--- title: "litellm vs MInference" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/berriai-litellm-vs-microsoft-minference" tools: ["berriai-litellm", "microsoft-minference"] --- # litellm vs MInference *GraphCanon updated Jul 12, 2026* ## Verdict Pick litellm if 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; pick MInference if mInference accelerates long-context LLMs' inference by up to 10x via approximate sparse calculation techniques while preserving model accuracy. [litellm](https://docs.litellm.ai/docs/) reports 53k GitHub stars, 9.7k forks, and 3.9k open issues, last pushed Jul 11, 2026. [MInference](https://aka.ms/MInference) has 1.2k stars, 78 forks, and 93 open issues, last pushed Apr 8, 2026. Figures are from public GitHub metadata via [litellm's repository](https://github.com/BerriAI/litellm) and [MInference's repository](https://github.com/microsoft/MInference). | | [litellm](/tools/berriai-litellm.md) | [MInference](/tools/microsoft-minference.md) | | --- | --- | --- | | Tagline | Python SDK and Proxy Server for calling multiple LLM APIs | Accelerates Long-context LLMs' inference through approximate sparse calculation for attention. | | Stars | 53,271 | 1,221 | | Forks | 9,671 | 78 | | Open issues | 3,915 | 93 | | 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. | MInference accelerates long-context LLMs' inference by up to 10x via approximate sparse calculation techniques while preserving model accuracy. | | 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. | MIT | | Categories | Inference & Serving, LLM Frameworks | Inference & Serving | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [litellm](/tools/berriai-litellm.md) | [MInference](/tools/microsoft-minference.md) | | --- | --- | --- | | Maintenance | Very active (96%) | Slowing (36%) | | Days since push | 0d | 94d | | Open issues (now) | 3.9k | 93 | | Security scan | 2 low (2 low) | No lockfile | | Full report | [trust report](/tools/berriai-litellm/trust.md) | [trust report](/tools/microsoft-minference/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. ## Decision facts: MInference - **Requirements:** Min 8 GB RAM; MInference requires at least Torch and optionally FlashAttention-2 for maximum efficiency.; Triton for faster deployment and integration. - **Adopt for:** MInference accelerates long-context LLMs' inference by up to 10x via approximate sparse calculation techniques while preserving model accuracy. ## Choose when ### Choose litellm if… - License: litellm is Other, MInference is MIT. - 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. - Also covers LLM Frameworks. - 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 MInference if… - License: MInference is MIT, litellm is Other. - Requirements: Min 8 GB RAM; MInference requires at least Torch and optionally FlashAttention-2 for maximum efficiency.; Triton for faster deployment and integration.. - Tags unique to MInference: attention mechanism, flashattention-2, inference acceleration, long-context llms. - MInference is ideal for scenarios where significant reduction in inference latency is needed without sacrificing the accuracy of long-context LLM outputs. ## 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 MInference - Avoid using MInference if your application does not benefit from or cannot tolerate slight variations in inference times due to its use of approximate sparse calculation. - MInference might not be suitable for applications where the model's accuracy is critical and any reduction in the precision introduced by approximations would be detrimental. ## Common questions ### What is the difference between litellm and MInference? litellm: Python SDK and Proxy Server for calling multiple LLM APIs. MInference: Accelerates Long-context LLMs' inference through approximate sparse calculation for attention.. See the comparison table for live GitHub stats and shared categories. ### When should I choose litellm over MInference? Choose litellm over MInference when License: litellm is Other, MInference is MIT; 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; Also covers LLM Frameworks; 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 MInference over litellm? Choose MInference over litellm when License: MInference is MIT, litellm is Other; Requirements: Min 8 GB RAM; MInference requires at least Torch and optionally FlashAttention-2 for maximum efficiency.; Triton for faster deployment and integration.; Tags unique to MInference: attention mechanism, flashattention-2, inference acceleration, long-context llms; MInference is ideal for scenarios where significant reduction in inference latency is needed without sacrificing the accuracy of long-context LLM outputs. ### 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 MInference? Avoid using MInference if your application does not benefit from or cannot tolerate slight variations in inference times due to its use of approximate sparse calculation. MInference might not be suitable for applications where the model's accuracy is critical and any reduction in the precision introduced by approximations would be detrimental. ### Is litellm or MInference more popular on GitHub? litellm has more GitHub stars (53,271 vs 1,221). Stars measure visibility, not whether either tool fits your constraints. ### Are litellm and MInference open source? Yes - both are open-source projects on GitHub (litellm: Other, MInference: MIT). ### Where can I find alternatives to litellm or MInference? GraphCanon lists graph-backed alternatives at [litellm alternatives](/tools/berriai-litellm/alternatives) and [MInference alternatives](/tools/microsoft-minference/alternatives) ([litellm markdown twin](/tools/berriai-litellm/alternatives.md), [MInference markdown twin](/tools/microsoft-minference/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-microsoft-minference.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, litellm or MInference? litellm: Very active. MInference: Slowing. 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 MInference? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [litellm trust report](/tools/berriai-litellm/trust); [MInference trust report](/tools/microsoft-minference/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/_