Cost Tracking

How Costs Are Calculated

Cost is based on:

1. Token usage -- The number of prompt (input) and completion (output) tokens consumed
2. Model pricing -- The per-token price for the specific LLM model used
3. Automatic aggregation -- Span-level costs are rolled up to execution and workspace levels

The formula is:

cost = (prompt_tokens * input_price_per_token) + (completion_tokens * output_price_per_token)

PromptRails keeps model pricing metadata for supported providers and uses it to estimate costs automatically.

Per-Execution Cost Breakdown

Every execution includes total cost and token usage:

execution = client.executions.get(execution_id="your-execution-id")
 
print(f"Total Cost: ${execution['data']['cost']:.6f}")
print(f"Token Usage: {execution['data']['token_usage']}")
print(f"Duration: {execution['data']['duration_ms']}ms")

For multi-step executions such as chains, workflows, and multi-agent runs, the execution cost is the sum of the LLM spans inside that execution.

Per-Span Cost Analysis

Drill down into individual spans to see cost at the LLM call level:

traces = client.traces.list(execution_id="your-execution-id", kind="llm")
 
for span in traces["data"]:
    print(f"Model: {span['model_name']}")
    print(f"  Prompt tokens: {span.get('prompt_tokens', 0)}")
    print(f"  Completion tokens: {span.get('completion_tokens', 0)}")
    print(f"  Cost: ${span.get('cost', 0):.6f}")
    print(f"  Duration: {span['duration_ms']}ms")
    print("---")

This is valuable for identifying which step in a complex pipeline is the most expensive.

Token Usage Tracking

Token usage is tracked with three metrics:

Token counts are provided by the LLM provider and recorded with each LLM span.

Workspace-Wide Cost Summary

Get aggregated cost data across your entire workspace:

summary = client.costs.get_summary()
 
print(f"Total Cost: ${summary.total_cost:.2f}")
print(f"Total Executions: {summary.total_executions}")
print(f"Total Tokens: {summary.total_tokens}")

Per-Agent Cost Analysis

Analyze costs broken down by agent to identify your most expensive workflows:

agent_costs = client.costs.get_agent_summary("your-agent-id")
 
print(f"Agent Total Cost: ${agent_costs.total_cost:.2f}")
print(f"Agent Executions: {agent_costs.total_executions}")
print(f"Agent Tokens: {agent_costs.total_tokens}")

LLM Model Pricing

PromptRails maintains pricing information for all supported LLM models. Prices are stored as cost per token (or per 1K/1M tokens depending on the model) and are used for automatic cost calculation.

Supported providers and their models include:

Cached input tokens (prompt-cache read hits) are billed at the model's discounted cached-input rate when configured, so prices reflect the lower cost providers charge for cached prompt prefixes.