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Multi-Provider

Track LLM Costs Across 12+ Providers in One Dashboard

SimplifAI Team

The question we hear most from engineering teams is some version of: "How do we actually see what we're spending across all our LLM providers?" Not just OpenAI. Not just Anthropic. All of them — in one place, broken down by model, feature, and team.

It sounds like it should be solved. It isn't. Here's why, and what to do about it.

The Multi-Provider Problem

A year ago, most teams had one LLM provider. Today, the typical production stack looks more like this: OpenAI for fast general tasks, Claude for reasoning-heavy workflows, Gemini for long-context document processing, and one or two cheaper providers — Groq, Together AI, or DeepSeek — for high-volume inference where cost-per-call matters more than raw capability.

That's four providers minimum, and often more. Each comes with its own dashboard, its own billing cycle, its own definition of what a "token" costs. OpenAI resets on the first of the month. Anthropic charges by usage tier. AWS Bedrock bills land inside your broader AWS invoice. Azure OpenAI costs are buried in resource groups. There is no shared currency, no shared timeline, and no shared view.

The result: you have no idea what you're actually spending on AI, or where that spend is going.

The 14+ Providers You're Probably Using

Here's a realistic picture of what the multi-provider landscape looks like in 2026, with approximate pricing for the most common model at each provider:

Provider Common Model Input / 1M tokens Output / 1M tokens Billing Home
OpenAI GPT-4o $2.50 $10.00 platform.openai.com
Anthropic Claude Sonnet 4 $3.00 $15.00 console.anthropic.com
Google Gemini Gemini 2.5 Pro $1.25 $10.00 aistudio.google.com
Mistral Mistral Large $2.00 $6.00 console.mistral.ai
Cohere Command R+ $2.50 $10.00 dashboard.cohere.com
AWS Bedrock Claude / Llama 3 varies varies AWS Cost Explorer
Azure OpenAI GPT-4o $2.50 $10.00 Azure portal
Together AI Llama 3.3 70B $0.88 $0.88 api.together.ai
Groq Llama 3.3 70B $0.59 $0.79 console.groq.com
Perplexity Sonar Pro $3.00 $15.00 perplexity.ai/settings
Replicate Various per-run per-run replicate.com/billing
OpenRouter Any model varies varies openrouter.ai/activity
Llama API Llama 4 Scout $0.11 $0.34 llama.developer.meta.com
DeepSeek DeepSeek V3 $0.27 $1.10 platform.deepseek.com

That's 14 separate places to check billing. And some providers — Bedrock, Azure — don't even give you a clean per-model breakdown without additional configuration. If you're routing traffic dynamically (which you should be), tracking actual spend becomes a genuine engineering problem.

Why Unified Tracking Actually Matters

This isn't just an accounting inconvenience. Multi-provider blind spots create real operational problems:

  • Cost attribution is impossible. Which team, feature, or customer is driving your AI spend? Without a unified log, the answer is "we're not sure." That makes chargeback models, per-customer billing, and budget accountability nearly impossible.
  • You can't detect waste. A broken retry loop on a Bedrock endpoint can generate thousands of spurious API calls before anyone notices. If your costs are spread across 6 dashboards, anomalies take days — or weeks — to surface.
  • Budget alerts don't work cross-provider. Each provider has its own alerting system. You can set a $500 alert on OpenAI and a $200 alert on Anthropic, but you can't set a $700 alert on your combined AI budget. That combined view simply doesn't exist natively.
  • Model comparisons are guesswork. If you're deciding whether to migrate a workload from GPT-4o mini to Groq's Llama, you need apples-to-apples cost data on equivalent request volumes. Piecing that together from two different dashboards with different date filters is error-prone and slow.

Three Approaches to Multi-Provider Cost Tracking

Teams typically end up in one of three places when they try to solve this:

1. Spreadsheet hell. Someone exports CSV from each provider, pastes them into a shared Google Sheet, applies manual exchange math between billing units (tokens, compute hours, per-run), and tries to normalize everything into a single currency. This works for a week, then falls apart when a new provider is added, a billing format changes, or the person who built the sheet leaves the team.

