Short answer: Peec AI and OtterlyAI both describe recurring prompt monitoring, but the useful comparison is their supported prompt/model scope, data-collection method, source detail, cadence, and plan limits:not a dashboard score. This is a documentation-based comparison; neither product was tested in a paid account.
What the vendors document
Peec AI’s product materials describe visibility, position, sentiment, share of voice, competitor analysis, and cited-source insights. Its pricing page lists plan-specific prompts, selectable models, projects, and tracking frequency. OtterlyAI’s help center describes search-prompt monitoring, response and citation review, country configuration, and how its collection works; the vendor says most supported engines use public interfaces, with Claude as an API exception.
Those descriptions do not establish equal coverage. The supported model list, mode, location, and data source can vary by plan and change over time. Read the live Peec product/pricing pages and OtterlyAI help/pricing pages before purchase.
Compare the buyer controls
- Prompt set: Can you enter exact prompts, organize them by topic, and preserve an identical test set across both products?
- Platform and mode: Does the plan query the same engine surface, such as ChatGPT Search versus another ChatGPT mode?
- Location and language: Are location settings available and equivalent for the market you need?
- Cadence and limits: Count one prompt × one model × one location × one run according to each provider’s billing rules; compare total checks, not just prompts.
- Metrics: Confirm each vendor’s definition of mention rate, citation, position, sentiment, and share of voice.
- Source evidence: Can you inspect response text and cited URLs at the prompt level, and export them?
- Collection method: Record the vendor’s stated collection method per engine. A UI-based run, API data, and an existing index are different observation systems.
A practical side-by-side trial
If you have access to both products, load the same 20–50 priority prompts, the same brand aliases and competitors, the same market, and the same engines where available. Run both on a matched schedule for several weeks. Export response text, run dates, mentions, citations, and failures. Compare missing-response rates and definition differences before comparing counts. Treat any mismatch as a method finding, not proof that one tool is wrong.
Do not declare a winner from a short trial without access to the underlying responses and the vendors’ measurement rules. Product convenience, UI quality, customer support, and data accuracy remain untested here.