aisix vs inference

Common questions

What is the difference between aisix and inference?

aisix: Open-source AI gateway for LLMs & AI agents. inference: Unified production-ready inference API for various models. See the comparison table for live GitHub stats and shared categories.

When should I choose aisix over inference?

Choose aisix over inference when aisix is primarily Rust; inference is Python; Pricing: The open-source core (AISIX Gateway) can be used for free. For advanced features like team governance, budgets, and audit dashboard access, the AISIX Cloud is available on a managed SaaS model.; Requirements: Requires etcd running to store configurations; Self-hosted setup requires Docker Compose for local testing with example configuration files; Tags unique to aisix: ai-agents, llm-gateway, observability, openai-compatible; Also covers AI Agents; aisix ships Docker support for self-hosted deployment; When you need an OpenAI-compatible API that can route requests to multiple LLM providers like OpenAI, Anthropic, Gemini, and Bedrock.

When should I choose inference over aisix?

Choose inference over aisix when inference is primarily Python; aisix is Rust; Pricing: Primary core services offer under free Apache-2.0 license; advanced support might incur costs based on the deployment scale and environment complexity.; Requirements: Min 4 GB RAM; Requires Docker; Compatibility with Nvidia GPUs requires Docker, CUDA setup.; Tags unique to inference: artificial-intelligence, deployment, machine-learning; - When you need to deploy multiple types of models (like speech, text, and multimodal) through a single unified interface.

When should I avoid aisix?

If you require a fully managed service with minimal setup and configuration since AISIX requires self-hosting or connecting to AISIX Cloud for management features For projects preferring dynamic languages over statically compiled binaries, given that AISIX is built in Rust and ships as one static binary

When should I avoid inference?

- When strict control over individual model interfaces is required and a unified API complicates your workflow. - If you’re working with proprietary models that aren’t supported by Xinference’s built-in or custom integration mechanisms. - In cases where the project mandates use of specific deployment tools that are not well-aligned with Xinference’s recommended methods (e.g., Docker, Kubernetes), unless you can adapt your setup.

Is aisix or inference more popular on GitHub?

inference has more GitHub stars (9,470 vs 92). Stars measure visibility, not whether either tool fits your constraints.

Are aisix and inference open source?

Yes - both are open-source projects on GitHub (aisix: Apache-2.0, inference: Apache-2.0).

Where can I find alternatives to aisix or inference?

GraphCanon lists graph-backed alternatives at aisix alternatives and inference alternatives (aisix markdown twin, inference markdown twin), 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 mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers.

Which is better maintained, aisix or inference?

aisix: Very active. inference: 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 aisix and inference?

GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: aisix trust report; inference trust report.