Say you want to use Llama 3.3 70b Instruct. You could:
- Use it via an API. OpenRouter offers it at ~12 cents / MTok. Azure offers it at 71 cents. Your price may vary.
- Self-host it on a major cloud provider. Azure offers A100 80GB at ~$3.67 / hour. In a day, you could generate ~0.5-1M tokens.
- Self-host it on an emerging cloud provider. Lambda Labs offers A100 80GB at ~$1.79 / hour. Again, ~0.5-1M tokens a day.
Clearly, self-hosting is cheaper if you run it continuously. Let’s say we run for 1 million tokens every day. Then:
- APIs cost 12 – 71 cents
- Azure costs $3.67 x 24 = $88
- Lamba Labs costs $1.79 x 24 = $43
So, the API is between 60 – 700 times cheaper than running it yourself. 60 times cheaper if you move from Lambda Labs to Azure AI Foundry. 700 times cheaper if you move from Azure servers to OpenRouter.
Not 60-700% cheaper. 60 – 700 times cheaper. Instead of spending a million dollars, you can get away with $1,500 – $15,000.
But what if…
- What if you need higher scale? APIs typically offer better scalability than most organizations can configure.
- What if you need higher uptime? Again, APIs typically offer higher uptime than most organizations can handle.
- What if you need lower latency? Check the throughputs. APIs are typically much faster throughput than self-hosting.
- What if you need edge computing? Then you’re GPU is one-time cost. My comparison is irrelevant.
So, are there any reasons to self-host? I’ve seen only a few.
- Fine-tuned models. No one else offers an API version of your model.
- Ultra-high security. You can’t trust even Microsoft, Google, or Amazon with your data. Your chats must remain in your own data center. (In this case, you probably run your own email and cloud services instead of Office 365 or Google Workspace.)
- Learning. You’re curious about what it takes to self-host these models or want to build this skill.
If you don’t have one of the above needs, remember: your GPU costs can shrink from $1,000K to $1.5 – $15K.
Source: OpenAI Deep Research.
I asked ChatGPT: Other than extraordinary scale, what other possible reasons will make self-hosting the right choice for the customers, like customization, personalization, security, on-time support for unexpected failures, etc.?
The answer as below
Great follow-up. While cost is often the primary consideration, there are several compelling reasons why self-hosting a language model might be the better choice for some customers—even at small to moderate scales. Here’s a breakdown:
🛠 1. Customization & Fine-Tuning
- You can fine-tune or RAG-augment open models on your domain-specific data. - Adjust the tokenizer, prompt structure, or system instructions in ways not possible on shared APIs. - Example: A legal-tech company might self-host Llama 3.3 fine-tuned on case law to improve legal document reasoning.
👤 2. Personalization & Memory
- Full control over session memory and user data enables long-term personalization. - API services might limit how much context or history you can store, or restrict memory features. - You can implement user embeddings, persistent profiles, or behavioral adaptation directly.
🛡 3. Data Privacy & Compliance
- Some orgs (especially in healthcare, finance, or defense) need full control over where data is stored and processed. - Self-hosting ensures no data ever leaves your infrastructure—critical for HIPAA, GDPR, or SOC2 compliance. - This also helps meet residency requirements (e.g., EU or on-prem data sovereignty).
🔐 4. Security & Access Control
- You control who accesses the model and how. - You can implement your own authentication layers, audit trails, rate limits, and internal RBAC policies. - This reduces the attack surface and risks of using public APIs.
🚨 5. Reliability & SLAs
- For mission-critical applications, self-hosting allows you to: - Ensure high uptime with redundancy and failover strategies. - Implement real-time monitoring, autoscaling, and hotfixes. - You’re not dependent on third-party API outages or usage limits.
🧩 6. Architectural Flexibility
- Custom batching, streaming, quantization, or hardware-specific optimizations are possible when you self-host. - You can deploy quantized models (e.g., 4-bit or GGUF variants) on CPU/GPU hybrids, reducing cost without relying on API providers to support those formats.
🧪 7. Experimentation & Research
- If you’re developing new LLM applications, agents, or model architectures, having full control over inference is a must. - You can benchmark across variants, simulate failure scenarios, or modify decoding strategies (e.g., contrastive search, top-k sampling) as needed.
🌍 8. Offline or Air-Gapped Environments
- Self-hosting is the only viable option for offline environments (e.g., submarines, satellites, isolated R&D labs, or government-secured zones).
So even if you’re not pushing 100M tokens/day, self-hosting might be the right call if:
You need strict control over data.
You’re building a custom experience.
You want architectural or security flexibility.
You’re doing R&D or long-horizon experimentation.
Want a quick decision checklist or flowchart to help folks figure this out more systematically?