Best AI Development Platforms for DeepSeek
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Compare the Top AI Development Platforms that integrate with DeepSeek as of June 2026
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This a list of AI Development platforms that integrate with DeepSeek. Use the filters on the left to add additional filters for products that have integrations with DeepSeek. View the products that work with DeepSeek in the table below.
What are AI Development Platforms for DeepSeek?
AI development platforms are tools that enable developers to build, manage, and deploy AI applications. These platforms provide the necessary infrastructure for the development of AI models, such as access to data sets and computing resources. They can also help facilitate the integration of data sources or be used to create workflows for managing machine learning algorithms. Finally, these platforms provide an environment for deploying models into production systems so they can be used by end users. Compare and read user reviews of the best AI Development platforms for DeepSeek currently available using the table below. This list is updated regularly.
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1
LM-Kit.NET
LM-Kit
With minimal setup, developers can add advanced generative AI to .NET projects for chatbots, text generation, content retrieval, natural language processing, translation, and structured data extraction, while on-device inference uses hybrid CPU and GPU acceleration for rapid local processing that protects data, and frequent updates fold in the latest research so teams can build secure, high-performance AI applications with streamlined development and full control.Starting Price: Free (Community) or $1000/year -
2
Ollama
Ollama
Ollama is an innovative platform that focuses on providing AI-powered tools and services, designed to make it easier for users to interact with and build AI-driven applications. Run AI models locally. By offering a range of solutions, including natural language processing models and customizable AI features, Ollama empowers developers, businesses, and organizations to integrate advanced machine learning technologies into their workflows. With an emphasis on usability and accessibility, Ollama strives to simplify the process of working with AI, making it an appealing option for those looking to harness the potential of artificial intelligence in their projects.Starting Price: Free -
3
Oumi
Oumi
Oumi is a fully open source platform that streamlines the entire lifecycle of foundation models, from data preparation and training to evaluation and deployment. It supports training and fine-tuning models ranging from 10 million to 405 billion parameters using state-of-the-art techniques such as SFT, LoRA, QLoRA, and DPO. The platform accommodates both text and multimodal models, including architectures like Llama, DeepSeek, Qwen, and Phi. Oumi offers tools for data synthesis and curation, enabling users to generate and manage training datasets effectively. For deployment, it integrates with popular inference engines like vLLM and SGLang, ensuring efficient model serving. The platform also provides comprehensive evaluation capabilities across standard benchmarks to assess model performance. Designed for flexibility, Oumi can run on various environments, from local laptops to cloud infrastructures such as AWS, Azure, GCP, and Lambda.Starting Price: Free -
4
TensorBlock
TensorBlock
TensorBlock is an open source AI infrastructure platform designed to democratize access to large language models through two complementary components. It has a self-hosted, privacy-first API gateway that unifies connections to any LLM provider under a single, OpenAI-compatible endpoint, with encrypted key management, dynamic model routing, usage analytics, and cost-optimized orchestration. TensorBlock Studio delivers a lightweight, developer-friendly multi-LLM interaction workspace featuring a plugin-based UI, extensible prompt workflows, real-time conversation history, and integrated natural-language APIs for seamless prompt engineering and model comparison. Built on a modular, scalable architecture and guided by principles of openness, composability, and fairness, TensorBlock enables organizations to experiment, deploy, and manage AI agents with full control and minimal infrastructure overhead.Starting Price: Free -
5
Disco.dev
Disco.dev
Disco.dev is an open source personal hub for MCP (Model Context Protocol) integration that lets users discover, launch, customize, and remix MCP servers with zero setup, no infrastructure overhead required. It provides plug‑and‑play connectors and a collaborative environment where users can spin up servers instantly via CLI or local execution, explore and remix community‑shared servers, and tailor them to unique workflows. This streamlined, infrastructure‑free approach accelerates AI automation development, democratizes access to agentic tooling, and fosters open collaboration across technical and non-technical contributors through a modular, remixable ecosystem.Starting Price: Free -
6
FastbuildAI
FastbuildAI
FastbuildAI is an open source, self-hosted framework designed to empower AI developers and entrepreneurs to rapidly build and deploy full-stack AI applications with a commercial-ready setup. The platform provides a visual “DIY” interface that requires minimal coding, bundled tools for managing user authentication, subscription billing, usage metering, and payment integration, and a plugin engine that lets users extend functionality (e.g., chatbots, agent workflows, custom APIs, multi-modal features). It supports rapid deployment via Docker and offers flexible infrastructure (on-premises or cloud), allowing full control of branding, data, and monetization. With FastbuildAI, you can turn an AI concept into a live SaaS product in minutes, complete with GUI, plugin architecture, monetization tiering, and self-hosted operations. The framework is geared to support both technical users who want to customize flows and non-technical users who want to launch an AI-led business.Starting Price: Free -
7
Vertesia
Vertesia
Vertesia is a unified, low-code generative AI platform that enables enterprise teams to rapidly build, deploy, and operate GenAI applications and agents at scale. Designed for both business professionals and IT specialists, Vertesia offers a frictionless development experience, allowing users to go from prototype to production without extensive timelines or heavy infrastructure. It supports multiple generative AI models from leading inference providers, providing flexibility and preventing vendor lock-in. Vertesia's agentic retrieval-augmented generation (RAG) pipeline enhances generative AI accuracy and performance by automating and accelerating content preparation, including intelligent document processing and semantic chunking. With enterprise-grade security, SOC2 compliance, and support for leading cloud infrastructures like AWS, GCP, and Azure, Vertesia ensures secure and scalable deployments. -
8
NexaSDK
NexaSDK
Nexa SDK is a unified developer toolkit that lets you run and ship any AI model locally on virtually any device with support for NPUs, GPUs, and CPUs, offering seamless deployment without needing cloud connectivity; it provides a fast command-line interface, Python bindings, mobile (Android and iOS) SDKs, and Linux support so you can integrate AI into apps, IoT devices, automotive systems, and desktops with minimal setup and one line of code to run models, while also exposing an OpenAI-compatible REST API and function calling for easy integration with existing clients. Powered by the company’s custom NexaML inference engine built from the kernel up for optimal performance on every hardware stack, the SDK supports multiple model formats including GGUF, MLX, and Nexa’s proprietary format, delivers full multimodal support for text, image, and audio tasks (including embeddings, reranking, speech recognition, and text-to-speech), and prioritizes Day-0 support for the latest architectures.
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