2. DIY logging. Engineers add logging middleware to capture token usage from every API response. In principle, this is correct — the usage data is right there in the response object. In practice, you end up maintaining a bespoke observability pipeline: a database schema, a query layer, a dashboard of some kind, and ongoing maintenance every time an SDK changes its response format. The total engineering cost is usually 2-4 weeks of developer time, with ongoing maintenance overhead.

3. An observability tool. The cleanest solution if it actually supports your provider set. Most LLM observability tools were built when OpenAI was the only game in town. They support OpenAI thoroughly, add partial Anthropic support as an afterthought, and have spotty or no coverage for the long tail of providers — Groq, Together AI, Replicate, DeepSeek. You end up with a "unified" dashboard that's missing 40% of your actual spend.

What to Look for in a Multi-Provider Cost Dashboard

If you're evaluating tools — or building something yourself — these are the capabilities that actually matter:

  • Provider normalization. Every provider reports usage differently. Bedrock uses "input tokens" and "output tokens." Replicate charges by the second of compute. OpenRouter aggregates multiple underlying models. A real multi-provider tracker normalizes all of this into a consistent schema — ideally USD cost, input tokens, and output tokens at minimum.
  • Per-model breakdown. Provider-level spend isn't enough. You need to know that your Claude Sonnet 4 usage is 3x your Haiku usage, and that specific feature is responsible. Model granularity is non-negotiable.
  • Cost attribution by tag or label. The ability to tag requests with metadata — feature name, user ID, environment, team — so you can answer "how much did the document summarizer cost last week?" not just "how much did we spend on Anthropic?"
  • Real-time vs. delayed data. Provider dashboards are often 24-48 hours delayed. For anomaly detection and budget alerts, you want near-real-time data — ideally updated within minutes of each API call.
  • Alert thresholds that span providers. A budget alert that fires when your combined spend across all providers crosses a threshold, not just per-provider limits.
  • No proxy requirement. Proxy-based solutions route all your API traffic through a third-party server. That means added latency, a new failure point, and a third party with access to your prompts and completions. SDK-based approaches — where you report usage data after the call completes — avoid all of these concerns.

LLMeter: 2-Minute Setup, All Providers

LLMeter was built specifically to solve the multi-provider tracking problem without the proxy tradeoff. Instead of sitting in the middle of your API calls, LLMeter uses an ingest API: you make your normal provider call using the official SDK, and then send the usage data — tokens, model, cost — to LLMeter in a single lightweight POST request after the call completes.

This means:

  • No latency added to your actual LLM calls
  • No third party sees your prompts or completions — only token counts and metadata
  • Works with every provider that returns usage data in its response (all 14 listed above do)
  • No SDK to install — just a plain HTTP call to a single endpoint
  • Setup takes about 2 minutes

The unified dashboard shows spend by provider, by model, by day, and by any custom tag you attach to the ingest call. Budget alerts fire across your combined spend, not per-provider. And because you're sending data after the call, there's no risk to your LLM uptime if LLMeter ever has an issue.

Code Example: Tracking Multi-Provider Usage

Here's how to instrument two providers — OpenAI and Anthropic — with LLMeter in Python. The pattern is identical for every other provider: make your API call, read the token counts from the response, send to LLMeter.

import anthropic
import openai
import httpx

LLMETER_KEY = "your_api_key"
LLMETER_INGEST = "https://llmeter.org/api/ingest"

def track(provider, model, input_tokens, output_tokens, cost, tags=None):
    httpx.post(LLMETER_INGEST, json={
        "provider": provider,
        "model": model,
        "input_tokens": input_tokens,
        "output_tokens": output_tokens,
        "cost": cost,
        "tags": tags or {},
    }, headers={"Authorization": f"Bearer {LLMETER_KEY}"})

# --- OpenAI ---
oai = openai.OpenAI()
oai_resp = oai.chat.completions.create(
    model="gpt-4o",
    messages=[{"role": "user", "content": "Summarize this document."}],
)
usage = oai_resp.usage
track(
    provider="openai",
    model="gpt-4o",
    input_tokens=usage.prompt_tokens,
    output_tokens=usage.completion_tokens,
    cost=(usage.prompt_tokens / 1_000_000 * 2.50)
        + (usage.completion_tokens / 1_000_000 * 10.00),
    tags={"feature": "doc-summarizer", "env": "production"},
)

