Comparison
paddler vs sglang
paddler (Open-source LLM load balancer and serving platform) vs sglang (Serving framework for large language models and multimodal models) - live GitHub stats and typed graph relationships, not marketing.
Markdown twin · paddler alternatives · sglang alternatives
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Tagline
- paddler
- Open-source LLM load balancer and serving platform
- sglang
- Serving framework for large language models and multimodal models
Stars
- paddler
- 1.6k
- sglang
- 30k
Forks
- paddler
- 89
- sglang
- 7.0k
Open issues
- paddler
- 25
- sglang
- 4.1k
Language
- paddler
- Rust
- sglang
- Python
Adopt for
- paddler
- Paddler is a self-hosting platform built in Rust for managing inference and deployment of Language and Vision Models (LLMs/VLMs) on private infrastructure. It offers features like dynamic agent addition, request handling
- sglang
- SGLang is a high-performance serving framework designed specifically for deploying, optimizing inference tasks on large language models (LLMs) and multimodal models. It supports multiple backend architectures including n
Persona
- paddler
- -
- sglang
- -
Runtime
- paddler
- -
- sglang
- -
License
- paddler
- Apache-2.0
- sglang
- SGLang is licensed under the Apache-2.0 license, offering permissive open-source terms that are flexible for commercial use with attribution requirements.
Last pushed
- paddler
- Jul 7, 2026
- sglang
- Jul 8, 2026
Categories
- paddler
- Inference & Serving
- sglang
- Inference & Serving
Trust and health
Days since push
- paddler
- 1d
- sglang
- 0d
Open issues (now)
- paddler
- 25
- sglang
- 4.1k
Security scan
- paddler
- Not scanned
- sglang
- No lockfile
Full report
- paddler
- Trust report
- sglang
- Trust report
Typed relationship
paddler alternative sglangBoth sglang and Paddler offer serving frameworks for large language models with considerations for multimodal models; however, their approaches to deployment and management may differ.
Choose paddler if…
- paddler is primarily Rust; sglang is Python.
- Requirements: Min 8 GB RAM; Requires a Rust toolchain with MSRV of at least 1.88.0 for building from source..
- Both sglang and Paddler offer serving frameworks for large language models with considerations for multimodal models; however, their approaches to deployment and management may differ.
- Tags unique to paddler: llmops, ggml ecosystem, deployment, ai.
- paddler ships Docker support for self-hosted deployment.
- - When you need to deploy LLM and VLM models at scale with precise control over your own hardware and software environment.
When NOT to use paddler
- - If you prefer platforms with a larger community base or extensive third-party integrations. Paddler may not offer the depth of support for specific use cases as more established competitors might.
- - For those looking for extensive pre-built model integration and automation tools, since Paddler focuses on minimalistic setup around ggml ecosystem.
Choose sglang if…
- sglang is primarily Python; paddler is Rust.
- Deploy SGLang in a self-hosted environment tailored to your specific hardware, such as NVIDIA GPUs or TPUs.
- Both sglang and Paddler offer serving frameworks for large language models with considerations for multimodal models; however, their approaches to deployment and management may differ.
- Tags unique to sglang: llama, deepseek, cuda, diffusion.
- When you require support for the latest open-source model releases such as Nemotron 3 Ultra, Nemotron 3 Super, or Higgs Audio v3 TTS.
When NOT to use sglang
- Avoid using SGLang if your project relies exclusively on CPU-based inference, as it specifically optimizes for GPU architectures like CUDA.
- SGLang may not be suitable for scenarios where the primary model focus is reinforcement learning (RL), given its specific strengths in LLM and multimodal model serving.
- If you need a broader range of features beyond solely inference speed and efficiency for large language models, SGLang's specialized capabilities might not address all your needs.
Explore
paddler trust report →sglang trust report →Inference & Serving category →All comparisonsStack workflowsTrending tools
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Common questions
- What is the difference between paddler and sglang?
- paddler: Open-source LLM load balancer and serving platform. sglang: Serving framework for large language models and multimodal models. See the comparison table for live GitHub stats and shared categories.
- When should I choose paddler over sglang?
- Choose paddler over sglang when paddler is primarily Rust; sglang is Python; Requirements: Min 8 GB RAM; Requires a Rust toolchain with MSRV of at least 1.88.0 for building from source.; Both sglang and Paddler offer serving frameworks for large language models with considerations for multimodal models; however, their approaches to deployment and management may differ; Tags unique to paddler: llmops, ggml ecosystem, deployment, ai; paddler ships Docker support for self-hosted deployment; - When you need to deploy LLM and VLM models at scale with precise control over your own hardware and software environment.
- When should I choose sglang over paddler?
- Choose sglang over paddler when sglang is primarily Python; paddler is Rust; Deploy SGLang in a self-hosted environment tailored to your specific hardware, such as NVIDIA GPUs or TPUs; Both sglang and Paddler offer serving frameworks for large language models with considerations for multimodal models; however, their approaches to deployment and management may differ; Tags unique to sglang: llama, deepseek, cuda, diffusion; When you require support for the latest open-source model releases such as Nemotron 3 Ultra, Nemotron 3 Super, or Higgs Audio v3 TTS.
- When should I avoid paddler?
- - If you prefer platforms with a larger community base or extensive third-party integrations. Paddler may not offer the depth of support for specific use cases as more established competitors might. - For those looking for extensive pre-built model integration and automation tools, since Paddler focuses on minimalistic setup around ggml ecosystem.
- When should I avoid sglang?
- Avoid using SGLang if your project relies exclusively on CPU-based inference, as it specifically optimizes for GPU architectures like CUDA. SGLang may not be suitable for scenarios where the primary model focus is reinforcement learning (RL), given its specific strengths in LLM and multimodal model serving. If you need a broader range of features beyond solely inference speed and efficiency for large language models, SGLang's specialized capabilities might not address all your needs.
- Is paddler or sglang more popular on GitHub?
- sglang has more GitHub stars (30,062 vs 1,627). Stars measure visibility, not whether either tool fits your constraints.
- Are paddler and sglang open source?
- Yes - both are open-source projects on GitHub (paddler: Apache-2.0, sglang: Apache-2.0).
- Where can I find alternatives to paddler or sglang?
- GraphCanon lists graph-backed alternatives at /tools/intentee-paddler/alternatives and /tools/sgl-project-sglang/alternatives (/tools/intentee-paddler/alternatives.md, /tools/sgl-project-sglang/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 /compare/intentee-paddler-vs-sgl-project-sglang.md mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers.
- Which is better maintained, paddler or sglang?
- paddler: Very active. sglang: Very 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 paddler and sglang?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: paddler: /tools/intentee-paddler/trust; sglang: /tools/sgl-project-sglang/trust.