Apify vs BigQuery
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.
BigQuery doubles down on Iceberg, graph, and global data sharing as the lakehouse fight intensifies.
BigQuery's May 2026 ship list is dominated by three tracks: open-format lakehouse integration (Iceberg v3 with deletion vectors, REST catalog support in Conversational Analytics), graph capabilities maturing inside BigQuery Studio, and global data exchange via multi-region sharing listings reaching GA. Alongside the feature work, Google is tightening Data Transfer Service security (MFA on Google Ads transfers) and warning about Ads retention changes that will cap historical backfills from June 1. The release notes show a mature warehouse continuing to absorb adjacent workloads rather than reinventing itself.
BigQuery is positioning itself as the federated query and sharing fabric for a multi-format world, with Iceberg getting closer to first-class status and Conversational Analytics extending across external catalogs. The graph and notebook work signals a push to keep more analytical work inside Studio instead of bouncing to specialized tools. Expect continued layering of governance, AI-assisted query, and open-table support on top of the existing engine rather than core engine reinvention.
Next obvious step is GA for Iceberg v3 features and full conversational graph querying without Preview gating. Watch for additional first-party data sources getting MFA mandates, mirroring the Google Ads tightening.
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