# --- Anthropic ---
ant = anthropic.Anthropic()
ant_resp = ant.messages.create(
    model="claude-sonnet-4-5",
    max_tokens=1024,
    messages=[{"role": "user", "content": "Explain this contract clause."}],
)
usage = ant_resp.usage
track(
    provider="anthropic",
    model="claude-sonnet-4-5",
    input_tokens=usage.input_tokens,
    output_tokens=usage.output_tokens,
    cost=(usage.input_tokens / 1_000_000 * 3.00)
        + (usage.output_tokens / 1_000_000 * 15.00),
    tags={"feature": "contract-review", "env": "production"},
)

And the same pattern in JavaScript / Node.js, showing Groq and Google Gemini side by side:

import Groq from "groq-sdk";
import { GoogleGenerativeAI } from "@google/generative-ai";

const LLMETER_KEY = "your_api_key";
const LLMETER_INGEST = "https://llmeter.org/api/ingest";

async function track({ provider, model, inputTokens, outputTokens, cost, tags = {} }) {
  await fetch(LLMETER_INGEST, {
    method: "POST",
    headers: {
      "Content-Type": "application/json",
      Authorization: `Bearer ${LLMETER_KEY}`,
    },
    body: JSON.stringify({
      provider,
      model,
      input_tokens: inputTokens,
      output_tokens: outputTokens,
      cost,
      tags,
    }),
  });
}

// --- Groq ---
const groq = new Groq();
const groqResp = await groq.chat.completions.create({
  model: "llama-3.3-70b-versatile",
  messages: [{ role: "user", content: "Classify this support ticket." }],
});
const groqUsage = groqResp.usage;
await track({
  provider: "groq",
  model: "llama-3.3-70b-versatile",
  inputTokens: groqUsage.prompt_tokens,
  outputTokens: groqUsage.completion_tokens,
  cost:
    (groqUsage.prompt_tokens / 1_000_000) * 0.59 +
    (groqUsage.completion_tokens / 1_000_000) * 0.79,
  tags: { feature: "support-classifier", team: "cx" },
});

// --- Google Gemini ---
const genai = new GoogleGenerativeAI(process.env.GEMINI_API_KEY);
const geminiModel = genai.getGenerativeModel({ model: "gemini-2.5-pro" });
const geminiResp = await geminiModel.generateContent("Analyze this dataset.");
const geminiMeta = geminiResp.response.usageMetadata;
await track({
  provider: "google",
  model: "gemini-2.5-pro",
  inputTokens: geminiMeta.promptTokenCount,
  outputTokens: geminiMeta.candidatesTokenCount,
  cost:
    (geminiMeta.promptTokenCount / 1_000_000) * 1.25 +
    (geminiMeta.candidatesTokenCount / 1_000_000) * 10.0,
  tags: { feature: "data-analysis", team: "analytics" },
});

The tags object is arbitrary — send whatever metadata is useful for your cost attribution. Feature name, user ID, customer tier, environment, experiment variant. Everything you tag shows up as a filter dimension in your LLMeter dashboard.

What You Can See Once You're Tracking

Once usage data from all your providers flows into a single place, cost questions that were previously unanswerable become routine:

  • Which provider is actually cheapest for your specific workload at your specific volumes?
  • Which feature is responsible for the spike in Anthropic spend last Tuesday?
  • Is the new DeepSeek integration saving money relative to what it replaced?
  • What's the cost-per-customer for the AI features in your Pro tier?
  • Which model is being called most — and is that the most cost-effective choice for that use case?

These are the questions that lead to real cost reductions. You can't answer them by switching between 14 browser tabs.

The Bottom Line

Multi-provider LLM stacks are the norm in 2026, not the exception. But the tooling hasn't caught up — most teams are still navigating provider-by-provider dashboards with no unified view, no cross-provider budget alerts, and no way to attribute costs to the features and customers driving them.

The fix doesn't require rearchitecting your stack or routing traffic through a proxy. It requires sending a small payload to an ingest API after each LLM call — 10 lines of code that unlock a full cross-provider cost picture.

If you're running more than two providers and still managing cost visibility in spreadsheets, it's worth spending the 2 minutes to set up a proper unified view. LLMeter is free to start — no credit card, no proxy, no SDK to install. Just connect your usage data and get the dashboard your multi-provider stack deserves.

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