The agent successfully gathered enough information to explain SambaNova's core business, target market, products, and differentiation, though pricing information required working around an incomplete website structure. The main site lacks a public pricing page (404 on /pricing), forcing the agent to rely on external sources and a separate cloud subdomain to answer the pricing question completely. Despite this fragmentation, the agent assembled a coherent, well-sourced explanation of what SambaNova does and who it's for.

• ›Step [1] (homepage) and step [2] (/products) provided basic positioning ('The Fastest AI Inference Platform', SambaStack product overview) but lacked pricing and detailed use-case information. The homepage is the primary discovery surface but doesn't fully answer the core task.
• ›Step [3] revealed a critical gap: /pricing returns 404, indicating no public pricing page on the main domain. This forced the agent to use web search to find pricing information.
• ›Step [7] (web search) was essential routing that surfaced cloud.sambanova.ai/plans/pricing—a separate subdomain with the actual pricing data. This fragmentation means pricing discovery is not self-contained on the main brand site.
• ›Step [8] (cloud.sambanova.ai pricing page) contained token-based pricing details ($0.26–$3.15 per 1M tokens by model), directly cited in the final answer. This is the only fetchable source for quantitative pricing.
• ›Step [9] (blog post on cost-effective AI) provided supporting narrative about pricing philosophy but was lower-content for the specific pricing figures.
• ›The agent had to cite external sources (eesel AI, Artificial Analysis) because SambaNova's own site does not publish comparative pricing or clear differentiators in a machine-readable form.
• ›Product positioning (SambaStack, SambaCloud, SambaManaged) was inferred from scattered navigation and product page fragments, not from a structured product overview or comparison table on the main site.