
LiteLLM一个跨100+LLM(使用统一OpenAI格式)提供API、SDK和代理服务器,进行身份验证、负载均衡以及支出追踪的开源Python SDK和代理服务器。
概览
主要功能
- 统一的OpenAI格式针对100+LLM
- OpenAI客户端的直接替代方案
- 路由器(带有重发、降低重发和负载均衡)
- 可观察性回调(Langfuse,MLflow,Helicone)
- 虚拟密钥(以按键/团队/用户为单位预算)
- 集中日志,守护护栏和缓存
价格
- 模型
- Freemium
- 评分
- 4.6 / 5 (5)
使用场景
统一多个LLM的集成
通过单个OpenAI兼容的接口调用100+LLM提供商,简化代码并使可快速在模型之间切换而无需重写接入。
LLM提供程序之间的负载均衡
将请求分布在多个LLM端点来提高可靠性和性能,自动对LLM提供商出现宕机或率-limit状态时进行故障转移。
集中式身份验证管理
从一个代理服务器中管理API密钥和身份验证,减少credential扩散并简化访问控制
LLM支出追踪
在一个地方监视和跟踪不同LLM提供商的支出,帮助团队控制开支并分析使用模式
优点 & 缺点
优点
- 统一的接口支持多个LLM
- 简化的身份验证和支出追踪
- 支持可观察性和日志工具
- 自主托管代理服务器为可定制部署
- OpenAI客户端的直接替代方案
缺点
- 需要Python编程经验才能集成
- 可能需要额外的设置以便于具体的LLM或业务场景
- 在管理和监视大规模部署时复杂
评测
5 个评分的平均值。
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Use it every day
Honestly didn't expect to like it this much. The dashboard is exactly what I needed, and it is genuinely easy to set up. I do wish pricing gets steep at scale, but I reach for it almost every day now and it just clicks.
Does the job
Pretty happy overall. The dashboard just works and it saves real time. but no dealbreakers — I'd recommend it to a friend without hesitating.
Skeptical, then convinced
I went in skeptical — most tools in this space overpromise. It actually delivers on the onboarding, and support is responsive caught me off guard. still, I'd recommend giving it a real trial.
Solid for our team
We rolled this out across the team last quarter and it saves real time. The automation fits neatly into how we already work, and the onboarding removed a step we used to do by hand. The mobile experience lags, which is the main caveat, but it has held up under daily use.
Years in this space
I've evaluated a lot of these over the years. What stands out here is the API — handled better than most — and it is genuinely easy to set up. Worth the time if this is your use case.
问答
How do I get day-0 support for new models without restarting?
Use Auto Sync New Models to pull the latest pricing and context-window data from GitHub on demand or on a schedule — no restart required. Trigger a manual sync with POST /reload/model_cost_map, or schedule periodic syncs with POST /schedule/model_cost_map_reload?hours=6.
Asked by Gabriel Duarte · Feb 1, 2026
How is pricing structured?
Pricing is based on usage. Contact us for a quote tailored to your team.
Asked by Fiorella Bianchi · Feb 1, 2026
Where can I read more about data security and compliance?
See Data Security / Legal / Compliance FAQs.
Asked by Zara Ahmed · Jan 17, 2026
How do I set up and verify an Enterprise License?
Add the license key to your environment: LITELLM_LICENSE="eyJ..." Restart LiteLLM Proxy. Open http://<your-proxy-host>:<port>/ — the Swagger page should show "Enterprise Edition" in the description. If it doesn't, confirm the key is correct, unexpired, and that the proxy was fully restarted.
Asked by George Papadakis · Dec 11, 2025
Why Enterprise?
LiteLLM OSS already covers the fundamentals — an OpenAI-compatible gateway, virtual keys, spend tracking, budgets, fallbacks, and request/response logging. Enterprise adds the controls larger organizations need to safely give hundreds of users and dozens of applications access to LLMs. OSSEnterpriseAuthAPI keysSSO + SCIM, OIDC/JWTKey ManagementVirtual keys, users, teams across LLM APIs, MCPs, and AgentsOrganizations, org/team admins, delegated admin rolesSecurity—Key rotations, read/write to secret managerGuardrailsAlways-on / request-based1Key and team scoped guardrailsLoggingRequest/response logging, Prometheus metricsPer-key / per-team routing to Langfuse, Langsmith, Arize and more. Management-op logsDeploymentSingle-region proxyMulti-region deployment under one license, admin/worker split 1 The OSS guardrail framework supports custom guardrails plus Presidio (PII masking). Several built-in callback integrations — including llmguard_moderations, llamaguard_moderations, hide_secrets, openai_moderations, google_text_moderation, lakera_prompt_injection, and aporia_prompt_injection — require a LiteLLM Enterprise license.
Asked by Ahmed Saleh · Nov 25, 2025
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