Foglamp vs Visiby: Features, Pricing & Which Is Better (2026)

A side-by-side comparison of Foglamp and Visiby — features, pricing, and ideal use cases — to help you decide which AI tool fits your workflow.

Foglamp

Freemium

Observability for AI agents: see the cost, latency, traces, and output quality of every LLM call with one SDK.

Two-Line SDK Instrumentation: Wrap your model once and every generateText / streamText call is automatically instrumented.
Per-Agent Spans and Spend: View per-agent spans, latency, and spend with the full call flow across orchestrator, researcher, writer, and critic.
Evals: Score production traffic with code checks and LLM judges, including PII checks and pass-rate scoring.
Distributed Traces: Waterfall every run with the exact prompt and response captured per span.
Alerts: Set threshold rules on cost, latency, and error rate to catch problems early.
Cost Intelligence: Know exactly what every call costs broken down by model, agent, and customer.

Catching Cost Regressions: Detect a sudden 10x cost spike days after shipping before it drains the budget.
Debugging Bad Output: Trace the exact prompt and response that produced a wrong or hallucinated answer.
Quality Gating with Evals: Continuously score production traffic to verify agents stay accurate and PII-safe.
Latency Monitoring: Alert when per-agent latency crosses a threshold so slow responses are caught fast.
Per-Customer Spend Analysis: Break down LLM spend by customer and model to understand unit economics.

FNA Technology

Paid

AI visibility platform that tracks how ChatGPT, Perplexity, Claude, Gemini and AI Overviews cite your brand, and ships fixes.

AI Citation Tracking: Continuously samples roughly 50,000 prompts per week across ChatGPT, Claude, Perplexity, Gemini and Google AI Overviews to record where and how a brand is cited.
Per-Engine Visibility Scoring: Reports a composite AI Visibility score plus share of voice and prompts won or lost, broken out engine by engine so declines can be traced to a specific model.
Prompts & Citations Explorer: Lets teams open any tracked prompt and read the actual model answer to see which competitor was named and why.
Brand Entity Analysis: Maps the adjectives each engine associates with your brand versus competitors and suggests reframing plays to change that portrait.
Competitor Intelligence: Tracks rival citation share on comparison and 'alternatives to' prompts, highlighting categories where a competitor dominates.
Prioritized Action Plan: Converts findings into P0/P1 recommendations such as schema additions or comparison pages, each with a time estimate and projected score gain.
Site Audit for AI Parseability: Audits pages for missing entity definitions, structured Q&A data and other signals that prevent models from citing the site correctly.
White-Label Reporting and API: Higher tiers add white-label client reports, SSO/SAML and API access for agencies managing multiple brands.

AI Search Monitoring: Marketing teams track whether ChatGPT and Perplexity recommend their product or a competitor on high-intent category prompts.
Competitive Benchmarking: Brands quantify how much citation share a named rival is capturing on 'alternatives to' and 'best of' queries.
Content Prioritization: Content teams decide which pages to write or refresh based on which prompts are currently missed rather than on keyword volume alone.
Technical AEO Audits: SEO specialists find pages lacking FAQ schema or entity markers that keep answer engines from parsing them.
Agency Client Reporting: Agencies run pooled prompt tracking across multiple client workspaces and deliver white-label AI visibility reports.
Executive Reporting: Operators present a weekly digest showing search clicks alongside AI citation share to explain traffic shifts leadership sees.