AIHOT 于 2026-08-17 收录了“OpenRouter 推出 Activity 仪表盘与 Analytics API：按智能体、模型、请求追踪 AI 使用成本”这一公开动态。以下先呈现从来源页面抓取的正文，再给出 AIHOT 摘要与 TopoReduce 编辑解读。
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Understand your AI usage: every agent, model, and request — OpenRouter Blog
Understand your AI usage: every agent, model, and request
Cailee Moberg ·8/17/2026
Start with the big pictureStart with the big pictureExplore any questionClick a chart, land in the logsSame data as Explore, via APIFor agentsGet started
Every company that spent the last two years deploying agents is now asking the same question: what are they costing us, and which ones are worth it? OpenRouter’s Activity dashboard and beta Analytics API answer that question per agent, per model, per request.
Open Activity to see how spend breaks down across agents, apps, and team members. Find out which models and tasks drive costs, and where caching cuts your bill. Create and save custom views, then drill from any chart down to the individual requests.
All of the data is available through the beta Analytics API. Point your agent at the openrouter-analytics skill to dig into your usage, get a quick answer in your terminal, or pull the numbers into your own dashboards.
Start with the big picture
Overview is your usage at a glance. Five metrics at the top: total spend, requests, token volume, cache hit rate, and blended cost per million tokens, each with a sparkline and a comparison against the previous period. On the same screen, your top users and apps, spend by model, OpenRouter credits versus BYOK spend, request volume by model, prompt and completion token breakdown, and prompt caching.
Trends takes the same data and sorts by movement instead of size, with a panel showing what’s rising and falling. See your trends across models, users, API keys, and apps. Use it to spot a runaway agent, a new model gaining traction, or a tool spreading across your org.
Explore any question
Every card on Overview and Trends links into Explore, where you assemble the view yourself:
- Metric: spend, request count, tokens (prompt, completion, reasoning, or cached), cache hit rate, blended cost per million tokens, BYOK versus credit spend, or latency and throughput down to P50/P90/P99
- Group by (up to two dimensions): model, variant, provider, API key, app, user, workspace, origin, country, data region, finish reason, context length, session, generation, custom user IDs, or any classifier dimension you’ve defined
- Rollup: minute, hour, day, week, or month, or drop the time axis for a ranked table
- Chart type: bar, line, or dot plot
Use it to see which apps are driving usage in each workspace, which models your agents are using for each task type, or how your provider latency is trending over time.
Save your go-to views. Open the options menu, choose Save current chart, and name it. In an organization you also choose who sees it: Only me, or Everyone in my organization.
Download CSV or Download PDF to send any chart’s data straight into a spreadsheet or report.
Guardrails shows what your prompt injection and sensitive-information rules blocked, redacted, or flagged, and which rules are doing the work. Use it to monitor the rate of sensitive data entering your prompts, and which rules and data types are catching it. Filter by workspace or classifier to narrow in on where it’s coming from.
Expand a card to get the full breakdown, including which combinations of detected patterns drove each block, redaction, and flag.
Click a chart, land in the logs
Aggregates show you that something got expensive. The next question is which requests.
Every chart and ranked table in Activity links through to the logs behind it. Click Tuesday’s bar, one model’s slice of a stacked chart, or a row in a ranked table, and you land in your logs filtered to those requests.
Open any row in logs for the Generation detail view. It shows:
- Cost: upstream inference, caching, web search, and file processing, plus the discounts and cache savings applied
- Performance: provider latency, throughput, and time to first token
- Routing: which provider served the request, whether it fell back to another one, and the finish reason
- Attribution: the app, API key, and workspace behind it, plus session and request IDs and data region
- Context: any guardrail events, classifier tags, and the raw metadata
The Prompt detail view renders the full messages array and a flamegraph of estimated tokens per message colored by role: system, user, assistant, and tool. A conversation that costs three times what you expected usually shows it here as a wide band of tool calls or a heavy system prompt. The cached prefix is shaded, so you can see how far into the prompt the cache held and which message broke it.
Per-message token counts are estimates derived from message size; the totals on the generation itself are recorded usage. Prompts and completions detail only exist if private input/output logging was enabled when the request ran, which you can enable in your workspace observability.
