Digraph vs AthenaHQ pricing
| Plan detail | Digraph | AthenaHQ |
|---|---|---|
| Lowest published price | Free for 14 days, then $99/mo | Free |
| Plan and billing | Standard, billed monthly | Essential; Starter $295/mo |
| Allowance | 30 prompts, 1 brand | 300 credits (1 credit = 1 response) |
| AI coverage | Supported AI models | 11 models on Starter |
| Next plan | Pro $249/mo: 300 prompts, 3 brands | See AthenaHQ’s pricing page |
Lowest published plan for each vendor; billing terms differ. See which AI systems Digraph checks and how.
Features compared
| Capability | Digraph | AthenaHQ |
|---|---|---|
| Published entry pricing | Standard: $99/month for 30 tracked prompts and 1 brand. | Essential: free, with $25 free credit and 300 credits; AthenaHQ defines one credit as one AI response. |
| Paid self-serve plan | Pro: $249/month for 300 prompts, 3 brands, with supported model APIs. | Starter: $295/month with $300/month free credit, 3,600 credits, and visibility across 11 models; API and extra credits are paid add-ons. |
| Model and evidence scope | Inspect prompts, full answers, recommendations, competitor mentions, cited URLs, source domains, and changes over time. | AthenaHQ lists ChatGPT, Perplexity, Google AI Overviews and AI Mode, Gemini, Claude, Copilot, Grok, DeepSeek, Meta AI, Mistral, and additional models on request; verify which response, citation, and prompt records are exportable. |
| Optimization workflow | Route observed visibility gaps into audits, analysis, Signal, and agent workflows tied to prompt and source evidence. | AthenaHQ lists content recommendations, a content optimization agent, self-learning content improvement, on-page and off-page actions, and enterprise recommendation or citation engines. |
| Competitors and citations | Compare competitor mentions, ranking context, cited URLs, source domains, and answer changes. | AthenaHQ lists sources and competitor insights, share-of-voice monitoring, citation analysis, and alerts; validate the underlying answer set and source-level export. |
| Access and governance | Reports and exports are public-plan capabilities; API access is listed for Enterprise. | AthenaHQ lists unlimited members on Essential and Starter, CSV export and integrations on Starter, and Enterprise SSO, audit logs, multi-region or multi-language support, custom access controls, and BI dashboards. |
Key differences between Digraph and AthenaHQ
AthenaHQ and Digraph both monitor how AI systems mention brands, cite sources, and position competitors. AthenaHQ emphasizes credit-based cross-platform tracking, content optimization, competitor and source insights, hallucination or claim review, and enterprise governance. Digraph emphasizes a defined prompt portfolio, full answer evidence, citations, competitors, and connected analysis workflows across supported model APIs. Their published units and automation boundaries differ, so a controlled prompt-and-response pilot is essential.
Credits, prompts, and responses are not interchangeable
AthenaHQ defines one credit as one AI response and publishes 300 credits on Essential and 3,600 on Starter. Digraph publishes tracked prompts and brands, with 30 prompts on Standard and 300 on Pro across supported model APIs. A prompt can produce multiple model responses, so compare the exact number of prompts, model calls, categories, markets, and stored answers rather than the headline quota.
Compare a broad optimization suite with answer-level evidence
AthenaHQ positions content recommendations, citation analysis, competitor share of voice, alerts, and optimization agents as a connected action layer. Digraph emphasizes the prompt, full answer, recommendation, cited URL, source domain, competitor context, and follow-up analysis behind a finding. Give both tools the same citation or missed-recommendation problem and record whether a reviewer can trace the proposed action back to evidence.
Model coverage needs a like-for-like test
AthenaHQ lists 11 models on Starter and additional models on request, while Digraph lists supported AI models on Standard and Pro. The names alone do not establish equivalent model versions, query modes, geography, or sampling. Run the same dated prompt set across the overlapping engines, record the full answers and citations, and document any differences in refresh or credit accounting.
Separate included features from paid add-ons
AthenaHQ says API access and extra credits are optional paid add-ons on Starter, while Enterprise adds governance, custom access, BI, and recommendation or citation capabilities. Digraph lists reports and exports on public plans and API access on Enterprise. Confirm export fields, API limits, integrations, user permissions, archive retention, and any credit overage before choosing an operating model.
Run a controlled side-by-side pilot
Use the same brand, competitors, categories, markets, language, buyer questions, active engines, collection window, and reporting format. Record full answers, model and prompt context, brand position, competitor mentions, sentiment or claim labels, cited URLs, source domains, proposed actions, refresh time, and resource consumed. Compare one recurring report and one verified follow-up action before choosing the operating model.
Need the work done, not another tool?
A tracking tool tells you where you stand in AI answers; it does not write the content, fix the site or earn the mentions that change them. Digraph is an AI marketing agency: we measure on our own platform and do that work for you, starting with a one-off audit.
See our AEO agency servicesHow to evaluate both
This documentation-based comparison was reviewed September 21, 2026; it is not a hands-on benchmark. AthenaHQ’s public site lists a free Essential plan with 300 credits, Starter at $295/month with 3,600 credits and visibility across 11 models, and custom Enterprise pricing; it defines one credit as one AI response and says API access and extra credits are paid add-ons on Starter. Digraph lists Standard at $99/month for 30 prompts and 1 brand and Pro at $249/month for 300 prompts and 3 brands across supported model APIs. Credits, prompts, responses, brands, and model coverage are different units. Normalize the exact run volume, engines, evidence, content workflows, and access terms before comparing value.
Sources reviewed
AthenaHQ’s capabilities are summarized from the official sources below. This is a documentation-based comparison published by Digraph, not a hands-on benchmark.
Common questions
How does Digraph compare with AthenaHQ?
AthenaHQ and Digraph both monitor how AI systems mention brands, cite sources, and position competitors. AthenaHQ emphasizes credit-based cross-platform tracking, content optimization, competitor and source insights, hallucination or claim review, and enterprise governance. Digraph emphasizes a defined prompt portfolio, full answer evidence, citations, competitors, and connected analysis workflows across supported model APIs. Their published units and automation boundaries differ, so a controlled prompt-and-response pilot is essential.
How does AthenaHQ pricing compare with Digraph?
AthenaHQ’s lowest published plan is Free (Essential; Starter $295/mo) with 300 credits (1 credit = 1 response) and 11 models on Starter. Digraph starts with a 14-day free trial; Standard is then $99/mo billed monthly for 30 prompts, 1 brand across supported model APIs, and Pro is $249/mo for 300 prompts, 3 brands. Billing basis, collection surfaces, and plan units differ, so confirm current terms with both vendors.
Is Digraph a good AthenaHQ alternative?
Digraph is a fit for teams comparing a credit-based AI search optimization suite with content and enterprise controls against a prompt-centered evidence and investigation workflow. Run the same buyer prompts in both products and compare the full answers, citations, and next actions before deciding.
Can Digraph complement an existing AthenaHQ workflow?
Yes. Many teams use more than one research or measurement system. Digraph adds an evidence-led view of AI answers, prompts, citations, competitors, and follow-up work alongside the tools SEO, brand, and content teams already use.
How should I run a fair AI visibility evaluation?
Use the same prompt portfolio, competitors, markets, AI systems, and reporting period in each tool. Review the underlying answers and sources, not only aggregate scores, before choosing a workflow.