Snowflake

Snowflake is a cloud data warehouse platform. OpenRouter can stream traces directly to your Snowflake database for custom analytics, long-term storage, and business intelligence.

Step 1: Create the traces table

Before connecting OpenRouter, create the OPENROUTER_TRACES table in your Snowflake database. You can find the exact SQL in the OpenRouter dashboard when configuring the destination:

Step 2: Create access credentials

Generate a Programmatic Access Token with ACCOUNTADMIN permissions in the Snowflake UI under Settings > Authentication.

Step 3: Enable Broadcast in OpenRouter

Go to Settings > Broadcast and toggle Enable Broadcast.

Step 4: Configure Snowflake

Click the edit icon next to Snowflake and enter:

• Account: Your Snowflake account identifier (e.g., eac52885.us-east-1). You can find your account region and your account number at the end of your Snowflake instance’s URL: https://app.snowflake.com/us-east-1/eac52885; together these make your account identifier.
• Token: Your Programmatic Access Token.
• Database: Target database name (default: SNOWFLAKE_LEARNING_DB).
• Schema: Target schema name (default: PUBLIC).
• Table: Table name (default: OPENROUTER_TRACES).
• Warehouse: Compute warehouse name (default: COMPUTE_WH).

Step 5: Test and save

Click Test Connection to verify the setup. The configuration only saves if the test passes.

Step 6: Send a test trace

Make an API request through OpenRouter and query your Snowflake table to verify the trace was received.

Example queries

Cost analysis by model

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SELECT
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  DATE_TRUNC('day', TIMESTAMP) as day,
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  MODEL,
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  SUM(TOTAL_COST) as total_cost,
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  SUM(TOTAL_TOKENS) as total_tokens,
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  COUNT(*) as request_count
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FROM OPENROUTER_TRACES
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WHERE TIMESTAMP >= DATEADD(day, -30, CURRENT_TIMESTAMP())
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  AND STATUS = 'ok'
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  AND SPAN_TYPE = 'GENERATION'
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GROUP BY day, MODEL
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ORDER BY day DESC, total_cost DESC;

User activity analysis

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SELECT
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  USER_ID,
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  COUNT(DISTINCT TRACE_ID) as trace_count,
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  COUNT(DISTINCT SESSION_ID) as session_count,
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  SUM(TOTAL_TOKENS) as total_tokens,
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  SUM(TOTAL_COST) as total_cost,
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  AVG(DURATION_MS) as avg_duration_ms
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FROM OPENROUTER_TRACES
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WHERE TIMESTAMP >= DATEADD(day, -7, CURRENT_TIMESTAMP())
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  AND SPAN_TYPE = 'GENERATION'
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GROUP BY USER_ID
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ORDER BY total_cost DESC;

Error analysis

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SELECT
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  TRACE_ID,
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  TIMESTAMP,
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  MODEL,
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  LEVEL,
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  FINISH_REASON,
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  METADATA as user_metadata,
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  INPUT,
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  OUTPUT
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FROM OPENROUTER_TRACES
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WHERE STATUS = 'error'
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  AND TIMESTAMP >= DATEADD(hour, -1, CURRENT_TIMESTAMP())
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ORDER BY TIMESTAMP DESC;

Provider performance comparison

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SELECT
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  PROVIDER_NAME,
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  MODEL,
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  AVG(DURATION_MS) as avg_duration_ms,
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  PERCENTILE_CONT(0.5) WITHIN GROUP (ORDER BY DURATION_MS) as p50_duration_ms,
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  PERCENTILE_CONT(0.95) WITHIN GROUP (ORDER BY DURATION_MS) as p95_duration_ms,
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  COUNT(*) as request_count
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FROM OPENROUTER_TRACES
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WHERE TIMESTAMP >= DATEADD(day, -7, CURRENT_TIMESTAMP())
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  AND STATUS = 'ok'
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  AND SPAN_TYPE = 'GENERATION'
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GROUP BY PROVIDER_NAME, MODEL
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HAVING request_count >= 10
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ORDER BY avg_duration_ms;

Usage by API key

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SELECT
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  API_KEY_NAME,
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  COUNT(DISTINCT TRACE_ID) as trace_count,
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  SUM(TOTAL_COST) as total_cost,
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  SUM(PROMPT_TOKENS) as prompt_tokens,
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  SUM(COMPLETION_TOKENS) as completion_tokens
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FROM OPENROUTER_TRACES
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WHERE TIMESTAMP >= DATEADD(day, -30, CURRENT_TIMESTAMP())
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  AND SPAN_TYPE = 'GENERATION'
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GROUP BY API_KEY_NAME
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ORDER BY total_cost DESC;

Accessing VARIANT columns

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SELECT
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  TRACE_ID,
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  METADATA:custom_field::STRING as custom_value,
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  ATTRIBUTES:"gen_ai.request.model"::STRING as requested_model
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FROM OPENROUTER_TRACES
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WHERE METADATA:custom_field IS NOT NULL;

Parsing input messages

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SELECT
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  TRACE_ID,
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  INPUT:messages[0]:role::STRING as first_message_role,
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  INPUT:messages[0]:content::STRING as first_message_content
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FROM OPENROUTER_TRACES
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WHERE SPAN_TYPE = 'GENERATION';

Schema design

Typed columns

The schema extracts commonly-queried fields as typed columns for efficient filtering and aggregation:

• Identifiers: TRACE_ID, USER_ID, SESSION_ID, etc.
• Timestamps: For time-series analysis
• Model Info: For cost and performance analysis
• Metrics: Tokens and costs for billing

VARIANT columns

Less commonly-accessed and variable-structure data is stored in VARIANT columns:

• ATTRIBUTES: Full OTEL attribute set
• INPUT/OUTPUT: Variable message structures
• METADATA: User-defined key-values
• MODEL_PARAMETERS: Model-specific configurations

This design balances query performance with schema flexibility and storage efficiency.