Same data as Explore, via API
Everything in Explore is also available through the beta Analytics API: let your agent run the analysis, get a quick answer in your terminal, or pull the numbers into your own dashboards. Analytics queries require a management key. Two endpoints:
- GET /api/v1/analytics/meta returns the currently supported metrics, dimensions, filter operators, and granularities. Call the metadata endpoint first to see what’s available; we’re always adding new metrics and dimensions.
- POST /api/v1/analytics/query runs the query and returns the same aggregates the Explore charts are drawn from.
curl -X POST https://openrouter.ai/api/v1/analytics/query \
-H "Authorization: Bearer $OPENROUTER_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"metrics": ["total_usage", "tokens_total", "cache_hit_rate"],
"dimensions": ["model"],
"granularity": "day",
"time_range": {"start": "2026-07-01T00:00:00Z", "end": "2026-08-01T00:00:00Z"},
"limit": 20
}'
For agents
The cost control cookbook puts your agent in charge of spend analysis. Give your coding agent a management key and the openrouter-analytics skill and have it run a cost review on your OpenRouter account. It finds the models costing a multiple of your blended rate per million tokens, traces them back to the keys and pipelines responsible, and returns ranked recommendations.
We ran this internally and found a preview model burning ~$6.2K/month at roughly 25x the org’s blended rate. One drill-down query later, 98% of it traced to a single batch-pipeline key running a task that never needed a frontier model. The fix was a one-line model swap. See the query recipes and agent prompts we used in the cookbook.
Get started
Open Activity, or start in Explore if you already know the question you want answered. To work from your terminal instead, grab a management key and hit the beta Analytics API.
Tell us what you’d like to see next in #feedback on Discord.
Cailee Moberg ·8/17/2026
Start with the big pictureStart with the big pictureExplore any questionClick a chart, land in the logsSame data as Explore, via APIFor agentsGet started
Every company that spent the last two years deploying agents is now asking the same question: what are they costing us, and which ones are worth it? OpenRouter’s Activity dashboard and beta Analytics API answer that question per agent, per model, per request.
Open Activity to see how spend breaks down across agents, apps, and team members. Find out which models and tasks drive costs, and where caching cuts your bill. Create and save custom views, then drill from any chart down to the individual requests.
All of the data is available through the beta Analytics API. Point your agent at the openrouter-analytics skill to dig into your usage, get a quick answer in your terminal, or pull the numbers into your own dashboards.
Start with the big picture
Overview is your usage at a glance. Five metrics at the top: total spend, requests, token volume, cache hit rate, and blended cost per million tokens, each with a sparkline and a comparison against the previous period. On the same screen, your top users and apps, spend by model, OpenRouter credits versus BYOK spend, request volume by model, prompt and completion token breakdown, and prompt caching.
Trends takes the same data and sorts by movement instead of size, with a panel showing what’s rising and falling. See your trends across models, users, API keys, and apps. Use it to spot a runaway agent, a new model gaining traction, or a tool spreading across your org.
Explore any question
Every card on Overview and Trends links into Explore, where you assemble the view yourself:
- Metric: spend, request count, tokens (prompt, completion, reasoning, or cached), cache hit rate, blended cost per million tokens, BYOK versus credit spend, or latency and throughput down to P50/P90/P99
- Group by (up to two dimensions): model, variant, provider, API key, app, user, workspace, origin, country, data region, finish reason, context length, session, generation, custom user IDs, or any classifier dimension you’ve defined
- Rollup: minute, hour, day, week, or month, or drop the time axis for a ranked table
- Chart type: bar, line, or dot plot
Use it to see which apps are driving usage in each workspace, which models your agents are using for each task type, or how your provider latency is trending over time.
Save your go-to views. Open the options menu, choose Save current chart, and name it. In an organization you also choose who sees it: Only me, or Everyone in my organization.
Download CSV or Download PDF to send any chart’s data straight into a spreadsheet or report.
Guardrails shows what your prompt injection and sensitive-information rules blocked, redacted, or flagged, and which rules are doing the work. Use it to monitor the rate of sensitive data entering your prompts, and which rules and data types are catching it. Filter by workspace or classifier to narrow in on where it’s coming from.
Expand a card to get the full breakdown, including which combinations of detected patterns drove each block, redaction, and flag.
Click a chart, land in the logs
Aggregates show you that something got expensive. The next question is which requests.
