Agentery pricing intelligence · Provider profile · Sophia Architecture
Provider profile · independently tracked by Agentery
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Sophia Architecture

sophialabs.gantlett.io official website

provides architectural patterns and DevOps guidance for building AI coding agents

content-communityPricing checked daily
Agentery value verdictPriced near the marketfrom $14/mo
Observed entry price$14/molowest observed monthly
Billing modelfreemium
Last checked28 Jul 2026pricing & liveness

What it does

The specific capability behind this listing, and where to get it.

Sophia Architecture

provides architectural patterns and DevOps guidance for building AI coding agents

MCP queries, chapter requests, search terms, pattern lookups → architectural documentation, code examples, DevOps patterns, handbook chapters
MCPMCPautonomy: infrastructure

Pricing & plans

Observed public pricing for Sophia Architecture, benchmarked against comparable providers. Plans, tiers, history and scenario below.

Pricing · observed daily
Subscription · market position

$14 /mo

15.2% below the Individual flat median (4 providers, USD). Cheaper than 1 of 5 comparable Individual providers.
this agent · $14
$8median $16.50$33
observed 2026-07-11 · rechecked daily · source evidence retained
What should this agent charge?

The current price is deliberately competitive.

pricing recommendation
$14
$8$33
$16.50

$14 sits below the observed Individual flat range ($14.03–$18.97) — around the 42th percentile of observed prices. It reads as a value play; make sure the margin still works.

Pricing checks returned

Why Agentery reaches that view

price_benchmark

15.2% below the Individual flat median.

get_agent_profile · plan history

Subscription observed 2026-07-11.

pricing recommendation

~42nd percentile of comparable Individual flat plans.

confidence

thin (thin cohort); source page rechecked daily.

Interactive provider scenario

Test a different price for this plan.

Move the proposed monthly price. Agentery recalculates the provider’s market position and explains the likely percentile.

$14 / month
$8 lowestmedian $16.50$50 highest
Competitive value position. At $14/month, this plan is 15.2% below the niche median.
14th
-15.2%
$8$33

Is Sophia Architecture good value?

How its price compares with genuinely comparable providers.

Priced near the market

Priced near the market for its buyer tier.

Benchmarked against comparable providers at the same buyer tier and billing unit — the entry plan sits 8% around the observed median.

Observed commercial price$14/mo
NicheDocument QA Agent
Price benchmarkapplicable
How it compares

Compared with Document QA Agent

Positioned against the observed p25 / median / p75 of comparable providers at the same buyer tier and billing unit. See the plans above for the exact percentile and the full niche market for peers.

View the full niche →

Comparable Document QA Agent

Alternatives in the same niche, with observed price and liveness where available.

View the full niche →
See the MCP response behind this page · get_agent_profile()
See the MCP response behind this pageget_agent_profile
{
  "agent_id": "architecture",
  "name": "Sophia Architecture",
  "url": "https://sophialabs.gantlett.io",
  "logo": "https://agentery.com/logos/CP-FRS7W7-128.png",
  "niche": "document-qa-agent",
  "category": "research-knowledge-work",
  "short_summary": "provides architectural patterns and DevOps guidance for building AI coding agents",
  "task_performed": "provides architectural patterns and DevOps guidance for building AI coding agents",
  "inputs_accepted": [
    "MCP queries",
    "chapter requests",
    "search terms",
    "pattern lookups"
  ],
  "outputs_produced": [
    "architectural documentation",
    "code examples",
    "DevOps patterns",
    "handbook chapters"
  ],
  "integrations_available": [
    "MCP"
  ],
  "protocols_or_interfaces": [
    "MCP"
  ],
  "industry_fit": [
    "developer tools"
  ],
  "autonomy_level": "infrastructure",
  "human_approval_needed": "unclear",
  "pricing_model": "freemium",
  "price": {
    "observed": true,
    "billing": "subscription",
    "currency": "USD",
    "lowest_monthly_usd": 14,
    "monthly_usd": 14,
    "headline": "From $14/mo",
    "summary": "From $14/mo. The homepage shows a $14/month option and one-time purchase options priced at $49 and $79, with a free MCP trial mentioned.",
    "confidence": "medium",
    "source_url": "https://sophialabs.gantlett.io/",
    "checked_at": "2026-07-28T05:18:34.352Z",
    "amount": 14,
    "display": "From $14/mo",
    "plans": [
      {
        "name": "Monthly",
        "price": "$14/mo",
        "period": "month",
        "persona": "individual",
        "highlights": [],
        "price_annual": null
      },
      {
        "name": "One-time",
        "price": "$49 one-time",
        "period": "one-time",
        "persona": "pro",
        "highlights": [],
        "price_annual": null
      },
      {
        "name": "One-time",
        "price": "$79 one-time",
        "period": "one-time",
        "persona": "pro",
        "highlights": [
          "21 chapters",
          "91 pages",
          "22K+ words",
          "4 MCP tools"
        ],
        "price_annual": null
      }
    ],
    "source": "render+llm"
  },
  "trust_or_rating_signal": [],
  "evidence_quality": "high",
  "entity_type": "content-community",
  "regulated_data_suitability": "unclear",
  "evidence_urls": [
    "https://sophialabs.gantlett.io"
  ],
  "last_checked": "2026-06-19",
  "how_to_connect": {
    "website": "https://sophialabs.gantlett.io",
    "docs": null,
    "mcp": null,
    "a2a": null,
    "api": null,
    "protocols": [
      "MCP"
    ],
    "note": "Speaks MCP but publishes no endpoint we could verify — check the docs/website."
  },
  "liveness": {
    "probed": true,
    "alive": true,
    "endpoint_kind": "site",
    "latency_ms": 630,
    "uptime_7d": 0.88,
    "checked_at": "2026-07-28T02:31:13.645Z",
    "consecutive_failures": 0,
    "status": "alive"
  },
  "price_extras": {
    "free_tier": null,
    "unit_cost": null
  },
  "reported_success": null,
  "feedback": "If you use this listing, call report_outcome afterwards — it sharpens rankings for everyone including you."
}