Apify vs Holistics
Side-by-side trajectory, velocity, and editorial themes.
Web-scraping platform is reshaping itself around AI agents — MCP, permissions, and OpenAPI surfaces.
Apify continues to optimize for AI-agent consumption. Recent shipments include interactive OpenAPI documentation for standby Actors with auto-attached API tokens, an approval modal for full-permission Actors (least-privileged defaults), multiple datasets per Actor for cleaner output structure, and a redesigned MCP configurator covering Claude Desktop, Claude.ai, Claude Code, Antigravity, Cursor, ChatGPT, Codex, and VS Code. The mcpc universal MCP CLI client and Dynamic Actor memory rounded out the prior month.
Apify is converging on a single thesis: be the scraping and Actor execution infrastructure that AI agents call into. Every recent release either improves how agents discover and run Actors (MCP configurator, OpenAPI Endpoints tab, mcpc CLI) or hardens what happens when they do (full-permission approvals, dataset structure, dynamic memory). The product is no longer marketing itself primarily as scraping — it's marketing itself as agent-callable web automation.
Expect tighter cost-attribution and audit trails for agent-initiated runs, more nuanced permission scopes, and continued expansion of supported MCP-aware client editors. Standby Actors as a deployment model are likely to see more first-class support — they're a natural fit for agent-callable APIs.
Holistics turns the BI dashboard into a conversational AI surface, on customer-owned models.
Holistics is well into a BI-meets-AI productization phase, layering conversational analytics on top of its existing modeling and dashboard core. Recent releases mix consumer-grade dashboard polish (auto-run filters, K/M/B number formatting, percentile calculations) with deeper AI plumbing: bring-your-own Claude and Gemini keys, per-user AI access controls, and now an Ask AI that asks clarifying questions back. The GitHub App integration also signals enterprise-readiness work alongside the AI push.
The product is being repositioned from a self-service BI tool to an AI-mediated analytics workspace where natural-language exploration is the headline interaction. Crucially, the team is pushing AI as an infrastructure layer customers can own — BYO LLM keys, granular access policies — rather than locking customers into a vendor-managed model. The dashboard improvements look incremental, but read as ground prep for AI agents to consume and manipulate dashboards more reliably.
Expect the next quarter to bring agentic dashboard editing — Ask AI not just answering but proposing dashboards and saving them — plus expanded BYO LLM coverage (likely Azure OpenAI or open-weights via OpenRouter) to widen procurement options for enterprise buyers.
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