AI engineering

The FDE Arms Race: AI Companies Spend $10B on Forward-Deployed Engineering

AI companies have committed ~$10B in 12 months to forward-deployed engineering, embedding engineers inside enterprises to solve the deployment bottleneck. Three structural models emerge: internal army, PE-backed JV, and Palantir's original approach. The question is whether scaling the FDE model 10x breaks the economics that made it work for Palantir.

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AlloyDB AI Functions: Boost Performance and Lower Costs

AlloyDB's new AI functions bring Gemini's world knowledge directly into SQL, with smart batching delivering up to 2,400x performance improvements and an optimized mode using proxy models that slashes costs by 6,000x. The key insight is that batching must happen at the database layer, not the application layer, to avoid bloated prompts or missed efficiency gains.

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SOCRadar achieves 20x faster threat queries with AlloyDB and Gemini

SOCRadar, a cybersecurity company delivering threat intelligence, hit a hard ceiling with its on-premises, self-managed PostgreSQL database. As both threat data volume and customer demands grew, the database couldn't keep up with the simultaneous pressure of high-velocity data ingestion and heavy real-time analytical queries. This created a critical data bottleneck that slowed down threat insights and forced engineers to spend their time on manual database tuning instead of product innovation.

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Most AI Work Can Wait: Routing Before Models

The article makes a clear, contrarian case that most agent teams optimize the wrong thing first. Picking the model before the routing architecture buries cost and latency tradeoffs inside prompts. The three-layer routing stack — skill classifier, router, model selector — is the concrete infrastructure most teams are missing, and queueing makes async inference viable for most agent workloads.

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