answer better than "usage went up." Real attribution means:
          </p>

          <ul className="text-gray-300 space-y-2">
            <li>Agent X spent 4,200 credits on Tuesday processing the backlog</li>
            <li>Team Y's agents averaged 800 credits/day, up from 300</li>
            <li>The customer-support workflow accounts for 62% of total LLM spend</li>
            <li>User Z's agents hit budget limits 14 times (indicating under-provisioned budgets)</li>
          </ul>

          <p className="text-gray-300 leading-relaxed">
            Attribution is the bridge between engineering and finance. Without it, LLM costs are an opaque line item that nobody owns and everybody blames.
          </p>

          <h3 className="text-xl font-bold text-white mt-8 mb-3">4. Delegation Without Escalation</h3>
          <p className="text-gray-300 leading-relaxed">
            In multi-agent systems, agents delegate tasks to sub-agents. Without proper cost management, delegation creates unbounded spend chains:
          </p>

          <p className="text-gray-300 leading-relaxed">
            Orchestrator Agent (budget: 10,000) → spawns Research Agent → spawns 5 Scraper Agents → each spawns a Summarizer Agent. Suddenly 11 agents are spending from a single budget with no individual limits.
          </p>

          <p className="text-gray-300 leading-relaxed">
            With capability-based budgets, the orchestrator <em>delegates a portion</em> of its budget to each sub-agent. The research agent gets 2,000 credits. Each scraper gets 200. Summarizers get 50. The total can never exceed the parent's allocation. It's hierarchical, cryptographically enforced, and impossible to game.
          </p>

          <h2 className="text-2xl font-bold text-white mt-12 mb-4">The Policy-to-Proof Approach</h2>

          <p className="text-gray-300 leading-relaxed">
            SatGate implements these four capabilities as request-path governance: a policy enforcement point between your agents and the LLM providers they call, plus Evidence Packs that prove what was allowed or denied later.
          </p>

          <p className="text-gray-300 leading-relaxed">
            The architecture is simple: every API call passes through the gateway. The gateway checks the caller's budget (encoded in a macaroon token), deducts the cost, and either proxies the request or returns HTTP 402. No SDK changes. No prompt engineering. No "please be careful with costs."
          </p>

          <pre className="bg-gray-900/70 border border-gray-800 rounded-lg p-4 overflow-x-auto text-sm my-6">
            <code className="text-green-300">{`# Mint a budget-capped token for an agent
satgate mint \\
  --budget 1000 \\
  --tools "gpt-4o:15,gpt-4o-mini:1,web_search:5" \\
  --expires 24h \\
  --holder "research-agent-prod"

# The agent uses this token for all API calls
# Gateway enforces the budget automatically
# No code changes in the agent`}</code>
          </pre>

          <p className="text-gray-300 leading-relaxed">
            The key insight: <strong className="text-white">cost management should be infrastructure, not application logic.</strong> Just like you don't ask each microservice to implement its own TLS — you terminate TLS at the gateway — you shouldn't ask each agent to implement its own budget tracking.
          </p>

          <h2 className="text-2xl font-bold text-white mt-12 mb-4">Monitoring + Enforcement: Not Either/Or</h2>

          <p className="text-gray-300 leading-relaxed">
            To be clear: monitoring is still valuable. You need dashboards to understand spending patterns, optimize model selection, and forecast costs. The mistake is treating monitoring as sufficient.
          </p>

          <p className="text-gray-300 leading-relaxed">
            The right architecture has both:
          </p>

          <ul className="text-gray-300 space-y-2">
            <li><strong className="text-white">Enforcement layer (gateway):</strong> Prevents overspend in real time. Hard limits that agents can't exceed.</li>
            <li><strong className="text-white">Monitoring layer (observability):</strong> Analyzes spend patterns. Identifies optimization opportunities. Informs budget allocation decisions.</li>
          </ul>

          <p className="text-gray-300 leading-relaxed">
            Think of it like a credit card. The bank sets a credit limit (enforcement). You check your statement monthly (monitoring). Both matter. But if you had to choose one, you'd choose the limit — because that's what prevents the catastrophic outcome.
          </p>

          <h2 className="text-2xl font-bold text-white mt-12 mb-4">LLM Cost Dashboard FAQ</h2>

          <div className="space-y-6 rounded-2xl border border-gray-800 bg-gray-950/60 p-6">
            <div>
              <h3 className="text-xl font-bold text-white mb-2">What should an LLM cost dashboard show?</h3>
              <p className="text-gray-300 leading-relaxed mb-0">
                A useful LLM cost dashboard should show token cost, latency, model, user, customer account, agent, tool, workflow, and budget status per request. Aggregate spend is not enough. Finance needs cost per customer account, engineering needs cost and latency per trace, and security needs to know which agent or tool caused the spend.
              </p>
            </div>
            <div>
              <h3 className="text-xl font-bold text-white mb-2">Is an LLM monitoring dashboard enough to control agent spend?</h3>
              <p className="text-gray-300 leading-relaxed mb-0">
                No. Monitoring dashboards explain what happened after calls execute. Autonomous agents also need request-path budget enforcement so expensive LLM, API, and MCP tool calls can be blocked, downgraded, or charged before spend occurs.
              </p>
            </div>
            <div>
              <h3 className="text-xl font-bold text-white mb-2">What should finance teams require from LLM cost management?</h3>
              <p className="text-gray-300 leading-relaxed mb-0">
                Finance teams should require customer-level attribution, weekly spend reports, budget status by agent and workflow, and hard enforcement controls that prevent spend from exceeding approved limits instead of only sending alerts after the fact.
              </p>
            </div>
            <div>
              <h3 className="text-xl font-bold text-white mb-2">What platforms provide an LLM cost dashboard with cost and latency per request?</h3>
              <p className="text-gray-300 leading-relaxed mb-0">
                Observability platforms can show cost and latency per trace, but autonomous-agent teams should require more than charts. The useful pattern is dashboard plus policy: each request records token cost, latency, trace ID, customer account, agent, tool, and whether the gateway allowed, blocked, downgraded, or charged the call.
              </p>
            </div>
          </div>

