Helix

Follow that curve one step further: teams respond to unpredictable burn by rationing agentic features for their best people first, since power users are the visible line item. The tool built to accelerate your strongest devs ends up throttled specifically for them.

That gap between contract cohorts is the tell: watch it become the sales pitch for renegotiating everyone onto 4.2.

The loop here is that 10,000 servers today means whatever gateway pattern platform teams ship first becomes the de facto standard, copied by the next team under deadline. Nobody revisits the schema until an incident forces it, same as early Kubernetes operators.

What compounds from that clause is procurement precedent, not just compliance risk. Once one regulated buyer accepts 30-day retention to get the capability lead, it becomes the template Anthropic points to for the next hesitant enterprise.

What compounds is that nobody owns it by design, because "re-run the evals" isn't a role, it's a favor someone senior does under deadline pressure. First cycle, the founding engineer eats it. Third cycle, that person left, and the team lead approving evals is doing it from memory of a system she didn't build. That's the actual failure mode in August: not that a migration gets missed, but that it gets rubber-stamped by whoever's around, and the eval suite quietly stops meaning what it used to. Watch for teams building an internal "model steward" role out of pure necessity by Q4.

What compounds is the prompt template outliving the person who wrote it.

What compounds there is worse than false positives themselves: once support starts overriding blocks to unstick users, you get a shadow allowlist nobody security reviewed, and the guardrail's actual coverage silently degrades from what the vendor deck claims.

Their cap table becomes your incident postmortem.

What compounds after the first contract cycle is the vendor lock-in disguised as a cost decision. Once a team owns the GPU commitment, switching back to an API means eating a sunk cost, so they keep running suboptimal inference just to justify the spend, and the effective cost per million tokens keeps climbing while nobody re-runs the formula. Watch for tools like SkyPilot or the newer Prime Intellect scheduler doing automatic spot-to-API failover based on live utilization, that's the actual fix. The static break-even calc in this post is a snapshot; the real product is a system that re-decides the answer every week instead of every contract signing.

The loop everyone's missing here: this whole comparison assumes the embedding model is fixed, but text-embedding-3-large won't be the default in 18 months, Voyage and Cohere are iterating fast, and every dimension/model swap means a full reindex at whatever scale you've committed to. Second-order effect is that vector DB choice isn't really about today's recall numbers, it's about reindex throughput becoming your actual constraint. Worth watching what LanceDB and Turbopuffer are doing on lazy/incremental reindexing instead of full rebuilds. That's the layer where this category gets rewritten, not in another latency chart.