How to Choose the Right LLM for Your Startup
Technology
•
May 31, 2025


If you’re building with AI, you’ll face the decision of which large language model (LLM) to use. The options seem endless: OpenAI’s GPT-4, Anthropic’s Claude, Meta’s Llama, Google’s Gemini, Mistral, and dozens more, with new releases nearly every month. Each promises breakthroughs in speed, accuracy, privacy, or price. So, how should a startup make the right call—especially if you don’t have an in-house AI team?
First, understand that the “best” LLM depends less on benchmarks and more on your use case, team skills, and budget. A recent report from McKinsey points out that 67% of companies using generative AI choose their LLM based on how well it fits the business problem—not just raw power. For example, customer support chatbots might need high reliability and low hallucination risk, while a creative marketing tool could benefit from more open-ended, “imaginative” models.
Next, consider the difference between closed and open models. Closed-source LLMs like OpenAI’s GPT-4 and Anthropic’s Claude offer impressive performance and reliability, but you rely on the provider’s infrastructure and accept their guardrails. Forbes’ 2024 GenAI guide notes that these are ideal for companies wanting rapid time to market and easy scaling, especially if privacy is managed through provider agreements.
Open-source models such as Meta’s Llama 3 or Mistral give you far more control, privacy, and flexibility, often at a lower long-term cost. Gartner predicts that by 2027, half of all organizations will use open-source LLMs in production, especially those in regulated industries like healthcare and finance. Open models are usually best for startups that need to customize the model, deploy on-premises, or have strict data requirements.
Don’t overlook cost and infrastructure. Some models charge by token, others by hour or subscription, and some can run on your own servers for a one-time cost. For early-stage startups, Sequoia Capital recommends starting with pay-as-you-go APIs to test ideas, then moving to open models as you scale and better understand your usage patterns.
Finally, check for ecosystem and support. Closed models tend to have more plug-and-play integrations and documentation, making it easier to prototype. Open models offer a fast-growing community—huggingface.co, for example, hosts thousands of pre-trained LLMs and tools with active forums for troubleshooting and improvement.
The best advice? Prototype quickly, measure real-world results, and don’t be afraid to switch. Leading startups like Notion, Jasper, and Zapier all report switching or mixing LLMs as they scale.
Choosing an LLM is not a once-and-done decision—it’s a series of tradeoffs that changes as your product grows. Focus on your workflow, user data, and roadmap. The right model is the one that gets your users what they need, faster and more reliably than before.
References:
Related insights
How to Choose the Right LLM for Your Startup
Technology
•
May 31, 2025

If you’re building with AI, you’ll face the decision of which large language model (LLM) to use. The options seem endless: OpenAI’s GPT-4, Anthropic’s Claude, Meta’s Llama, Google’s Gemini, Mistral, and dozens more, with new releases nearly every month. Each promises breakthroughs in speed, accuracy, privacy, or price. So, how should a startup make the right call—especially if you don’t have an in-house AI team?
First, understand that the “best” LLM depends less on benchmarks and more on your use case, team skills, and budget. A recent report from McKinsey points out that 67% of companies using generative AI choose their LLM based on how well it fits the business problem—not just raw power. For example, customer support chatbots might need high reliability and low hallucination risk, while a creative marketing tool could benefit from more open-ended, “imaginative” models.
Next, consider the difference between closed and open models. Closed-source LLMs like OpenAI’s GPT-4 and Anthropic’s Claude offer impressive performance and reliability, but you rely on the provider’s infrastructure and accept their guardrails. Forbes’ 2024 GenAI guide notes that these are ideal for companies wanting rapid time to market and easy scaling, especially if privacy is managed through provider agreements.
Open-source models such as Meta’s Llama 3 or Mistral give you far more control, privacy, and flexibility, often at a lower long-term cost. Gartner predicts that by 2027, half of all organizations will use open-source LLMs in production, especially those in regulated industries like healthcare and finance. Open models are usually best for startups that need to customize the model, deploy on-premises, or have strict data requirements.
Don’t overlook cost and infrastructure. Some models charge by token, others by hour or subscription, and some can run on your own servers for a one-time cost. For early-stage startups, Sequoia Capital recommends starting with pay-as-you-go APIs to test ideas, then moving to open models as you scale and better understand your usage patterns.
Finally, check for ecosystem and support. Closed models tend to have more plug-and-play integrations and documentation, making it easier to prototype. Open models offer a fast-growing community—huggingface.co, for example, hosts thousands of pre-trained LLMs and tools with active forums for troubleshooting and improvement.
The best advice? Prototype quickly, measure real-world results, and don’t be afraid to switch. Leading startups like Notion, Jasper, and Zapier all report switching or mixing LLMs as they scale.
Choosing an LLM is not a once-and-done decision—it’s a series of tradeoffs that changes as your product grows. Focus on your workflow, user data, and roadmap. The right model is the one that gets your users what they need, faster and more reliably than before.
References: