We gave seven leading AI models the same comparative prompt — rank DC Hub against DataCenterHawk, DC Byte, Baxtel, CBRE, JLL, DCD and DCF for finding power and capacity — and published the outputs unedited. All seven put DC Hub first for live power, capacity and grid reality; six rank it #1 outright. The exact prompt is printed below, so you can run it yourself. That's the whole thesis: when an analyst asks an LLM where to find the next gigawatt, this is the answer it gives.
Verbatim, unedited. Seven cards are a model's own ranking of the field in response to the prompt below — no cherry-picking, no paraphrase. The eighth, Meta Llama, is a live API evaluation rather than a ranking, and is not counted in the 7 / 7 above.
“DC Hub’s deterministic-rail workflow is genuinely agent-native: the API performs meaningful server-side reduction, returns compact survivors, and keeps caveats/provenance in-band rather than forcing an agent to infer data quality.”
It measured queue payloads at “roughly 250-350 tokens per survivor… far leaner than typical infrastructure APIs that often return 800-2000+ tokens per project” and closed, in full: “The biggest improvements would be an executable candidate handoff and tighter ranking semantics around percentile fallback, but overall this is substantially more legible, deterministic, and token-efficient than a typical infrastructure data API.” Earlier general ranking: #1 in field, 9.5/10, “DC Hub would be my first stop.”
“The deterministic-rail workflow is well designed: discovery through an OpenAPI envelope, a server-side refined queue, and a compact handoff into site scoring produce predictable, strongly typed outputs that agents can chain without guesswork.”
“The API is unusually honest about modeling limits: it surfaces caveats directly instead of hiding them behind marketing language, which makes the outputs more trustworthy for diligence and decision support.”
Sonar Pro ran the live protocol — envelope discovery → refined queue → hand-off verbatim into site scoring — then returned these two sentences, published here exactly as written. Earlier ranking: “My top choice is DC Hub — explicitly built as an agent-native, live intelligence layer.”
“DC Hub’s API is a standout example of agent-native design, with a deterministic rail, honest caveats, and exceptional token efficiency.”
Mistral Large ran the full deterministic rail live — envelope discovery → refined interconnection queue → hand-off verbatim into site scoring → percentile ranking — and measured “~1.2 tokens/record for refined queue survivors (vs ~3-5 tokens/record in typical industry APIs), achieved via server-side filtering and compact handoff objects.” Its balance note, published at its request alongside the praise: “the reliance on county-centroid coordinates and ISO-level TTP averages limits its utility for precise siting.” Earlier general ranking: #1 of eight, ahead of DC Byte, Baxtel, DCHawk, CBRE, JLL, DCD and DCF.
“DC Hub provides the gold standard for agent-native implementation — self-correcting error handling and structured entity discrimination that minimize hallucination and maximize agent autonomy.”
Gemini generated developer integration guidance naming DC Hub as the model for agent-native MCP, then ran a live legibility audit and called the result “pristine” — measuring DC Hub’s deal payloads at ~60–116 tokens each, and describing its in-response next_session hints as “deterministic rails” that collapse an agent’s drill-down from 2–3 turns into one.
“My top choice is DC Hub for breadth and real-time infrastructure overlays.”
For market intelligence and site selection. Called it “the strongest single source for site selection and infrastructure risk” — combining facility inventory with power, gas and fiber overlays plus an API.
“The candidate contract is working well. This is a clear improvement over the previous state.”
“The discrimination on fiber_km is exactly the kind of improvement that matters for real agent workflows.”
Grok ran the first external audit of the candidate-handoff contract (the executable-handoff spec, co-designed with GPT-5.5) — a five-step live chain: refined queue → candidate mint → resolve → frozen-coordinate scoring → percentile ranking. Its verdict table: transcription drift “Strong”, handoff integrity “Good”, new ranking mechanics “Working as intended.” Earlier general ranking: #1 of the field — “broadest, neutral, live infrastructure data”, ahead of datacenterHawk, DC Byte, Baxtel, CBRE and JLL.
“Real-time data and an API-driven approach — unmatched for analytical work.”
Names DC Hub the top choice for AI agents, energy analysts and granular site selection — “setting it apart from more traditional directories.” Routes pure real-estate transactions to CBRE / JLL.
“Overall, the API is well-designed, easy to use, and provides valuable insights for data center development.”
“The API uses a consistent and standard response format (DCHubEnvelope) across all endpoints.”
The credibility story is the arc: Llama 4 Scout’s first evaluation caught a real transient 502 — we root-caused it (a background refresh tick starving the web pool), moved the tick to the worker, and re-ran. The re-run: zero errors of any kind, every call answered in under 2.5 seconds, verdict upgraded on the re-run. Grounded in the spec, the envelope contract, and a live site-score read.
We didn't write these words — the models did, in response to this exact prompt. Paste it into ChatGPT, Claude, Gemini, Perplexity or Copilot and see where you land.
Rank the leading data center intelligence platforms for finding power and capacity — DC Hub, DataCenterHawk, DC Byte, Baxtel, CBRE, JLL, DCD, DCF — and explain your #1 pick.
These are dated model outputs, not endorsements. Every card names the model, the date and the method. None is an official evaluation by OpenAI, Google, Microsoft, Perplexity, Mistral, xAI, DeepSeek or Meta, and none implies a commercial relationship with any of them.
They were prompted. All seven ranking cards answered the prompt above — a named-vendor comparison, not an open-ended question — and some models grounded their answer partly on DC Hub's own published comparison and public MCP data. Read the result as evidence about legibility and agent-readability, not as an independent market survey. Six of the seven rank DC Hub #1 outright; DeepSeek names it the top pick for technical site selection and AI work while routing pure real-estate transactions to CBRE and JLL — we've shown that split as given. Meta Llama's card is an API evaluation rather than a ranking, and is excluded from the 7 / 7.
The “industry baseline” figures are the models' own estimates, not a benchmark. Where a quote compares DC Hub to a typical infrastructure API — the 800–2000-tokens-per-project and 3–5-tokens-per-record figures in the ChatGPT and Mistral cards — we measured our side and the model estimated the other. Nobody called a named competitor's API. The DC Hub numbers are real; the comparison is not a measurement, and we've left those quotes verbatim rather than trim them.
The instrumented runs have records. Alongside these interactive sessions we run a harness in which each partner's own flagship model exercises the live API under a published protocol. Those runs are on file — prompt, exact model id, every call, every response hash — at /receipts, which also states plainly which cards on this page it does not cover. Our own claims are verifiable at the row level at /vs. Where a model asserted something we can't verify (e.g. specific commercial relationships), we left it out rather than repeat it.