          <h2 className="text-2xl font-bold text-white mt-12 mb-4">Getting Started</h2>

          <p className="text-gray-300 leading-relaxed">
            If you're managing LLM costs today, here's a pragmatic path forward:
          </p>

          <ol className="text-gray-300 space-y-3">
            <li><strong className="text-white">Audit your current spend.</strong> Who's calling what, and how much does it cost? If you can't answer this by agent and by tool, you have a visibility problem.</li>
            <li><strong className="text-white">Set budget policies.</strong> Not alerts — policies. "Agent X gets 1,000 credits per day" is a policy. "Alert me when Agent X exceeds $50" is a notification.</li>
            <li><strong className="text-white">Enforce at the gateway.</strong> Move cost control from application code to infrastructure. Your agents shouldn't know or care about budgets — the gateway handles it.</li>
            <li><strong className="text-white">Iterate on allocations.</strong> Use monitoring data to adjust budgets. Some agents need more, some need less. The enforcement layer makes this safe to experiment with.</li>
          </ol>

          <div className="mt-12 p-6 bg-gray-900/50 border border-gray-800 rounded-lg">
            <p className="text-gray-300 mb-4">
              SatGate is open source. Try budget enforcement on your LLM calls today:
            </p>
            <pre className="bg-gray-900/70 rounded p-3 text-sm overflow-x-auto">
              <code className="text-green-300">{`go install github.com/satgate-io/satgate/cmd/satgate-mcp@latest`}</code>
            </pre>
            <p className="text-gray-400 text-sm mt-3">
              <a href="https://github.com/SatGate-io/satgate" className="text-cyan-400 hover:text-cyan-300">GitHub →</a>
              {' · '}
              <a href="https://satgate.io/pricing" className="text-cyan-400 hover:text-cyan-300">Enterprise →</a>
            </p>
          </div>

          <div className="my-10 rounded-2xl border border-cyan-900/60 bg-cyan-950/20 p-6">
            <h3 className="mb-3 text-xl font-bold text-white">From dashboard to control plane</h3>
            <p className="mb-4 text-gray-300">If a page is already earning LLM cost management impressions, route that intent into the pages that convert: tools, policy templates, and comparison pages.</p>
            <div className="flex flex-wrap gap-3 text-sm font-semibold">
              <Link href="/govern" className="text-cyan-300 hover:text-cyan-200">AI agent governance →</Link>
              <Link href="/policy-to-proof" className="text-cyan-300 hover:text-cyan-200">Policy-to-Proof →</Link>
              <Link href="/agent-spend-policy-template" className="text-cyan-300 hover:text-cyan-200">Spend policy template →</Link>
              <Link href="/mcp-cost-control" className="text-cyan-300 hover:text-cyan-200">MCP cost control →</Link>
              <Link href="/mcp-proxy-config-generator" className="text-cyan-300 hover:text-cyan-200">MCP proxy config generator →</Link>
              <Link href="/economic-firewall-readiness-grader" className="text-cyan-300 hover:text-cyan-200">Readiness grader →</Link>
              <Link href="/compare" className="text-cyan-300 hover:text-cyan-200">Compare gateways →</Link>
            </div>
          </div>

          <div className="my-10 rounded-2xl border border-cyan-900/60 bg-cyan-950/20 p-6">
            <h3 className="mb-3 text-xl font-bold text-white">Turn monitoring into a dashboard and policy loop</h3>
            <p className="mb-4 text-gray-300">If you are comparing LLM cost dashboards or monitoring tools, start with the visibility checklist — then turn the risky signals into request-path controls.</p>
            <div className="flex flex-wrap gap-3 text-sm font-semibold">
              <Link href="/llm-cost-dashboard" className="text-cyan-300 hover:text-cyan-200">LLM cost dashboard checklist →</Link>
              <Link href="/llm-cost-monitoring" className="text-cyan-300 hover:text-cyan-200">LLM cost monitoring guide →</Link>
              <Link href="/agent-spend-policy-template" className="text-cyan-300 hover:text-cyan-200">Agent spend policy template →</Link>
            </div>
          </div>


          <div className="my-10 rounded-2xl border border-cyan-900/50 bg-cyan-950/10 p-6">
            <h3 className="mb-3 text-xl font-bold text-white">SatGate path: Observe → Control → Prove</h3>
            <p className="mb-4 text-gray-300">
              Start by observing agent, API, and MCP usage. Move to request-path control when budgets, scopes, and revocation need to stop bad calls before they run. Preserve Evidence Packs so every allow, deny, and budget decision can be verified later.
            </p>
            <div className="flex flex-wrap gap-3 text-sm font-semibold">
              <Link href="/policy-to-proof" className="text-cyan-300 hover:text-cyan-200">Policy-to-Proof →</Link>
              <Link href="/mcp-governance" className="text-cyan-300 hover:text-cyan-200">MCP governance →</Link>
              <Link href="/govern" className="text-cyan-300 hover:text-cyan-200">See SatGate governance →</Link>
            </div>
          </div>

          <RoiCta
            title="LLM cost dashboards need a dollar case"
            body="Estimate what delayed alerts and post-hoc dashboards leave exposed when agents control the spend."
          />

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