---
title: "litellm vs Awesome-LLM-Inference"
type: "comparison"
canonical_url: "https://www.graphcanon.com/compare/berriai-litellm-vs-xlite-dev-awesome-llm-inference"
tools: ["berriai-litellm", "xlite-dev-awesome-llm-inference"]
---

# litellm vs Awesome-LLM-Inference

*GraphCanon updated Jul 11, 2026*

## Verdict

Pick litellm when license: litellm is Other, Awesome-LLM-Inference is GPL-3.0; pick Awesome-LLM-Inference when license: Awesome-LLM-Inference is GPL-3.0, litellm is Other.

[litellm](https://docs.litellm.ai/docs/) reports 53k GitHub stars, 9.7k forks, and 3.9k open issues, last pushed Jul 11, 2026. [Awesome-LLM-Inference](https://github.com/xlite-dev/Awesome-LLM-Inference) has 5.4k stars, 421 forks, and 4 open issues, last pushed Jun 23, 2026. Figures are from public GitHub metadata via [litellm's repository](https://github.com/BerriAI/litellm) and [Awesome-LLM-Inference's repository](https://github.com/xlite-dev/Awesome-LLM-Inference).

| | [litellm](/tools/berriai-litellm.md) | [Awesome-LLM-Inference](/tools/xlite-dev-awesome-llm-inference.md) |
| --- | --- | --- |
| Tagline | Python SDK and Proxy Server for calling multiple LLM APIs | 📚A curated list of Awesome LLM/VLM Inference Papers with Codes: Flash-Attention, Paged-Attention, WINT8/4, Parallelism, etc.🎉 |
| Stars | 53,271 | 5,383 |
| Forks | 9,671 | 421 |
| Open issues | 3,915 | 4 |
| Language | Python | Python |
| Adopt for | litellm is a Python SDK and Proxy Server that facilitates the interaction with over 100 LLM APIs, offering features such as cost tracking, guardrails, load balancing, and logging. | - |
| Persona | - | - |
| Runtime | - | - |
| License | The licensing terms for LiteLLM are provided under a license type categorized as 'Other'; details of the exact license should be referenced directly from its source. | GPL-3.0 |
| Categories | Inference & Serving, LLM Frameworks | Inference & Serving, LLM Frameworks |

## Trust and health

_Sourced signals - not a safety guarantee. No winner column._

| | [litellm](/tools/berriai-litellm.md) | [Awesome-LLM-Inference](/tools/xlite-dev-awesome-llm-inference.md) |
| --- | --- | --- |
| Maintenance | Very active (96%) | Active (82%) |
| Days since push | 0d | 18d |
| Open issues (now) | 3.9k | 4 |
| Security scan | 2 low (2 low) | No lockfile |
| Full report | [trust report](/tools/berriai-litellm/trust.md) | [trust report](/tools/xlite-dev-awesome-llm-inference/trust.md) |

## Decision facts: litellm

- **Pricing:** freemium - While the core functionality is provided free, specific extended features might require a paid plan.
- **Requirements:** Requires Docker
- **Adopt for:** litellm is a Python SDK and Proxy Server that facilitates the interaction with over 100 LLM APIs, offering features such as cost tracking, guardrails, load balancing, and logging.
- **License detail:** The licensing terms for LiteLLM are provided under a license type categorized as 'Other'; details of the exact license should be referenced directly from its source.

## Choose when

### Choose litellm if…

- License: litellm is Other, Awesome-LLM-Inference is GPL-3.0.
- Pricing: While the core functionality is provided free, specific extended features might require a paid plan..
- Requirements: Requires Docker.
- Tags unique to litellm: ai-gateway, azure-openai, bedrock, llm.
- litellm ships Docker support for self-hosted deployment.
- When you need to integrate multiple LLM (Language Learning Modelling) APIs into your application across different providers like Bedrock, Azure, OpenAI, VertexAI, Cohere, Anthropic, Sagemaker, Hugging

### Choose Awesome-LLM-Inference if…

- License: Awesome-LLM-Inference is GPL-3.0, litellm is Other.
- Tags unique to Awesome-LLM-Inference: awesome-llm, deepseek, deepseek-r1, deepseek-v3.
- Leaner open-issue backlog (4).

