Cost Watchdog

Cost and model watchdog

A recurring read of how evlog spends its model budget and whether the models in use are still the right ones. Run it weekly, on the last full week. Grounded in the AI Gateway report and the current model landscape, never in your memory of prices.

The core question: for every surface, is the model it runs still a sensible buy? The honest answer is often "yes, no change." A quiet week is a real result.

What the report gives you

ai_gateway__report with groupBy: 'tag' returns one row per tag value scoped to the current environment: the evi:env:* row (the total) plus one evi:surface:* row per surface. Each row carries total_cost, market_cost, input_tokens, output_tokens, cached_input_tokens, reasoning_tokens and request_count. groupBy: 'model' returns one row per model.

The surface list is whatever evi:surface:* rows the report actually returns. Do not assume the set; read it from the data.

Resources

Open these directly instead of searching; they are the stable home for everything the model-landscape step needs.

• AI Gateway model catalog, as JSON (pricing and capabilities for every model in one fetch): https://ai-gateway.vercel.sh/v1/models
• AI Gateway models browser (human-readable, filter by provider, pricing, latency, throughput): https://vercel.com/ai-gateway/models
• AI Gateway docs, models & providers: https://vercel.com/docs/ai-gateway/models-and-providers
• Model quality leaderboard (ex-LMArena, blind A/B human preference Elo): https://arena.ai/leaderboard
• Independent cost-efficiency and benchmarks (Intelligence Index, cost per task, tokens per task, time per task, per-benchmark scores): https://artificialanalysis.ai/. Head-to-head pages live at https://artificialanalysis.ai/models/comparisons/<a>-vs-<b>, using AA slugs (glm-5-3-flash, gpt-6-luna-medium).

Use web_search/web_fetch only for what these do not cover, such as a candidate model's fit for a specific surface. Every figure cited still needs a source and a recency, and a benchmark figure comes from Artificial Analysis or the leaderboard directly, never from a blog or aggregator quoting them.

Steps

1. Define the window

Run Monday morning. Cover the last 7 full days ending yesterday, and pull the 7 days before that as the comparison window, so every drift figure is period-over-period.

2. Pull the numbers

• ai_gateway__report for both windows, groupBy: 'tag'. That is the spend and token picture per surface and the total.
• ai_gateway__report for both windows, groupBy: 'model'. The per-model mix (today this is usually one model everywhere).
• For any surface worth a closer look, ai_gateway__report scoped to that surface (tags: ['evi:env:<env>', 'evi:surface:<name>']) with groupBy: 'model' to see what it runs and at what cost.

Use the eval environment tag to keep benchmark and eval traffic out of the production read when the report lets you.

3. Build the candidate set

Derive it from the catalog every run; do not pick alternatives from memory. From /v1/models, keep every model tagged both tool-use and reasoning that takes image input (the base model reads images natively, see docs/vision.md), and whose input and output prices are each within 3x of the current model's. Then add the top three of that set by Artificial Analysis cost per task. List the full candidate set in the report, with the reason each one was dropped.

4. Compare candidates

Score the current model and every surviving candidate on the same four axes, in one table:

• Agentic quality. Terminal-Bench, AutomationBench, tau-bench and the hallucination rate, the benchmarks closest to what Evi's surfaces do. The headline Intelligence Index is context, not the verdict.
• Cost per task. Artificial Analysis cost per task and output tokens per task. Two models at the same per-token price can differ tenfold per task, and a model that spends fewer tokens is cheaper even when it is less capable.
• Speed. Time per task and output tokens per second. This matters most on interactive surfaces (slack, photon), where a person is waiting.
• Projected cost on Evi's real traffic. Reprice last week's token mix from the report (uncached input, cached input, output, reasoning) at the candidate's catalog rates, including cache-write pricing when the candidate has one. The advertised price is a floor: gatewayRouting sends zeroDataRetention, which can drop the cheapest deployments, so the price Evi pays for the current model comes from a call's provider_metadata.gateway, not from the catalog.

Where quality and cost disagree, also compute cost per solved task (cost per task divided by score) on the benchmark closest to the surface.

Never discard a candidate for being less capable alone. A model that is materially cheaper or faster and trails on quality is a tradeoff to report, not a non-starter.

5. Flag drift

Compare the two windows and call out what moved, with a reason where one is visible:

• Total or per-surface cost up or down, as a percent and a dollar figure.
• Model mix change: a model appearing, disappearing, or shifting share.
• Token shape change (input, output, cached, reasoning) that hints at a behavior or prompt drift, not just volume.
• A surface whose cost is out of proportion to its request_count.