OpenAI, Anthropic, Deepseek and other LLM Providers keep dropping prices: Should you host your own model?
Manouk
Feb 20, 2025
As OpenAI, Anthropic, and other large language model (LLM) providers continue to reduce prices while enhancing their models, many companies find themselves asking: "When does it actually make sense to host our own open-source model?"
If you’re asking this question, the short answer is: you should probably stick with a provider. Here’s why.
Why using a provider makes sense
1. State-of-the-Art models come with engineering you can’t teplicate easily
Modern LLMs aren’t just about the model weights anymore. Take OpenAI’s o1 model—it’s not just a neural network. It’s an ecosystem of advanced engineering, optimized reasoning, and tools that open-source alternatives like Llama 3.3 simply don’t offer out of the box. Recreating that infrastructure requires significant time, resources, and specialized expertise.
2. Data privacy concerns? Providers have you covered
Worried about sensitive data? Providers like AWS Bedrock offer solutions that keep your data secure while using hosted models. With options for encryption, private endpoints, and strict compliance standards, you can leverage powerful models without sacrificing privacy.
3. Cost-effectiveness & Scalability
While the idea of “saving” on model usage fees sounds tempting, self-hosting comes with hidden costs: infrastructure, ongoing maintenance, fine-tuning, and continuous optimization. Providers, on the other hand, offer scalable solutions that grow with your business—without the headache.
When does Self-Hosting make sense?
There are select scenarios where self-hosting an open-source model is the right move. Consider self-hosting if:
✅ You work in an air-gapped environment: Security protocols prevent any external data transfer.
✅ Strict regulations block external providers: Certain industries (like defense or government sectors) require absolute control over data and infrastructure.
✅ Your CISO mandates it: If internal policy demands it, self-hosting may be unavoidable.
Outside of these cases? The complexity and cost rarely justify going it alone.
Final Thoughts
Self-hosting a large language model sounds appealing—until you factor in the real-world challenges. Unless you’re dealing with strict regulatory demands or air-gapped environments, leveraging hosted solutions from providers like OpenAI, Anthropic, or AWS Bedrock is the smarter choice. You get cutting-edge technology, enhanced security, and scalability—without the operational burden.
Need guidance on making the right decision for your business? LangWatch.ai helps companies navigate LLM choices, ensuring you get the best balance of performance, privacy, and cost.
As OpenAI, Anthropic, and other large language model (LLM) providers continue to reduce prices while enhancing their models, many companies find themselves asking: "When does it actually make sense to host our own open-source model?"
If you’re asking this question, the short answer is: you should probably stick with a provider. Here’s why.
Why using a provider makes sense
1. State-of-the-Art models come with engineering you can’t teplicate easily
Modern LLMs aren’t just about the model weights anymore. Take OpenAI’s o1 model—it’s not just a neural network. It’s an ecosystem of advanced engineering, optimized reasoning, and tools that open-source alternatives like Llama 3.3 simply don’t offer out of the box. Recreating that infrastructure requires significant time, resources, and specialized expertise.
2. Data privacy concerns? Providers have you covered
Worried about sensitive data? Providers like AWS Bedrock offer solutions that keep your data secure while using hosted models. With options for encryption, private endpoints, and strict compliance standards, you can leverage powerful models without sacrificing privacy.
3. Cost-effectiveness & Scalability
While the idea of “saving” on model usage fees sounds tempting, self-hosting comes with hidden costs: infrastructure, ongoing maintenance, fine-tuning, and continuous optimization. Providers, on the other hand, offer scalable solutions that grow with your business—without the headache.
When does Self-Hosting make sense?
There are select scenarios where self-hosting an open-source model is the right move. Consider self-hosting if:
✅ You work in an air-gapped environment: Security protocols prevent any external data transfer.
✅ Strict regulations block external providers: Certain industries (like defense or government sectors) require absolute control over data and infrastructure.
✅ Your CISO mandates it: If internal policy demands it, self-hosting may be unavoidable.
Outside of these cases? The complexity and cost rarely justify going it alone.
Final Thoughts
Self-hosting a large language model sounds appealing—until you factor in the real-world challenges. Unless you’re dealing with strict regulatory demands or air-gapped environments, leveraging hosted solutions from providers like OpenAI, Anthropic, or AWS Bedrock is the smarter choice. You get cutting-edge technology, enhanced security, and scalability—without the operational burden.
Need guidance on making the right decision for your business? LangWatch.ai helps companies navigate LLM choices, ensuring you get the best balance of performance, privacy, and cost.
As OpenAI, Anthropic, and other large language model (LLM) providers continue to reduce prices while enhancing their models, many companies find themselves asking: "When does it actually make sense to host our own open-source model?"
If you’re asking this question, the short answer is: you should probably stick with a provider. Here’s why.
Why using a provider makes sense
1. State-of-the-Art models come with engineering you can’t teplicate easily
Modern LLMs aren’t just about the model weights anymore. Take OpenAI’s o1 model—it’s not just a neural network. It’s an ecosystem of advanced engineering, optimized reasoning, and tools that open-source alternatives like Llama 3.3 simply don’t offer out of the box. Recreating that infrastructure requires significant time, resources, and specialized expertise.
2. Data privacy concerns? Providers have you covered
Worried about sensitive data? Providers like AWS Bedrock offer solutions that keep your data secure while using hosted models. With options for encryption, private endpoints, and strict compliance standards, you can leverage powerful models without sacrificing privacy.
3. Cost-effectiveness & Scalability
While the idea of “saving” on model usage fees sounds tempting, self-hosting comes with hidden costs: infrastructure, ongoing maintenance, fine-tuning, and continuous optimization. Providers, on the other hand, offer scalable solutions that grow with your business—without the headache.
When does Self-Hosting make sense?
There are select scenarios where self-hosting an open-source model is the right move. Consider self-hosting if:
✅ You work in an air-gapped environment: Security protocols prevent any external data transfer.
✅ Strict regulations block external providers: Certain industries (like defense or government sectors) require absolute control over data and infrastructure.
✅ Your CISO mandates it: If internal policy demands it, self-hosting may be unavoidable.
Outside of these cases? The complexity and cost rarely justify going it alone.
Final Thoughts
Self-hosting a large language model sounds appealing—until you factor in the real-world challenges. Unless you’re dealing with strict regulatory demands or air-gapped environments, leveraging hosted solutions from providers like OpenAI, Anthropic, or AWS Bedrock is the smarter choice. You get cutting-edge technology, enhanced security, and scalability—without the operational burden.
Need guidance on making the right decision for your business? LangWatch.ai helps companies navigate LLM choices, ensuring you get the best balance of performance, privacy, and cost.