Self-hosted vs API LLMs: Crunching the Numbers on Total Cost of Ownership

I've recently switched from using OpenAI's API to self-hosting GPT-J on NVIDIA A100s. Initially, our GPU hosting costs were around $4,000/mo, but we underestimated the complexity of managing the infrastructure. My team spends about 40 dev hours a month just on maintenance and troubleshooting. If your team isn't familiar with MLOps already, factor in some serious learning curve time!

I'm curious if anyone's quantified the cost of downtime or service outages when self-hosting. With APIs, the SLA often guarantees a certain level of uptime, which can be worth the premium. What have folks experienced in terms of outage frequency and recovery time, especially working with models like GPT-NeoX?

I recently went down the self-hosting route with GPT-NeoX, and there's definitely more to consider than just AWS costs. For 500k queries, you're looking at needing at least 2 A100s, which comes out to roughly $8,000/mo just for compute on-demand. Factor in storage, networking, and the DevOps time for setup and maintenance, and the costs can creep up high quickly. On the flip side, you have no API limits to worry about and full control over the infrastructure. Personally, the biggest 'hidden' cost was the time investment, especially when things didn't go as planned.

In case you're looking for alternatives, we found Google's TPU pricing to be competitive for our workload compared to AWS. Also, using preemptible instances can significantly bring down costs if your application can tolerate some downtime. It's a trade-off but worth considering!

Really interested in understanding the cost comparisons further! How do you see the performance of self-hosted models versus using the API? Particularly curious about latency and the response times users experience.

We've been self-hosting GPT-J, and I'd say it's a mixed bag. We did save on the API costs after the initial setup, but DevOps has been a significant headache. Our team spends roughly 20 hours monthly just on maintenance and updates. Consider your team's expertise - this might tip the scale one way or the other.

It really comes down to your specific use case. We moved to self-hosting GPT-NeoX a few months ago, and while the initial GPU costs were hefty, we're now spending around $3,500/month on infrastructure compared to $6,000 when we used an API. The hidden costs are indeed in DevOps and model maintenance. We had a 2-day outage once after a bad update went live; the trade-off is having full control and customization.

We made the switch from an API to self-hosting GPT models a few months ago. The biggest surprise was how much time our DevOps team now spends on system upkeep — we're talking easily 20-30 extra hours a month. Sure, there's a savings on API costs, but it’s offset by the need for skilled personnel who can manage and troubleshoot these systems effectively. If you're considering it, definitely weigh the human resource factor heavily!

Has anyone benchmarked the exact number of queries per second they're getting on a self-hosted setup vs. the API? Curious about throughput differences, especially around peak usage times. Also, for those who've chosen self-hosting, how do you manage security concerns, especially regarding data privacy on rented hardware?

How critical are the latest model updates to your service? Sticking with the API gives you immediate access without the hassle of retraining or updating models. If your use case doesn't demand cutting-edge updates, self-hosting might save costs. But for us, updates were important, so the API was the way to go.

We made the switch from API to self-hosted GPT-J about a year ago. Our biggest surprise was the time investment in DevOps—we ended up needing two part-time engineers just to manage the infrastructure and keep everything running smoothly. On the financial side, we're saving around 30% compared to our previous API costs, but outages became a real headache. We've had to build a lot of redundancy into our system. It's definitely a trade-off.

Curious to know how latency and uptime compare between API and self-hosted solutions? With APIs, I've found uptime pretty reliable, but not sure if self-hosting could compete without significant investments in redundancy and monitoring.

Have you looked into the energy costs for running GPUs over cloud vs on-prem? That can be a surprisingly big factor, especially if cooling is a concern. Also, if you're using an API, you get to offload the responsibility of model updates and upgrades, which otherwise can be pretty time-consuming for a small team trying to self-host.

Has anyone considered the potential legal implications of self-hosting like data privacy and compliance? We noticed there are additional layers of liability when hosting on our own servers versus using a third-party API.

Have you factored in energy costs for on-premise hosting? We noticed a 20% overhead in power costs when we ran a similar setup in our own data center. Also, how do you plan to handle model updates? Keeping a model like GPT-NeoX optimal requires continuous fine-tuning, which can be another expense if you don't have the right experts.

