Manage costs effectively

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Track your costs

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Using the /usage command

Total cost:            $0.55
Total duration (API):  6m 20s
Total duration (wall): 6h 33m 10s
Total code changes:    0 lines added, 0 lines removed
Usage by model:
   claude-sonnet-4-6:  1.2k input, 5.3k output, 940.0k cache read, 50.0k cache write ($0.55)

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Plan usage breakdown

• Attribution: recent usage attributed to skills, subagents, plugins, and individual MCP servers, each shown as a percentage of the total. An MCP server’s share counts only the requests that consumed one of its tool results. Before v2.1.222, after one call to an MCP server, Claude Code attributed every subsequent request to that server, overstating its share.
• Behavior flags: behaviors such as long context or cache misses, flagged when one accounts for 10% or more of recent usage.

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When the usage request fails

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Analyze your usage patterns

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Add usage credits to your subscription

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Manage costs for your organization

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Claude for Teams and Enterprise

• See spend: the spend report in org analytics shows estimated spend per user and per model, with CSV export, updated daily. The report covers usage-credit spend and appears once usage credits are turned on. Usage inside the seat allowance isn’t metered in dollars.
• See adoption: the analytics dashboard shows daily active users, sessions, and contribution metrics, with CSV export of contribution data. See track team usage with analytics.
• Cap spend: the seat allowance is the default ceiling. To let members continue past it, turn on usage credits and set spend limits at the organization, group, or individual member level.
• Pull per-user numbers: on the Enterprise plan, the Enterprise Analytics API returns per-user usage and cost reports across Claude surfaces, including Claude Code. A Primary Owner creates a key with the read:analytics scope at claude.ai/analytics/api-keys. On the Teams plan, export the spend report CSV, which lists token usage and estimated spend per user and per model.

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Claude Console

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Rate limit recommendations

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Cloud providers

• OpenTelemetry: export metrics from each developer’s machine to your own observability stack. This gives you per-user token counts, costs, and tool activity regardless of provider.
• A Claude apps gateway: a self-hosted Claude apps gateway provides per-user usage attribution, OTLP metrics with token counts, and per-user spend limits on these providers.
• An LLM gateway: route all Claude Code traffic through a proxy that tracks spend per key. Several large enterprises reported using LiteLLM, an open-source tool that tracks spend by key. This project is unaffiliated with Anthropic and has not been audited for security.

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When a developer asks about a limit

• “You’ve hit your session limit” or “You’ve hit your weekly limit”: a seat-based usage window on a subscription plan. These windows are shared across all models, so switching models with /model doesn’t restore access, though it does keep the developer working after the model-specific “You’ve hit your Opus limit” message. The message shows when the window resets, and the developer can run /usage-credits to request usage beyond the allowance if you have usage credits turned on. See usage limit errors.
• A spend limit message from a Claude apps gateway: the developer passed a spend cap you set on your self-hosted gateway, and the gateway blocks their requests until the period resets or you raise the cap. See gateway spend limits for caps, reset schedules, and the message the developer sees.
• A context or auto-compact warning: not a usage limit. The conversation has grown close to the session’s auto-compact window, the threshold where Claude Code summarizes older history to free space. Point the developer at reduce token usage.
• Unexpectedly high spend on an API or cloud-provider plan: usually traces back to long sessions that were never cleared or to Opus left as the default model. The highest-impact habits to share are clearing between unrelated tasks and matching the model to the job, both covered in reduce token usage.

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Agent team token costs

• Use Sonnet for teammates. It balances capability and cost for coordination tasks.
• Keep teams small. Each teammate runs its own context window, so token usage is roughly proportional to team size.
• Keep spawn prompts focused. Teammates load CLAUDE.md, MCP servers, and skills automatically, but everything in the spawn prompt adds to their context from the start.
• Shut down teammates when their work is done. Each active teammate continues consuming tokens until it exits or the session ends.
• Agent teams are disabled by default. Set CLAUDE_CODE_EXPERIMENTAL_AGENT_TEAMS=1 in your settings.json or environment to enable them. See enable agent teams.

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Reduce token usage

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Manage context proactively

• Clear between tasks: Use /clear to start fresh when switching to unrelated work. Stale context wastes tokens on every subsequent message. Use /rename before clearing so you can easily find the session later, then /resume to return to it.
• Add custom compaction instructions: /compact Focus on code samples and API usage tells Claude what to preserve during summarization. In a fresh session, /compact prints Not enough messages to compact. because there’s no conversation history to summarize yet.

# Compact instructions

When you are using compact, please focus on test output and code changes

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Choose the right model

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Reduce MCP server overhead

• Prefer CLI tools when available: Tools like gh, aws, gcloud, and sentry-cli are still more context-efficient than MCP servers because they don’t add any per-tool listing. Claude can run CLI commands directly.
• Disable unused servers: Run /mcp to see configured servers and disable any you’re not actively using.

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Install code intelligence plugins for typed languages

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Offload processing to hooks and skills

{
  "hooks": {
    "PreToolUse": [
      {
        "matcher": "Bash",
        "hooks": [
          {
            "type": "command",
            "command": "~/.claude/hooks/filter-test-output.sh"
          }
        ]
      }
    ]
  }
}

#!/bin/bash
input=$(cat)
cmd=$(echo "$input" | jq -r '.tool_input.command')

# If running tests, filter to show only failures
if [[ "$cmd" =~ ^(npm test|pytest|go test) ]];