The AI landscape shifted dramatically in early 2025 when Chinese startup DeepSeek trained a ChatGPT-o1-level model for just $6 million, triggering a 12% drop in NVIDIA’s stock and igniting debates about the future of AI development.As businesses weigh open-source disruptors like DeepSeek against closed-source giants like ChatGPT, the stakes are higher than ever.
This comparison isn’t just about performance—it’s about scaling laws, data economics, and a brewing open-source revolution. With OpenAI’s Sam Altman admitting the company has been “on the wrong side of history” regarding open-source models, and former OpenAI Chief Scientist Ilya Sutskever declaring “LLM scaling has plateaued” at NeurIPS 2024, the rules of AI are being rewritten. Let’s explore which solution delivers the best value for teams in this new era.
2. Why DeepSeek Matters in 2025: Breaking the Scaling Plateau
2.1 The $6 Million Disruption
Despite the DeepSeek-R1 achieved 97.3% accuracy on MATH-500 (vs. ChatGPT o1’s 96.4%) and a Codeforces Elo score of 2,029 (surpassing 96% of human coders) at a fraction of traditional costs.
Key breakthroughs:
Cost Efficiency:
5.58M for compute+420K for data.
Open-Source Edge: Shared training code slashes inference costs to 1/20th of ChatGPT’s API fees.
Policy Backing: Founder Liang Wenfeng’s meeting with China’s Premier signals state-level support for circumventing U.S. chip sanctions.
2.2 The Scaling Law Crisis
Ilya Sutskever’s warning—“We have but one internet”—highlights the finite data problem. With high-quality training data exhausted, brute-force scaling is dead. DeepSeek’s innovation lies in reinforcement learning (RL)-driven efficiency, bypassing the need for massive datasets.
2.3 Open Source vs. Closed Source
DeepSeek: Embraces transparency, releasing model weights and training pipelines.
GRPO Breakthrough: DeepSeek’s Group Relative Policy Optimization eliminates the need for a critic model, using Monte Carlo sampling to compare response groups. Result? Faster convergence and 71% accuracy on AIME 2024 without human intervention. ChatGPT’s Achilles’ Heel: Human feedback loops introduce bias—e.g., over-politeness filters that cripple technical accuracy.
4. Enterprise Reality Check: 5 Metrics for Small Teams
For small businesses, choosing the right AI model involves more than raw performance. Factors like cost, stability, compliance, and integrations play a crucial role.
4.1 Cost & Pricing
DeepSeek: Open-source but requires private deployment with 10x A100 GPUs ($500K/year).
ChatGPT: Subscription model at $25/user/month plus $0.03 per 1,000 tokens.
Tanka: Free, no GPU or API fees.
4.2 Service Stability
DeepSeek: Suffered 7.2% downtime in Q1 2025 due to traffic surges.
ChatGPT Team: 99.9% SLA, but VPN-dependent in China.
4.4 How to deploy Deepseek and ChatGPT with Memory for small teams
Before introducing Tanka, it’s important to understand the basic steps and common challenges when deploying your own AI solution using DeepSeek’s GitHub code or ChatGPT’s API with Retrieval Augmented Generation (RAG).Deploying DeepSeek from GitHubKey Steps:
Environment Setup:
Ensure you have a Linux-based server with sufficient GPU capacity (e.g., A100/H100 GPUs).
Install essential dependencies such as Python and Docker. Refer to the project documentation for specific software requirements.
Code Acquisition and Configuration:
Clone the DeepSeek GitHub repository to your local environment.
Configure environment variables, data paths, and training parameters as outlined in the repository’s documentation.
Training and Inference:
Launch the training process and adjust hyperparameters or data preprocessing as needed.
Once training is complete, deploy the model via a REST API or other service interfaces to integrate with internal systems.
Monitoring and Tuning:
Monitor resource usage and response times. Adjust batch sizes, parallelism, or deployment strategies if necessary.
Log issues for troubleshooting and continuous improvement.
Common Challenges:
Hardware Limitations: Small teams might struggle with limited GPU resources, causing slow training or inference.
Dependency and Environment Management: Variations in library versions and configurations may lead to runtime errors, requiring debugging expertise.
System Stability: Handling traffic spikes may lead to downtime or system crashes, necessitating load balancing and pre-planned scaling strategies.
Data Privacy and Security: Manual configuration of data encryption and access control can increase the management overhead.
Building an Enterprise Knowledge Base with ChatGPT’s API and RAGKey Steps:
API Access Setup:
Register on the OpenAI platform and obtain your API key.
Install the OpenAI Python client library (pip install openai).
Data Collection and Preprocessing:
Compile internal documents (emails, meeting transcripts, internal wiki pages) and preprocess them for text analysis.
