Application observability

Track application requests in code and inspect them in the console. For console dashboards and investigation, see Observability.

Token Usage Tracking

The native Edgee SDK exposes response usage for tracking tokens and costs. The examples below assume an initialized edgee client; see your language’s SDK guide for setup.

const response = await edgee.send({
  model: 'gpt-5.2',
  input: 'Your prompt here',
});

console.log(response.usage.prompt_tokens); // Compressed input tokens
console.log(response.usage.completion_tokens); // Output tokens
console.log(response.usage.total_tokens); // Total for billing

// Compression savings (when applied)
if (response.compression) {
  console.log(response.compression.saved_tokens); // Tokens saved by compression
  console.log(response.compression.cost_savings); // Cost savings in micro-units (e.g. 27000 = $0.027)
  console.log(response.compression.reduction); // Percentage reduction (e.g. 18 = 18%)
  console.log(response.compression.time_ms); // Time taken for compression in milliseconds
}

Track usage by:

Model (GPT-5.2 vs Claude vs Gemini)
Project or application
Environment (production vs staging)
User or tenant (for multi-tenant apps)
Time period (daily, weekly, monthly)

Use token usage data with provider pricing to calculate costs. The Edgee dashboard automatically calculates costs based on real-time provider pricing.

Request Tags for Analytics

Tags allow you to categorize and label requests for filtering and grouping in your analytics dashboard. Add tags to track requests by environment, feature, user, team, or any custom dimension. Using tags in native SDKs:

TypeScript
Python
Go
Rust

import Edgee from 'edgee';

const edgee = new Edgee("your-api-key");

const response = await edgee.send({
  model: 'gpt-5.2',
  input: {
    messages: [{ role: 'user', content: 'Hello!' }],
    tags: ['production', 'chat-feature', 'user-123', 'team-backend']
  }
});

from edgee import Edgee, InputObject, Message

edgee = Edgee("your-api-key")

response = edgee.send(
    model="gpt-5.2",
    input=InputObject(
        messages=[Message(role="user", content="Hello!")],
        tags=["production", "chat-feature", "user-123", "team-backend"]
    )
)

import "github.com/edgee-ai/go-sdk/edgee"

client, _ := edgee.NewClient("your-api-key")

response, err := client.Send("gpt-5.2", edgee.InputObject{
    Messages: []edgee.Message{
        {Role: "user", Content: "Hello!"},
    },
    Tags: []string{"production", "chat-feature", "user-123", "team-backend"},
})

use edgee::{Edgee, InputObject, Message};

let client = Edgee::from_env()?;

let input = InputObject::new(vec![Message::user("Hello!")])
    .with_tags(vec![
        "production".to_string(),
        "chat-feature".to_string(),
        "user-123".to_string(),
        "team-backend".to_string(),
    ]);

let response = client.send("gpt-5.2", input).await?;

Using tags with OpenAI/Anthropic SDKs via headers: If you’re using the OpenAI or Anthropic SDKs with Edgee, add tags via the x-edgee-tags header (comma-separated):

OpenAI SDK (TypeScript)
Anthropic SDK (Python)

import OpenAI from "openai";

const openai = new OpenAI({
  baseURL: "https://edgee.io/v1",
  apiKey: process.env.EDGEE_API_KEY,
  defaultHeaders: {
    "x-edgee-tags": "production,chat-feature,user-123,team-backend"
  }
});

import os
from anthropic import Anthropic

client = Anthropic(
    base_url="https://edgee.io/v1",
    api_key=os.environ.get("EDGEE_API_KEY"),
    default_headers={
        "x-edgee-tags": "production,chat-feature,user-123,team-backend"
    }
)

Common tagging strategies:

Environment taggingTag by environment: production, staging, development

Feature taggingTag by feature: chat, summarization, code-generation, rag-qa

User/tenant taggingTrack per-user or per-tenant usage: user-123, tenant-acme, customer-xyz

Team taggingOrganize by team: team-backend, team-frontend, team-data

Use tags consistently across your application to enable powerful filtering and cost attribution in your analytics dashboard. You can filter by multiple tags to drill down into specific segments (e.g., “production + chat-feature + team-backend”).

Compression Metrics

See exactly how much token compression is saving you on every request:

const response = await edgee.send({
  model: 'gpt-5.2',
  input: 'Long prompt with lots of context...',
  compression_model: "claude",
});

// Compression details
if (response.compression) {
  console.log(response.compression.saved_tokens); // Tokens saved
  console.log(response.compression.cost_savings); // Cost savings in micro-units (e.g. 27000 = $0.027)
  console.log(response.compression.reduction); // Percentage reduction (e.g. 18 = 18%)
  console.log(response.compression.time_ms); // Time taken for compression in milliseconds
}

Reading the `compression` block

The native Edgee SDK exposes a compression block when compression details are returned. Do not assume every provider-compatible response includes this extension; use the console to inspect those requests.

const response = await edgee.send({
  model: 'gpt-5.2',
  input: 'Long prompt with lots of context...',
});

if (response.compression) {
  console.log(response.compression.saved_tokens); // e.g. 450
  console.log(response.compression.cost_savings); // micro-units (1_000_000 = $1.00)
  console.log(response.compression.reduction);    // percentage, e.g. 18 → 18%
  console.log(response.compression.time_ms);      // ms spent on compression
}

Field reference:

Field	Type	Meaning
`saved_tokens`	integer	Input tokens removed (original count minus compressed count).
`cost_savings`	integer	Estimated cost savings in micro-units. Divide by `1_000_000` for USD.
`reduction`	number	Percentage reduction in input tokens. `18` → 18%.
`time_ms`	integer	Wall-clock time spent on compression.

The usage.prompt_tokens field on the same response reflects the compressed count actually billed by the provider, not the original input.

Get started

Integrations

Edgee SDKs

Application configuration

Gateway API

Token Usage Tracking

Request Tags for Analytics

Compression Metrics

Reading the `compression` block

​Token Usage Tracking

​Request Tags for Analytics

​Compression Metrics

​Reading the compression block

Token Usage Tracking

Request Tags for Analytics

Compression Metrics

Reading the `compression` block