DeepSeek’s meteoric rise has hit significant turbulence as US-based AI startups struggle to reliably access the Chinese company’s AI models. Despite the initial excitement over DeepSeek’s cost-effective alternative to Western AI models, businesses are encountering severe technical and logistical challenges that threaten to undermine the platform’s promise.
Neal Shah, CEO of Counterforce Health, revealed his company has cycled through seven different API providers attempting to access DeepSeek’s models consistently. Counterforce uses AI to generate responses to insurance claim denials, and the company desperately needs low-cost solutions since its service is currently free for individuals and in pilot testing with healthcare providers. After struggling with six providers that were either too slow or unreliable, Shah’s team finally found relative stability with Fireworks AI, though even this provider operates at only half the speed of DeepSeek’s native Chinese API.
Performance data from Artificial Analysis paints a concerning picture: most cloud providers running DeepSeek models operate at just one-third the speed of DeepSeek’s own API. This creates a significant dilemma for businesses concerned about data security—many prefer using US-based providers rather than sharing information directly with a Chinese API, but these intermediaries can’t deliver adequate performance.
Theo Browne of Ping, who develops AI tools for software developers, experienced similar frustrations. After testing DeepSeek’s V3 model in December and finding results comparable to Anthropic’s Claude at one-fifteenth the price, Browne watched access options deteriorate when mainstream attention arrived in mid-January. “It’s taking 100 times longer to generate a response than any traditional model provider,” he reported. The situation worsened when DeepSeek’s China-hosted API crashed on January 26 following what the company described as a malicious attack, and full functionality has yet to be restored. On February 6, DeepSeek suspended new API credits citing “resource constraints.”
Security concerns add another layer of complexity. Pukar Hamal, CEO of Security Pal, warns that relying on Chinese AI models could become a liability when startups attempt to sell to enterprise customers. “The moment a startup wants to sell to an enterprise, an enterprise wants to know what your exact data architecture system looks like. If they see you’re heavily relying on a Chinese-made LLM, ain’t no way you’re gonna be able to sell it,” Hamal stated.
Despite these challenges, demand remains intense. Hyperbolic saw inference users increase 150% after launching DeepSeek models, while Fireworks reported a 400% month-over-month increase in January users. The irresistible pricing continues to attract startups willing to navigate the technical obstacles and security considerations.
Key Quotes
We’re on our seventh provider
Neal Shah, CEO of Counterforce Health, describing the difficulty his healthcare AI startup has experienced trying to find reliable access to DeepSeek’s models through various API providers.
It’s taking 100 times longer to generate a response than any traditional model provider
Theo Browne of Ping explained the severe performance degradation experienced when trying to access DeepSeek through US-based cloud providers after mainstream attention increased demand in mid-January.
The moment a startup wants to sell to an enterprise, an enterprise wants to know what your exact data architecture system looks like. If they see you’re heavily relying on a Chinese-made LLM, ain’t no way you’re gonna be able to sell it
Pukar Hamal, CEO of Security Pal, warning about the enterprise sales liability that comes with building products on Chinese AI models, highlighting a significant barrier to DeepSeek adoption beyond technical issues.
After we launched the new DeepSeek model, we saw inference users increase by 150%
Jasper Zhang, cofounder and CEO of cloud service Hyperbolic, demonstrating the intense demand for DeepSeek access despite the technical challenges, showing that cost-effective AI remains highly attractive to developers.
Our Take
DeepSeek’s stumble reveals a harsh reality: disruptive pricing alone cannot overcome infrastructure limitations and geopolitical friction. The company’s technical achievement in creating cost-effective AI models is genuine, but the delivery ecosystem—cloud providers, API infrastructure, and computing capacity—wasn’t prepared for sudden mainstream demand. This creates a fascinating paradox where the most affordable AI solution becomes inaccessible precisely when it’s needed most. The security concerns raised by enterprise-focused companies suggest a more permanent challenge: even if technical issues resolve, Chinese AI models may face persistent market resistance in Western enterprise contexts. However, the 150-400% user growth reported by cloud providers indicates that for early-stage startups and individual developers, the cost advantages remain compelling enough to tolerate significant friction. The real test will be whether DeepSeek can scale its infrastructure and whether US cloud providers can optimize performance—or whether this window allows competitors like Meta’s Llama or other open-source alternatives to capture the market DeepSeek briefly opened.
Why This Matters
DeepSeek’s accessibility crisis represents a critical inflection point for the AI industry’s competitive landscape and the viability of cost-effective alternatives to dominant Western AI providers. The situation exposes fundamental infrastructure challenges in the global AI ecosystem—even when breakthrough technology emerges at dramatically lower costs, delivery mechanisms, geopolitical concerns, and technical capacity constraints can prevent widespread adoption.
For AI startups operating on tight budgets, DeepSeek promised democratized access to powerful models at a fraction of traditional costs. The current struggles reveal how dependent the AI industry remains on reliable infrastructure and raises questions about whether open-source alternatives can truly challenge established players like OpenAI, Anthropic, and Google when scaling issues emerge.
The security and compliance concerns highlighted by enterprise-focused companies signal a potential permanent barrier for Chinese AI models in Western markets, regardless of technical capabilities or pricing advantages. This could reinforce the bifurcation of global AI ecosystems along geopolitical lines, limiting competition and innovation. The window created by DeepSeek’s access problems also provides an opportunity for other open-source models to capture market share, potentially reshaping the competitive dynamics that DeepSeek briefly disrupted.
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Source: https://www.businessinsider.com/deepseek-switch-ai-startups-struggle-access-chinese-models-2025-2