Self-hosted LLM vs API: Diving into Total Cost of Ownership!

I'm curious, has anyone explored using dedicated inferencing chips like TPU or even optimized systems like Habana Gaudi cards for self-hosted LLMs? I've read they might offer more cost efficiency versus traditional GPUs but don't have first-hand experience.

Have you considered hybrid approaches? I've seen some teams use an open-source model for regular loads and leverage APIs like GPT-3 for peak times when they need additional capacity. It might give you the best of both worlds without overcommitting to hardware.

I actually took the plunge and started hosting LLaMA 2 on-premises. While the up-front costs were hefty (we spent around $20k on initial hardware setup), the monthly costs have stabilized at about $200, mostly electricity and cooling. However, the real challenge came from handling updates and maintenance—it demands a lot of time and expertise, more than we initially anticipated.

I'm currently managing a self-hosted LLM deployment at my company, and I can share a bit of our experience. We had similar API costs that were getting unsustainable. Transitioning to self-hosting did save us money in the long run, but it's crucial to have a realistic understanding of the upfront investment. We opted for an initial setup with cloud-based A100s, which came out to about $1500/month for compute alone. Add to that the salaries for an additional ML ops hire, it initially seemed more expensive. However, as our usage scales, we're predicting costs will level out. The hidden costs like energy consumption (if on-prem) and the time spent on maintenance and updates shouldn't be underestimated. It’s been worth it for us because of the flexibility and control over the model’s performance improvements.

Have you considered the power requirements if you’re going with on-premise A100s? We initially underestimated this aspect in our cost analysis. Also, do you have any plans for managing downtimes or failover mechanisms? In our case, the lack of a robust failover strategy led to significant penalties from our clients during a downtime incident. I'd recommend thoroughly planning for redundancy if you're aiming for high uptime.

I've been self-hosting LLaMA 2 for about 6 months now. Compute costs were the biggest factor for us — running a couple of A100s set us back around $1,000/month just in cloud costs. But on the upside, we gained more control over the model's optimizations and no longer need to worry about API outages. Worth noting, though, is the hidden cost in terms of time for maintenance and monitoring.

Have you considered a hybrid approach? We use a combination of API and self-hosting. For high-priority tasks, we leverage the API for guaranteed uptime, while less critical workloads run on our local hardware using GPT-J. This has helped us manage costs by nearly 40% and also gives us room for flexibility. It does require careful balancing of workloads, though!

What approach are you considering for ensuring fault tolerance? I've found that setting up redundant systems with automatic failover was necessary to maintain reliability when hardware issues crop up, adding both cost and complexity.

Have you considered using a hybrid approach? Sometimes, keeping the high-demand tasks on an API and handling lower priority tasks with a self-hosted model can strike a balance. This could mitigate some scalability issues and reduce costs without going all-in on infrastructure just yet.

I went the self-hosted route with LLaMA 2 not too long ago. Initially, it seemed like a no-brainer to save on those API costs, but the reality is a bit more complex. We spent about $8,000 upfront on cloud compute for a couple of A100s. Add in $300/month for storage and network traffic. Maintenance is no joke either; we had to bring on a part-time ML engineer which added to recurring costs. Just be prepared for that level of commitment!

I faced a similar decision a few months back and ultimately went with self-hosting GPT-J. My TCO analysis showed a breakeven point after about 8 months, considering I was paying around $1000/month on API usage before. For compute, I purchased a couple of A100s which was a significant upfront investment, but it's paying off now with more controlled ongoing costs. Bandwidth wasn't as big of an issue for me as I initially thought. However, do keep in mind the maintenance overhead; I've got a part-time DevOps consultant who helps with the ML ops, which is an additional expense I hadn't fully accounted for initially.

I'm really curious as well — specifically about how you're handling data security and compliance with a self-hosted LLM. We have some strict regulations to follow, which keeps us leaning towards a managed service like OpenAI. How do you plan on ensuring compliance when running it independently?

Have you considered an intermediary approach using managed services? There are providers that offer a balance by hosting models like LLaMA 2 but still handle the infrastructure aspect. It might be a way to test the waters without fully committing to either extreme. Curious if anyone else here has explored that middle path!

I've been hosting LLaMA 2 for the past few months. Compute costs can be hefty—I'm spending around $1,200/month on AWS for 2 A100s, but it's saved us on API costs that were close to $2,000/month. Networking costs were surprisingly high too, especially when we started scaling. Make sure to consider those.

Maintenance is a big part of it. We ended up hiring a part-time ML engineer to handle updates and scaling issues. If you're not experienced with infrastructure, that could be a hidden cost to factor in. But once things are set up, you do have more control compared to relying on an external API.

I've been self-hosting LLaMA 2 for the past few months, and while it's definitely cheaper for us than the API, there are a lot of hidden costs. Compute costs can vary; we ended up paying around $10K initially for a server with two A100s. We also had to factor in cooling and electricity, which bumped our ongoing monthly costs by about $200. DevOps wasn't too painful since we have an experienced team, but if you're planning to scale massively, you'll definitely need dedicated personnel or at least part-time help.

Have you looked into any hybrid approaches? You could continue using the API for less frequent, large-scale generation tasks while self-hosting a smaller model for day-to-day stuff. This way, you potentially save on some costs while avoiding the full burden of self-hosting. And how are you planning to handle fine-tuning and ensuring model updates? Keeping the model updated for state-of-the-art performance can add unexpected complexity.

Curious if anyone has crunched the numbers on training vs inference costs specific to models like LLaMA 2. If you're self-hosting, are the savings mainly from inference, or do they apply to training workloads as well? Also, has anyone quantified the bandwidth usage for these models on a month-by-month basis? Would love to dig into some statistics there.

Interesting dilemma! Have you considered hybrid approaches, like using a smaller, fine-tuned model for most of the tasks and then calling the API for more complex requests? This might reduce your API expenses while not entirely shifting the load to a self-hosted solution. Also curious, have you done any benchmarking on the performance differences between the models for your specific workload?

Have you considered hybrid models where you partially rely on the API and partially on your self-hosted instances? This can offset some of the costs when your usage peaks but still harness the reliability of the API. Also, you might want to benchmark the output quality and latency of the open-source models compared to the API—is the cost savings worth potential differences?

I've actually gone through a similar decision process, and ended up sticking with the API for now. Self-hosting initially looked cheaper, but the hidden costs in dev time, maintaining uptime, and unexpected outages made it less appealing. For example, our team spent around 15-20 hours per week just ensuring everything was running smoothly, so factor that into your TCO calculations if personnel cost is significant.

I've gone the self-hosted LLM route and here's what I found: Compute costs can be a bear, especially if you're looking at A100s—renting them can be like $16/hr each! I moved to a mixed approach with some on-demand cloud instances and some lower-end hardware on-prem for less critical needs. It does save some costs vs. API in very high usage scenarios but the hassle is real. Maintenance can eat up a lot of time if you don't have someone dedicated; our ML ops engineer spends about a third of their time on it.

I faced a similar dilemma a few months back. We decided to go the self-hosted route using LLaMA 2. Initially, our costs were lower than the API, but maintaining the system became labor-intensive. Make sure to account for not just initial setup costs but also ongoing maintenance, which, in our case, required hiring a couple of ML engineers. Compute costs were about $400/month using on-premise with some rented GPUs, but storage and networking unexpectedly jumped to ~$300/month due to extensive data usage. Overall, there's potential for savings, but it's crucial to have the right expertise in-house.

An alternative to consider is using a service like Hugging Face's Inference API, where you can self-host through them. It might cost more than full self-hosting, but you'll save on expertise and infrastructure headaches while being cheaper than OpenAI's API. Worth checking out if you haven't already!

I made the switch recently to hosting LLaMA 2 and it definitely requires a solid initial investment in both time and hardware. I estimate around $300/month for cloud instances with sufficient GPUs, but long-term savings could be significant if your usage keeps increasing. Maintenance is indeed a challenge — consider at least half a DevOps engineer's time if you're doing it all in-house. But the control you gain is incredible if you need it for specific use cases!

I've been in similar waters, and ultimately decided to stick with APIs. The self-hosting route can be appealing cost-wise initially, but the hidden costs really stack up. We estimated that, for us, the necessary personnel alone would be around $10K/month to handle monitoring, updates, and troubleshooting. Plus, there’s always the challenge of ensuring uptime and reliability. It's not for everyone!