Most teams using LLMs end up with multiple providers. OpenAI for chat and embeddings. Anthropic for long-context or safety-focused use cases. Cursor for developer tooling. Each has its own pricing model, billing cycle, and usage patterns. None of them talk to each other.
The result: fragmented visibility. You know OpenAI spend. You know Anthropic spend. You might not know Cursor spend. You definitely don't know the total until you add up three different dashboards—or three different invoices.
The Multi-Provider Reality
OpenAI charges per token. GPT-4 costs more than GPT-3.5. Embeddings cost less than chat. Usage is driven by product features, user traffic, and model choices.
Anthropic charges per token with different tiers for Claude models. Context windows are large; so are the bills when you use them. Enterprise use cases often default to Claude for compliance or safety.
Cursor charges per team, with usage tied to AI-assisted coding. Teams adopt Cursor for productivity; the cost scales with developers and their usage. Historical data is often limited to a few days.
Add cloud-hosted models (AWS Bedrock, Azure OpenAI, GCP Vertex) and the picture gets more complex. Now you have five or six sources of AI spend, each with different billing cadences and reporting.
Why Fragmented Tracking Fails
When each provider is tracked separately:
- No single total—you don't know combined AI spend until you manually aggregate.
- No cross-provider trends—you can't see "total LLM spend is up 60% month-over-month."
- No unified forecasting—each provider has its own pace; the aggregate is guesswork.
- Delayed discovery—by the time you reconcile, the month is over and the overrun is real.
What Unified LLM Cost Tracking Looks Like
A single view that shows:
- Total AI spend across OpenAI, Anthropic, Cursor, and any cloud AI services
- Spend by provider—which one grew, which one stayed flat
- Daily trends—is today's spend normal or spiking?
- Forecasts—where is combined spend heading this month?
This isn't about replacing provider dashboards. It's about adding a layer above them. Provider dashboards tell you usage and billing details. A unified view tells you the big picture: total AI cost and how it's changing.
Practical Steps
- Connect all LLM providers you use—OpenAI, Anthropic, Cursor—to one cost-tracking tool.
- Include cloud AI if you use AWS Bedrock, Azure OpenAI, or GCP Vertex. These often show up in cloud billing; they're still LLM spend.
- Set a combined baseline—what does a normal day look like across all providers?
- Alert on anomalies—when combined daily spend exceeds baseline by a threshold, investigate.
- Forecast the aggregate—"Total LLM spend will be $X this month" is more useful than per-provider forecasts that you add up manually.
LLM spend management isn't about choosing one provider. It's about seeing all of them in one place. When you do, overruns become visible before they become expensive.
Get started: Connect OpenAI, Anthropic, and Cursor to StackSpend for unified LLM cost tracking. Learn more about AI cost monitoring.