Convert documents into vector embeddings using OpenAI’s embedding models.
Creating a Vector Database:
Choose a vector database solution (e.g., FAISS, Pinecone, or Weaviate) and index the document embeddings.
Set up a query mechanism to quickly retrieve relevant documents based on similarity.
Develop a retrieval module that searches the vector database for the most relevant documents given a user query.
Combine the retrieved context with the query and call the ChatGPT API to generate a contextualized answer.
Integration and Automation:
Integrate this pipeline with internal communication tools (e.g., Slack or Teams) to allow real-time Q&A.
Regularly update the vector database to keep the knowledge base current.
Common Challenges:
Data Maintenance: Enterprise data evolves constantly, making it necessary to update the vector database regularly.
Retrieval Accuracy: Setting the right similarity thresholds to balance recall and precision is a technical challenge.
Cost Management: API usage fees can add up, especially under high query volumes.
Integration Complexity: Combining various components (API calls, database queries, error handling) requires additional development effort.
5. Tanka: The Game-Changer with AI Long-Term Memory
While DeepSeek and ChatGPT are great AI tools, they lack long-term memory that understands organizational structure, goals, and needs. This is where Tanka stands out. The AI landscape is rapidly evolving, with various players introducing innovative memory solutions to enhance long-term retention and contextual awareness.
While DeepSeek and similar solutions provide incremental improvements in AI memory, Tanka's proprietary MemTech offers a fundamentally superior approach. Here’s how Tanka differentiates itself and delivers unparalleled value:
5.1 Comprehensive Memory Graph Technology
Tanka long-term memory architecture
Unlike traditional memory models that rely on simplistic recall mechanisms, Tanka employs a Memory Graph architecture. This graph-based approach ensures:
Multi-layered Contextual Understanding: Data is not stored in isolation but interconnected within a semantic memory network, improving AI reasoning capabilities.
Efficient Storage & Retrieval: By leveraging structured indexing and vectorized search, Tanka ensures rapid access to relevant historical data, reducing redundancy and improving response accuracy.
5.2 Advanced Long-Term Memory (LTM) Capabilities
Many AI models struggle with context amnesia, where past interactions are lost once a session ends. Tanka’s LTM technology provides:
Persistent Context Retention: Unlike DeepSeek, which still operates within a limited context window, Tanka retains and updates knowledge dynamically across multiple sessions.
Adaptive Learning Mechanisms: Through incremental reinforcement, Tanka refines its memory to improve personalization over time.
5.3 Multi-Agent Collaboration & Context-Aware AI
Tanka’s OMNE Multi-Agent Framework ranks first on the GAIA benchmark, demonstrating its capability to handle:
Parallel Task Processing: Unlike DeepSeek, which processes memory sequentially, Tanka optimizes workflows by distributing memory tasks across its AI network.
5.4 Personalized & Extensible AI Memory
Tanka goes beyond static memory retention by implementing:
User-Centric Memory Structuring: Each user's interactions contribute to a tailored memory graph that evolves with their preferences and behavior.
Cross-Platform Memory Integration: Tanka seamlessly connects with enterprise tools such as WhatsApp, Slack, Outlook, and Notion, ensuring continuity across business workflows.
5.5 Secure, Enterprise-Ready Memory System
Security is paramount when handling long-term AI memory. Tanka differentiates itself by offering:
Zero Data Retention by Third-Party LLMs: Unlike many competitors, Tanka does not expose user data to external AI models for training.
Granular Access Control & Compliance: Full compliance with SOC2 ensures enterprise-grade security for organizational AI memory.
5.6 AI with Self-Evolution
DeepSeek and other memory-enhanced AI solutions still rely on predefined structures. Tanka, however, is designed for continuous self-evolution:
Memory-Driven AI Decision Systems: With ongoing reinforcement, Tanka autonomously refines its decision-making process, reducing the need for manual interventions.
Scalable for Enterprise and Personal Use: Tanka’s modular architecture allows for seamless scaling, making it an ideal choice for both individuals and large organizations.
Conclusion: Skip the Hype, Start with Tanka
While DeepSeek’s $6M breakthrough redefines AI economics and ChatGPT’s API with RAG provides powerful retrieval-augmented generation, the real value for small businesses lies in seamless integration and cost-effective deployment. Tanka offers:
No GPU Dependency: Deploy AI instantly through existing chat tools without heavy hardware investments.
Low-Cost, Ready-to-Use Solution: Enjoy a free tier for small teams without the need for extensive fine-tuning or development.
Enterprise-Grade Compliance and Long-Term Memory: Benefit from a system that continuously adapts to your business, backed by industry-leading memory capabilities and rigorous security standards.
