Developer tools

If you’re calling OpenAI, Anthropic, or Bedrock directly from your own code (as opposed to connecting your cloud account through the console), the SDKs are the fastest way to get that traffic showing up in TensorCost. Wrap your existing client in one line; nothing else about your code changes.

Looking for the terminal tool that estimates prompt cost without any account or network access? That’s the CLI — a separate, simpler tool.

Node.js — `@tensorcost/sdk`

npm install @tensorcost/sdk

Requires Node.js 18+ (uses native fetch).

import OpenAI from "openai";
import { wrap } from "@tensorcost/sdk";

const client = wrap(new OpenAI({ apiKey: process.env.OPENAI_API_KEY }));

// Use the client exactly as before — TensorCost observes every call
// and ships fire-and-forget observations to the platform.
const resp = await client.chat.completions.create({
  model: "gpt-4o-mini",
  messages: [{ role: "user", content: "hi" }],
});

Anthropic and Bedrock work the same way:

import Anthropic from "@anthropic-ai/sdk";
import { wrap } from "@tensorcost/sdk";

const client = wrap(new Anthropic({ apiKey: process.env.ANTHROPIC_API_KEY }));

await client.messages.create({
  model: "claude-3-5-sonnet-20241022",
  max_tokens: 256,
  messages: [{ role: "user", content: "hi" }],
});

import { BedrockRuntimeClient, InvokeModelCommand } from "@aws-sdk/client-bedrock-runtime";
import { wrap } from "@tensorcost/sdk";

const client = wrap(new BedrockRuntimeClient({ region: "us-east-1" }));

const response = await client.send(
  new InvokeModelCommand({
    modelId: "anthropic.claude-3-haiku-20240307-v1:0",
    body: JSON.stringify({
      anthropic_version: "bedrock-2023-05-31",
      max_tokens: 64,
      messages: [{ role: "user", content: "What is 2 + 2?" }],
    }),
    contentType: "application/json",
    accept: "application/json",
  }),
);

For Bedrock, @aws-sdk/client-bedrock-runtime is an optional peer dependency — install it only if you’re using Bedrock:

npm install @aws-sdk/client-bedrock-runtime

InvokeModelCommand and ConverseCommand report full token counts. InvokeModelWithResponseStreamCommand and ConverseStreamCommand currently report latency and success/error only — per-chunk token accounting for streaming Bedrock calls is on the roadmap.

Configuration

Variable	Purpose	Required
`TENSORCOST_API_KEY`	Your TensorCost API key, from the console	Yes
`TENSORCOST_BASE_URL`	Backend base URL	No — defaults to `https://api.tensorcost.com`
`TENSORCOST_TENANT_ID`	Explicit tenant ID	No
`TENSORCOST_ENVIRONMENT`	Environment tag stamped on every observation	No

Or pass options explicitly: wrap(client, { apiKey: "...", baseUrl: "..." }).

Applied mode (routing through TensorCost)

By default the SDK is observe-only — your call goes straight to the provider, and a fire-and-forget observation ships afterward. Setting appliedMode: true (plus a proxyUrl) instead routes OpenAI and Anthropic calls through TensorCost’s inference proxy, which can redirect a request to a different provider per your policy. Applied mode is not available for Bedrock — Bedrock’s request signing happens inside the AWS SDK before our wrapper can intercept it, so wrap(bedrockClient, { appliedMode: true }) throws immediately. In applied mode you can also configure retries, timeouts, lifecycle hooks, and a fail-open circuit breaker (opens after 3 consecutive proxy failures, routes direct to the provider, closes again after 5 consecutive successful probes). See the package README on npm for the full option list and typed error classes.

What gets sent

Only metadata — provider, model, operation, timestamps, token counts, status, and a correlation ID. Prompt and completion content are never captured.

Fail-open guarantee

If TensorCost is slow, down, or misconfigured, your underlying provider call still completes normally — the SDK logs a warning and moves on. The one exception is a missing API key, which throws at wrap() time rather than silently dropping every observation.

Python — `tensorcost`

pip install tensorcost

Runtime dependency: httpx only.

from openai import OpenAI
from tensorcost import wrap

client = wrap(OpenAI(api_key="sk-..."))

resp = client.chat.completions.create(
    model="gpt-4o-mini",
    messages=[{"role": "user", "content": "hello"}],
)

Anthropic works identically:

from anthropic import Anthropic
from tensorcost import wrap

client = wrap(Anthropic(api_key="sk-ant-..."))
client.messages.create(
    model="claude-3-5-sonnet-20241022",
    max_tokens=128,
    messages=[{"role": "user", "content": "hello"}],
)

Configuration

wrap() resolves config from, in order: explicit keyword arguments, then environment variables (TENSORCOST_API_KEY, TENSORCOST_BASE_URL, TENSORCOST_TENANT_ID, TENSORCOST_PROXY_URL), then defaults (base_url defaults to https://api.tensorcost.com). A missing API key raises MissingConfigError.

Applied mode

Same idea as the Node SDK: applied_mode=True plus proxy_url routes chat.completions / messages.create through the TensorCost inference proxy, which can redirect the call based on tenant policy. If the proxy call fails, the SDK falls back to the direct provider call and logs a warning rather than failing your request.

Currently supported (Python)

OpenAI (openai >= 1.0) — chat.completions.create, completions.create
Anthropic (anthropic >= 0.20) — messages.create

Bedrock, Azure OpenAI, Vertex AI, streaming responses, tool/function calling, async clients, and LangChain/LlamaIndex adapters are on the roadmap for the Python package — the Node SDK is ahead on Bedrock support today.

MCP

TensorCost also exposes an MCP endpoint for agent-based access — see real-time events for the tool surface and authentication.

Getting Started

Architecture

Setup

Cloud cost accounts

Features

FinOps Console

Reference

Developer tools

Developer tools

Node.js — `@tensorcost/sdk`

Configuration

Applied mode (routing through TensorCost)

What gets sent

Fail-open guarantee

Python — `tensorcost`

Configuration

Applied mode

Currently supported (Python)

MCP

​Developer tools

​Node.js — @tensorcost/sdk

​Configuration

​Applied mode (routing through TensorCost)

​What gets sent

​Fail-open guarantee

​Python — tensorcost

​Configuration

​Applied mode

​Currently supported (Python)

​MCP

Developer tools

Node.js — `@tensorcost/sdk`

Configuration

Applied mode (routing through TensorCost)

What gets sent

Fail-open guarantee

Python — `tensorcost`

Configuration

Applied mode

Currently supported (Python)

MCP