概览
主要功能
- 聚合的 API 目录
- 统一的 API Key 与访问管理
- 集中式使用量与计费
- 标准化的请求格式
- 开发者文档和示例
- 支持多种服务类别
价格
- 模型
- Free
- 分类
- 模型服务
- 评分
- 5.0 / 5 (5)
使用场景
快速使用多个 API 原型化 AI 应用
开发者只需一次认证，即可从众多 API 中获取数据，快速验证创意，无需为每个供应商单独对接或管理多个 SDK 与凭证。
简化初创公司的供应商管理
初创公司可将 API 凭证、使用量追踪和计费集中在一个平台，降低与众多外部服务供应商合作时的运营成本。
轻松比较并切换 API 提供商
产品团队利用标准化请求格式评估竞争供应商，并可在供应商之间切换，而无需为每个供应商重写集成代码。
利用多样化数据源驱动自动化
自动化构建者通过单一集成点将工作流连接到多个服务类别，简化外部数据和功能的调用流程。
优点 & 缺点
优点
- 一次集成即可使用众多 API
- 统一的认证与计费
- 更快速的原型开发和上手
- 更便捷地切换或比较供应商
缺点
- 依赖于市场可用性
- 可能在原生 API 之上增加抽象层
- 定价可能与直接供应商的费率不同
- 覆盖范围仅限于已列出的 API
对决战绩
在万神殿中参与了 6 对决。
Last 5 battles
- #2
Model Serving Showdown — January 28, 2026
Jan 28, 2026 · #2 of 8
- #1
Model Serving Showdown — July 4, 2025
Jul 4, 2025 · #1 of 3
- #6
Model Serving Showdown — January 20, 2025
Jan 20, 2025 · #6 of 6
- #1
Model Serving Showdown — December 1, 2024
Dec 1, 2024 · #1 of 2
- #4
Model Serving Showdown — June 13, 2024
Jun 13, 2024 · #4 of 7
评测
5 个评分的平均值。
登录以留下评测。
Does the job
Pretty happy overall. Unified API key and access management just works and unified authentication and billing. May add a layer of abstraction over native APIs can be annoying, but no dealbreakers — I'd recommend it to a friend without hesitating.
Does the job
Pretty happy overall. Support for multiple service categories just works and faster prototyping and onboarding. 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 developer documentation and examples, and faster prototyping and onboarding caught me off guard. Dependent on marketplace availability is why this isn't a perfect score, still, I'd recommend giving it a real trial.
Compared a few options
Evaluated this against two competitors. Where it wins: support for multiple service categories and faster prototyping and onboarding. On balance the feature set — especially standardized request format — justifies the 5 stars for our use case.
Years in this space
I've evaluated a lot of these over the years. What stands out here is aggregated API catalog — handled better than most — and faster prototyping and onboarding. Worth the time if this is your use case.
问答
Is ApiPass ready for production?
Yes. ApiPass is built for production workloads with high-availability infrastructure, task status queries, request logs, callback support for async jobs, and enterprise support options for larger teams and higher-volume workloads.
Asked by Miriam Cohen · May 8, 2026
Can I test ApiPass before I top up?
Yes. New users can get trial credits and test real models in the Playground before moving into paid usage. Docs, example requests, and response notes are included so you can validate parameters, callbacks, and output formats before shipping.
Asked by Malik Rasheed · May 8, 2026
How does billing work?
ApiPass uses pay-as-you-go credits. Your balance is charged based on actual API usage, and model pages show the billing unit for each API. You can also review usage and logs in the dashboard to keep costs visible as you scale.
Asked by Lior Ben-David · May 4, 2026
How much can I save with ApiPass?
Pricing depends on the model and route, but many ApiPass calls are priced 30%–70% below comparable official options. Each model page and the pricing catalog show current billing units and the latest model-specific prices.
Asked by Cristina Moreno · Apr 30, 2026
Which models can I access through ApiPass?
ApiPass supports popular models such as Nano Banana 2, Seedream 5 Lite, Kling AI, Google Veo 3.1, Gemini 3 Flash / Pro, Flux 2 Pro, Suno V5, MiniMax Music, Hailuo, Qwen Image, Grok Imagine, ElevenLabs, and more across text, image, video, and audio. You can explore the full model catalog on the homepage to view the latest supported models, parameters, and pricing.
Asked by Esther Adeyemi · Apr 21, 2026
提问
模型服务 的替代品
Fast360模型服务开源平台，用于对 PDF 转 Markdown 转换的 OCR 模型进行基准测试
完善的文档解析和索引平台，用于建立准确的RAG和代理工作流。
开源框架，快速构建和部署企业 AI 代理。
FloppyData模型服务高速住宅和移动代理，适用于网页抓取和数据采集
Groq模型服务专注于高性能AI推理解决方案，提供硬件和软件平台用于快速AI应用部署。
安全的云沙箱，运行 AI 生成代码与自主代理
桌面应用程序，用于在本地运行离线的 LLM，保留完全的数据隐私
