Looking for a free alternative to Profound or Peec? Start with the job you need done. Tracking brand mentions, auditing a website, and running an AEO operation require different systems.
Canonry is my pick for teams that want their agent to run the operation: measure AI visibility, investigate weak results, prepare changes, and verify what happened. Elmo is a focused option for teams primarily replacing a visibility dashboard. The other tools below cover specific parts of the work.
I build Canonry. This is a documentation-based shortlist, checked on September 11, 2026, rather than a performance benchmark. It includes open-source projects from GitHub's AEO topic, plus Microsoft's free reporting tools.
The free alternatives at a glance
Here, free means no software license fee or an ongoing free service. For software you run yourself, model calls, hosting, and maintenance can still cost money. Free trials are excluded.
| Tool | Best fit | Free offering | What you still provide |
|---|---|---|---|
| Canonry | Agent-first agency OS | Full platform; MIT from 5.0.0 | Hosting, provider usage, connected accounts |
| Elmo | AI visibility monitoring and reporting | MIT platform you host | Docker/PostgreSQL, scraping or model services |
| GeoLook | Local GEO implementation workflow | MIT application | Local runtime, model access, publishing credentials |
| GEO Optimizer | Technical audits and CI checks | MIT CLI, Python library, MCP server | Python; API usage for live citation checks |
| Bing Webmaster Tools | First-party Microsoft AI citation data | Hosted reporting service | Verified website and available citation data |
1. Canonry: best for an agent-operated agency OS
Canonry connects the work an agency normally spreads across monitoring, analytics, technical SEO, content, and reporting. Your agent operates it through CLI, REST API, or MCP. The dashboard gives the operator another view of the same system.
The useful difference is the connected project record. Answers, citations, integrations, schedules, and project memory persist between agent sessions. An investigation can continue tomorrow without rebuilding its evidence from exports.
The technical capabilities span five parts of that operation:
- Answer evidence: track ChatGPT, Claude, Gemini, and Perplexity through configured providers; retain responses and distinguish brand mentions from citations. Inspect provider, model, query, location, and sample counts. Grounding searches are available when the provider supplies them.
- Business context: join Google Search Console, GA4, Bing Webmaster Tools, Google Business Profile, backlinks, and server traffic. Demand, indexing, crawler requests, referrals, and conversions answer different questions; Canonry keeps those signals distinct.
- Site diagnosis: crawl pages and internal links, inspect page-level findings, trace reachability, and compare crawl snapshots. Dead-link checks distinguish confirmed HTTP failures from requests the crawler could not verify.
- Execution: use the content engine, WordPress drafts, structured data, and indexing submissions alongside your agent's coding tools. ChatGPT Ads operations include paused campaign editing and human-approved activation; ad spend remains separate.
- Portfolio measurement: Advanced Measurement models Properties and Targets across markets, with versioned plans and explicit URL attribution. A strong market should not hide a weak one inside a domain average.
For an existing project named my-site, an agent can read stored evidence directly:
canonry visibility-stats my-site --last-runs 10 --by-provider --format json
canonry technical-aeo score my-site --format json
canonry report my-site --period 30 --format json
Those commands read results; they do not launch a new measurement. The workflow around them might be: find a market losing citations, inspect the competing sources, check demand and indexing, prepare a WordPress draft or code change for review, then schedule another measurement after publication.
Cost and deployment: the full platform is MIT from version 5.0.0, with all previous functionality retained. You operate one instance per operator or team and pay for the infrastructure and external services you use. This is a good fit when someone can own that setup.
2. Elmo: best for a focused visibility platform
Elmo schedules prompts, stores raw answers in PostgreSQL, and produces brand-mention trends, citation analysis, competitor share of voice, query fan-out, opportunities, and shareable reports. Its REST API exposes brands, prompts, competitors, and analytics.
Its documented deployment separates the web application from the worker that executes measurements. Providers include both consumer-surface scraping services and direct model APIs, so the sampling method depends on configuration. See Elmo's provider guide.
Choose it when the main deliverable is recurring visibility measurement and client reporting. For the broader investigation, evaluate how you will connect first-party analytics, site diagnosis, and publishing to that evidence.
Cost boundary: the MIT software is free to host yourself. Infrastructure and measurement providers remain your responsibility; managed Elmo Cloud is a paid offering.
3. GeoLook: best for a local GEO implementation workflow
GeoLook combines answer sampling, site diagnosis, implementation tickets, a content workbench, publishing, and scheduled verification. It supports WordPress drafts, GitHub, and other publishing channels with per-article confirmation.
The architecture is deliberately local: Python scripts, a dashboard bound to localhost, and project data stored as files. Its documentation describes both automated API sampling and manual collection. Check which method applies to the engines you care about.
Choose it when you want a local workspace that moves from diagnosis into content and implementation tasks. It overlaps with Canonry's execution work. The deployment and integration model is the meaningful comparison: local files and scripts versus Canonry's persistent project API and connected business data.
Cost boundary: MIT software; model usage and publishing services depend on your configuration.
4. GEO Optimizer: best for technical audits in development
GEO Optimizer is a Python toolkit with CLI and MCP interfaces. It checks bot access, structured data, and content signals; it also documents saved audit history, regression checks, citation queries, and recurring reports.
For a deployment pipeline, its useful mechanism is a repeatable check:
geo audit --url https://example.com --save-history --regression
geo drift --url https://example.com --fail-on warning
Choose it when you want site checks in development or CI. Preserve its history between CI jobs if you want comparisons across deployments. Treat the audit score as a diagnostic heuristic: a higher score alone does not prove more recommendations or citations.
Cost boundary: the MIT toolkit is free. Live citation queries use your provider credentials. The separately hosted GeoReady product has its own plans.
5. Bing Webmaster Tools: best for first-party citation reporting
Bing Webmaster Tools is free. Its AI Performance reporting exposes citation activity across Microsoft Copilot, AI summaries in Bing, and selected partner integrations, including cited pages and sampled grounding queries. Microsoft documents that coverage here.
The June 2026 preview expansion adds intents, topics, citation share, and period comparisons. Citation share uses your site's citations divided by all citations for the same grounding query. It does not reveal competitor domains or measure traffic share. See Microsoft's metric definitions.
Choose it when you need evidence from Microsoft's supported surfaces without running your own prompt panel. It complements Canonry or Elmo; it cannot replace a controlled, cross-engine prompt basket.
Cost boundary: no tool subscription, but you need a verified site. Coverage depends on the reporting surface and available data.
When Profound or Peec is still worth paying for
The Profound pricing page lists paid plans covering monitoring and Agents credits. Peec's pricing page lists paid tracking plans, agent actions, and a free trial. Neither should be reduced to a static dashboard, and neither page lists an ongoing free platform plan at the time of this review.
A managed subscription can make sense when your team wants the vendor to operate collection and infrastructure. Before switching, compare the exact prompts, engines, geography, sampling method, retention, and exports you need. An API response and a consumer browser answer are different observations.
For a technical team, start with the work after measurement. If that includes investigating first-party data, changing content, managing multiple markets, and reporting the next result, Canonry's agency OS is the option to evaluate first.