https://www.iaifeed.com iAIFeed Wed, 07 Oct 2026 12:43:07 +0000 en-US hourly 1 https://wordpress.org/?v=7.1.3 https://www.iaifeed.com/wp-content/uploads/2026/07/cropped-iaifeed1-80x80.png https://www.iaifeed.com 32 32 https://www.iaifeed.com/how-to-track-llm-costs-across-providers-with-litellm https://www.iaifeed.com/how-to-track-llm-costs-across-providers-with-litellm#respond Wed, 07 Oct 2026 12:43:07 +0000 https://www.iaifeed.com/how-to-track-llm-costs-across-providers-with-litellm When several apps and teammates hit different LLM providers, the bill becomes guesswork. LiteLLM solves that by routing everything through one proxy that meters spend per key. You get a single place to see who spent what, and you can cap it before the invoice surprises you.

This builds on the basic LiteLLM proxy setup. Start there if you have not run litellm --config config.yaml yet — here we focus on the accounting layer.

How do LiteLLM virtual keys work?

A virtual key is a scoped token you hand to an app or a person. It maps to one or more models, carries its own spend limit, and can expire. Requests made with that key are attributed to it, so usage reports break down by key rather than by a shared provider account.

How do you set a budget?

1. Enable the database so LiteLLM can persist spend (Postgres).
2. Create a key with max_budget — say $50 per month.
3. Optionally set budget_duration: 30d so the cap resets monthly.
4. Set a rpm_limit and tpm_limit to stop runaway loops.
5. Hand the key to the app; let LiteLLM enforce the ceiling.

When a key hits its limit, requests fail until the window resets — a hard stop that protects the wallet.

How do you attribute spend to teams or projects?

Use tags and teams. Tag a group of keys by project, or create a team with a shared budget and issue child keys. Reports then roll up by tag or team, which is what finance asks for. Without tagging, you get totals but no ownership.

Which logging destinations are supported?

LiteLLM can export usage to Langfuse, Helicone, Prometheus, or a plain database, and it exposes an admin UI that shows recent spend. Pick one destination and stick to it rather than duplicating logs in several tools.

What are the real limits?

The cost estimates depend on per-model pricing tables; a brand-new model may be priced wrong until LiteLLM updates its list. Self-hosting means you also run Postgres and keep it backed up. And a budget stops spend, not bad prompts — it will not tell you that a loop is burning tokens for no output, only that the key is out of money.

Key Takeaways

• Virtual keys attribute LLM spend to a specific app or person.
• Set max_budget and budget_duration to cap monthly spend.
• Use tags or teams to roll costs up by project.
• Logs export to Langfuse, Helicone or Prometheus.
• Budget enforcement needs the Postgres database enabled.

Last updated October 2026. More on the LiteLLM page.

]]> https://www.iaifeed.com/how-to-track-llm-costs-across-providers-with-litellm/feed 0 https://www.iaifeed.com/how-to-set-up-litellm-proxy-for-100-llm-apis https://www.iaifeed.com/how-to-set-up-litellm-proxy-for-100-llm-apis#respond Wed, 07 Oct 2026 12:42:53 +0000 https://www.iaifeed.com/how-to-set-up-litellm-proxy-for-100-llm-apis LiteLLM is an open-source library and proxy server that gives you one OpenAI-compatible endpoint for over 100 LLM APIs — OpenAI, Anthropic, Gemini, Mistral, Cohere and local models. Instead of rewriting code when you switch providers, you change a config line. The proxy mode also adds budgets, rate limits and logging across keys.

The Python package is free, and the self-hosted proxy is free. Only the hosted enterprise tier is paid. For most developers, LiteLLM is the layer that stops provider lock-in.

What is LiteLLM?

LiteLLM is a unified interface for calling LLMs. In library mode you import it and call completion() with a model string like anthropic/claude-3-5-sonnet. In proxy mode you run a server that speaks the OpenAI API and routes each request to the right provider.

How do you install the LiteLLM proxy?

1. pip install 'litellm[proxy]'
2. Create a config.yaml with your models and API keys.
3. Start it: litellm --config config.yaml --port 4000
4. Point your app at http://localhost:4000 instead of a provider URL.
5. Call it with any OpenAI client, setting the model to your configured alias.

Because the interface matches OpenAI, existing SDKs work without changes.

How do you define models and keys?

Each entry in config.yaml has a model name, the underlying provider model, and the credentials. You can set a fallback list so a failed model rolls over to another provider automatically. Virtual keys let you mint scoped tokens for teams, each with its own budget and rate limit.

What do budgets and logging give you?

The proxy tracks spend per key and can hard-stop a key when it hits its limit — useful when many services share one provider account. It logs every request and can push usage to tools like Langfuse, Helicone or Prometheus. That visibility is the main reason teams run the proxy instead of the bare library.

When should you use the library instead?

If you have a single app and no need for shared budgets, importing the library is simpler — no server to run. Use the proxy when multiple apps or teammates share providers, or when you want centralized logging and failover. Running the proxy for one script is overkill.

Key Takeaways

• LiteLLM gives one OpenAI-compatible endpoint for 100+ LLM providers.
• The library and self-hosted proxy are free; only enterprise hosting is paid.
• Install with pip install 'litellm[proxy]' and run on port 4000.
• Config supports fallbacks, virtual keys, budgets and rate limits.
• Use the library for single apps, the proxy for shared/multi-service setups.

Last updated October 2026. Specs at LiteLLM.

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