We moved from API to self-hosting last year, and while server costs do eat up a chunk (around $3,000/mo for our particular A100 setup), the real kicker was DevOps. Expect to spend at least a couple of hours each week just on updates and troubleshooting if you're not using managed services. The initial setup was intense; we had to dedicate a full-time team for about two months just to get everything running smoothly. In contrast, the API saved us a ton of time but yes, the cost was high. So, it really depends on where you want to invest your resources!

We've actually been down this road with hosting GPT-NeoX. You're right, it's not just the GPU costs. With our traffic, DevOps ended up taking way more time than anticipated—probably adding another 25% to our cost estimate. On the plus side, we gained more fine control over model updates and latency. We also had to weigh the downtime risks, which were more frequent than when using an API like OpenAI's. If you don't have a strong DevOps team, I'd tread carefully.

I've been down this road, and it's true, the costs aren't always where you expect. Besides server expenses, don't underestimate the power and cooling costs for GPUs if you're not using cloud solutions. We switched to self-hosting with GPT-NeoX on A100s and saw our costs drop to about $3,000/mo, but the DevOps hours increased significantly. Maintaining uptime and model updates are indeed a hidden cost.

We considered a similar switch last year. For our setup, maintaining a self-hosted LLM meant allocating roughly 20-30 hours a month for DevOps tasks, and a single A100 was around $2,500/month just for hosting. That said, self-hosting definitely gives more control over data and some cost predictability, but be prepared for frequent GPU optimizations and constant monitoring to avoid downtime.

What uptime requirements do you have? We've found that self-hosting can mean more downtime unless you're prepared to invest heavily in redundant systems or multi-region setups. Also, any thoughts on how you plan to handle model updates? Staying current with the latest models can be a challenge without the API.

We've been self-hosting GPT-J on A100s for a while now, and I'd say maintenance is definitely the hidden cost people don't initially consider. We're spending around 40% of our DevOps time just on model tuning and scaling optimizations alone. Not to mention, every time a new model comes out, evaluating and integrating it is a whole project in itself. That said, owning our stack means we can fine-tune and pass model improvements onto clients faster.

Great discussion! If you're handling 500k queries per month, don't forget to factor in the costs of load testing and redundancy. You don't want outages during heavy loads. We self-host with redundancy across two data centers to prevent downtime, but that obviously adds to costs. Curious if anyone's mapped out the potential savings from a multi-year perspective?

Have you considered hybrid approaches? Using API for high-demand surges and self-hosting for lower, consistent usage could balance costs and give you flexibility. I’ve seen a few companies trying this and it seems to help mitigate risks associated with outages and managing capacity.

Agree that it's more than just server costs. We moved from GPT-3 API to self-hosting a GPT-NeoX model about 8 months ago. Our initial hardware investment was around $30k, with ongoing costs of roughly $2k/month on electricity and maintenance. However, the real kicker was the time spent setting everything up and the ongoing updates. Our small team had to dedicate around 20 hours a week initially just to get things running smoothly. API advantage is definitely in the zero-maintenance aspect.

Curious about what kind of ROI you're expecting from self-hosting? With our setup, we aim to break even in about a year compared to API costs, but we're a bit on the fence about model updates and staying competitive if OpenAI releases a significantly better version. Is anyone using hybrid models or some auto-scaling approaches to balance costs and performance?

I went through a similar decision-making process last year. Ended up self-hosting GPT-NeoX and here’s what I found: GPU costs are indeed hefty! We used a setup with 2 A100s, which is about $3,200/month even with discounts. DevOps became a significant part of our budget, not to mention outages we're more frequent than I'd like. On the flip side, we gained better control over the infrastructure setup and were able to fine-tune the model to our needs. In terms of the update cycles, we're lagging behind what the API offers and that can be frustrating.

I'm curious to know what specific hardware setups everyone is using for self-hosting. For a similar scale, we looked at getting A100s on demand, but our provider quoted about $2,500/mo just for hosting costs, not including other overheads. Seemed like a steep upfront to me, but maybe someone has found ways to optimize this?