Every chart and ranked table in Activity links through to the logs behind it. Click Tuesday’s bar, one model’s slice of a stacked chart, or a row in a ranked table, and you land in your logs filtered to those requests.
Open any row in logs for the Generation detail view. It shows:
- Cost: upstream inference, caching, web search, and file processing, plus the discounts and cache savings applied
- Performance: provider latency, throughput, and time to first token
- Routing: which provider served the request, whether it fell back to another one, and the finish reason
- Attribution: the app, API key, and workspace behind it, plus session and request IDs and data region
- Context: any guardrail events, classifier tags, and the raw metadata
The Prompt detail view renders the full messages array and a flamegraph of estimated tokens per message colored by role: system, user, assistant, and tool. A conversation that costs three times what you expected usually shows it here as a wide band of tool calls or a heavy system prompt. The cached prefix is shaded, so you can see how far into the prompt the cache held and which message broke it.
Per-message token counts are estimates derived from message size; the totals on the generation itself are recorded usage. Prompts and completions detail only exist if private input/output logging was enabled when the request ran, which you can enable in your workspace observability.
Same data as Explore, via API
Everything in Explore is also available through the beta Analytics API: let your agent run the analysis, get a quick answer in your terminal, or pull the numbers into your own dashboards. Analytics queries require a management key. Two endpoints:
- GET /api/v1/analytics/meta returns the currently supported metrics, dimensions, filter operators, and granularities. Call the metadata endpoint first to see what’s available; we’re always adding new metrics and dimensions.
- POST /api/v1/analytics/query runs the query and returns the same aggregates the Explore charts are drawn from.
curl -X POST https://openrouter.ai/api/v1/analytics/query \
-H "Authorization: Bearer $OPENROUTER_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"metrics": ["total_usage", "tokens_total", "cache_hit_rate"],
"dimensions": ["model"],
"granularity": "day",
"time_range": {"start": "2026-07-01T00:00:00Z", "end": "2026-08-01T00:00:00Z"},
"limit": 20
}'
For agents
The cost control cookbook puts your agent in charge of spend analysis. Give your coding agent a management key and the openrouter-analytics skill and have it run a cost review on your OpenRouter account. It finds the models costing a multiple of your blended rate per million tokens, traces them back to the keys and pipelines responsible, and returns ranked recommendations.
We ran this internally and found a preview model burning ~$6.2K/month at roughly 25x the org’s blended rate. One drill-down query later, 98% of it traced to a single batch-pipeline key running a task that never needed a frontier model. The fix was a one-line model swap. See the query recipes and agent prompts we used in the cookbook.
Get started
Open Activity, or start in Explore if you already know the question you want answered. To work from your terminal instead, grab a management key and hit the beta Analytics API.
Tell us what you’d like to see next in #feedback on Discord.
AIHOT 摘要
OpenRouter 发布 Activity 仪表盘和 beta Analytics API，可按智能体、模型、请求维度查看支出、token 量、缓存命中率等指标，并支持下钻至单条请求日志。
为什么值得关注
这项功能的价值在于聚合看板能下钻到单次请求，把成本异常直接定位到具体 API key 和任务，内部案例用一行模型替换就修正了每月约 6.2K 美元的误配支出。
工程化解读
从 TopoReduce 的工程视角看，这条信息属于“智能体与自动化”主题。它的价值不只在于一个新产品或新观点本身，还在于说明 AI 系统正在如何影响模型接入、智能体协作、研发流程、基础设施和团队决策。实际采用前，应结合原文确认版本、适用范围、价格和运行条件。
- 发布时间：2026-08-17；AIHOT 分类：智能体与自动化。
- AIHOT 标签：
- AIHOT 判断：这项功能的价值在于聚合看板能下钻到单次请求，把成本异常直接定位到具体 API key 和任务，内部案例用一行模型替换就修正了每月约 6.2K 美元的误配支出。
- AIHOT 评分：63；评分用于站内排序，不等同于独立评测结论。
TopoReduce 编辑观察
当 AI 动态进入真实生产环境，团队需要同时关注能力边界、数据来源、调用成本、权限控制和可回滚性。把单条新闻放回完整工程链路中阅读，比只看标题更有助于判断它是否适合自己的产品和工作流。