## When NOT to use litellm

- If your project only requires interaction with a single LLM API and basic functionalities, litellm may be overkill.

## When NOT to use Awesome-LLM-Inference

- Inference & Serving: Self-hosting rarely beats a hosted API on cost until you have steady, high-volume traffic.
- LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves.

## Common questions

### What is the difference between litellm and Awesome-LLM-Inference?

litellm: Python SDK and Proxy Server for calling multiple LLM APIs. Awesome-LLM-Inference: 📚A curated list of Awesome LLM/VLM Inference Papers with Codes: Flash-Attention, Paged-Attention, WINT8/4, Parallelism, etc.🎉. See the comparison table for live GitHub stats and shared categories.

### When should I choose litellm over Awesome-LLM-Inference?

Choose litellm over Awesome-LLM-Inference when License: litellm is Other, Awesome-LLM-Inference is GPL-3.0; Pricing: While the core functionality is provided free, specific extended features might require a paid plan.; Requirements: Requires Docker; Tags unique to litellm: ai-gateway, azure-openai, bedrock, llm; litellm ships Docker support for self-hosted deployment; When you need to integrate multiple LLM (Language Learning Modelling) APIs into your application across different providers like Bedrock, Azure, OpenAI, VertexAI, Cohere, Anthropic, Sagemaker, Hugging.

### When should I choose Awesome-LLM-Inference over litellm?

Choose Awesome-LLM-Inference over litellm when License: Awesome-LLM-Inference is GPL-3.0, litellm is Other; Tags unique to Awesome-LLM-Inference: awesome-llm, deepseek, deepseek-r1, deepseek-v3; Leaner open-issue backlog (4).

### When should I avoid litellm?

If your project only requires interaction with a single LLM API and basic functionalities, litellm may be overkill.

### When should I avoid Awesome-LLM-Inference?

Inference & Serving: Self-hosting rarely beats a hosted API on cost until you have steady, high-volume traffic. LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves.

### Is litellm or Awesome-LLM-Inference more popular on GitHub?

litellm has more GitHub stars (53,271 vs 5,383). Stars measure visibility, not whether either tool fits your constraints.

### Are litellm and Awesome-LLM-Inference open source?

Yes - both are open-source projects on GitHub (litellm: Other, Awesome-LLM-Inference: GPL-3.0).

### Where can I find alternatives to litellm or Awesome-LLM-Inference?

GraphCanon lists graph-backed alternatives at [litellm alternatives](/tools/berriai-litellm/alternatives) and [Awesome-LLM-Inference alternatives](/tools/xlite-dev-awesome-llm-inference/alternatives) ([litellm markdown twin](/tools/berriai-litellm/alternatives.md), [Awesome-LLM-Inference markdown twin](/tools/xlite-dev-awesome-llm-inference/alternatives.md)), ranked by typed relationship edges rather than popularity votes.

### Is there a machine-readable version of this comparison?

Yes. The markdown twin at [this comparison](/compare/berriai-litellm-vs-xlite-dev-awesome-llm-inference.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers.

### Which is better maintained, litellm or Awesome-LLM-Inference?

litellm: Very active. Awesome-LLM-Inference: Active. Compare maintenance labels, days since push, and release cadence in the trust section below - stars alone do not measure maintenance.

### Where are the full trust reports for litellm and Awesome-LLM-Inference?

GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [litellm trust report](/tools/berriai-litellm/trust); [Awesome-LLM-Inference trust report](/tools/xlite-dev-awesome-llm-inference/trust).

---

**Machine-readable endpoints**

- JSON: [`/api/graphcanon/graph?tool=berriai-litellm`](/api/graphcanon/graph?tool=berriai-litellm)
- LLM index: [/llms.txt](/llms.txt)
- Full corpus: [/llms-full.txt](/llms-full.txt)

_GraphCanon - The knowledge graph for AI development. https://www.graphcanon.com/_