AI Infrastructure Built for Algeria

One platform for AI inference, multimodal APIs, Cloud GPU & CPU compute, developer SDKs, and production infrastructure — billed in Algerian Dinar.

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DEVUP AI is an Algerian AI infrastructure platform combining a multi-model inference gateway with on-demand Cloud GPU and CPU compute.

Developers, startups, research teams, and businesses can access leading AI models, deploy high-performance compute instances, and manage their infrastructure through one unified platform with native billing in Algerian Dinar (DZD).

DEVUP AI provides:

• OpenAI-compatible APIs
• DEVUP-native multimodal APIs
• More than 170 AI models
• On-demand Cloud GPU instances
• Scalable Cloud CPU infrastructure
• Official developer SDKs
• API key and usage management
• Real-time billing and request telemetry
• Local payment access
• Infrastructure status and operational monitoring

Access more than 170 models across multiple AI modalities through one consistent infrastructure layer.

• Provisioned GPU capacity
• OpenAI-compatible request formats
• Unified authentication and billing
• Model-level usage and cost tracking
• Streaming responses
• Scoped API access
• Multimodal inference
• Local DZD payments
• No model training on customer inference data

Browse the Live Model Catalog →

Launch dedicated GPU instances for:

• Large language model inference
• Model training and fine-tuning
• LoRA and adapter training
• Image and video generation
• Computer vision
• 3D rendering
• Scientific computing
• GPU-accelerated development
• Custom containers and research workloads

Hardware availability and pricing depend on the selected region and live platform inventory.

View Available GPU Instances →

Deploy scalable CPU infrastructure for workloads that do not require dedicated GPU acceleration.

• Web applications and APIs
• Microservices
• AI agent orchestration
• Background workers
• Data processing
• Web scraping
• Automation
• CI/CD pipelines
• Databases and caching
• In-memory workloads
• Development environments
• Lightweight model serving

CPU instances provide configurable vCPU, memory, storage, and deployment options through the DEVUP AI dashboard.

Install the official SDK:

npm install devupai

Create a client and send your first request:

import DevupAI from "devupai";

const client = new DevupAI({
  apiKey: process.env.DEVUP_API_KEY!,
});

const response = await client.chat.completions.create({
  model: "deepseek-ai/DeepSeek-V4-Pro",
  messages: [
    {
      role: "system",
      content: "You are a concise technical assistant.",
    },
    {
      role: "user",
      content: "Explain artificial intelligence in one paragraph.",
    },
  ],
});

console.log(response.choices[0]?.message?.content);
console.log("Cost:", response._devup?.cost_dzd, "DZD");

curl https://api.devupai.com/v1/chat/completions \
  -H "Authorization: Bearer $DEVUP_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "deepseek-ai/DeepSeek-V4-Pro",
    "messages": [
      {
        "role": "user",
        "content": "Hello from Algeria!"
      }
    ],
    "max_tokens": 256
  }'

Configure your API key securely:

DEVUP_API_KEY=sk-devup-your-api-key

Never expose API keys in public repositories, screenshots, browser bundles, or client-side environment variables.

DEVUP AI is designed to integrate with modern AI application stacks.

The official SDK provides:

• Chat completions and streaming
• Image generation and editing
• Image proxy operations
• Embeddings
• Text-to-speech
• Speech transcription
• Video generation and editing
• Native model inference
• Model discovery
• Balance and health operations
• Vercel AI SDK integration
• TypeScript-first public contracts
• Zero runtime dependencies for the core REST client

View SDK Source · Install from npm

DEVUP AI is designed to process inference workloads without using customer content for model training.

• Prompts and completions are not used to train or fine-tune models.
• Inference content is processed inside isolated compute environments.
• Usage metadata such as timestamps, model, token counts, latency, and cost may be recorded for billing and platform operations.
• Usage metadata does not include prompt or completion content.
• Temporary in-memory caching may be used to improve response latency.
• Customer inference content is not written to persistent storage by the managed inference pipeline.

Read the full legal and infrastructure documentation before processing regulated or highly sensitive data.

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Build AI assistants, agents, RAG systems, search engines, voice applications, image pipelines, video products, automation platforms, research environments, and enterprise workloads on one unified infrastructure layer.

Built in Algeria. Connected to the global AI ecosystem.

Platform · Documentation · Models · Compute · Status

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