Rapidly Deploy ds2api: Full‑Stack Middleware Translating DeepSeek to OpenAI, Claude, and Google APIs

Problem addressed

AI developers encounter fragmented API formats: OpenAI, Claude, Google Gemini each require distinct request structures. Maintaining separate code paths or proxies is cumbersome when switching models.

Core approach

ds2api pretends to be a DeepSeek client, captures the request, then re‑emits it in the format expected by OpenAI, Claude, or Google APIs, allowing existing OpenAI‑centric code to call DeepSeek without modification.

Key features

Multi‑account rotation : configure multiple DeepSeek tokens; when a token’s quota is exhausted or rate‑limited, ds2api automatically switches to the next token.

Flexible deployment : pre‑compiled binaries for immediate use; Docker image for one‑click container deployment; native Vercel Serverless support enables running the gateway on Vercel’s free tier without managing servers.

Quick start (≈10 minutes)

The project is written in Go but does not require a Go toolchain to run. Download the appropriate release binary and execute:

# Download the release for your OS
./ds2api --port 8080 --deepseek-token "YOUR_DEEPSEEK_TOKEN"

After launching, point any OpenAI SDK to http://localhost:8080/v1; all requests are translated and forwarded to DeepSeek.

Docker deployment:

docker run -d -p 8080:8080 -e DEEPSEEK_TOKEN=YOUR_TOKEN cjackwang/ds2api

Vercel deployment: fork the repository, import into Vercel, set the DEEPSEEK_TOKEN environment variable, and deploy.

"I deployed it to Vercel in five minutes, switched Cursor’s API endpoint to my local ds2api, and instantly accessed DeepSeek models."

Target users

Individual developers who want to experiment with DeepSeek without rewriting existing OpenAI code.

Small teams that need a unified API entry point and token quota management across multiple projects.

AI toolchain builders (e.g., integrating DeepSeek into Cursor, Copilot) that require a plug‑and‑play middleware layer.

Conclusion

ds2api demonstrates a trend toward API standardization in AI infrastructure: a single compatible gateway reduces the need to learn separate APIs for each model. Implemented in Go, it offers strong performance and lightweight deployment, making it a practical gateway for personal or small‑team AI services.

Project repository: github.com/CJackHwang/ds2api