Self-Hosted vs API Models: Which Is Actually Cheaper?

I had a similar dilemma. My API costs were around $3,800/month and I switched to self-hosting LLaMA. Our ops cost went down but hardware upgrades and increased engineering hours were a surprise. Managed to cut expenses by around 30% eventually, but it took us a while to optimize. If you have spare A100s, it might be easier, just have a solid engineering team to manage the overhead.

One thing to consider is the cost of scaling inference. With OpenAI, you get the flexibility of scaling with just additional API calls. Self-hosting, however, means scaling isn't just about adding more calls—it's also about handling and distributing load, possibly needing additional hardware and bandwidth. In our experience, the cost-efficiency tipping point came with consistent high usage though. What are your monthly usage peaks looking like?

I went through the same debate a few months back. After doing some back-of-the-envelope calculations for our volume, we found that self-hosting with LLaMA and a few RTX 3090s gave us a 30% reduction in costs compared to OpenAI's API. However, the savings mostly come from longer sessions where the API would spike costs due to larger volumes. Initial setup and fine-tuning took a bit of engineering effort, but we managed to stabilize and operationalize it with a small team within a month.

One question: have you factored in the cost of downtime or errors if things don't go smoothly with self-hosting? In my experience, the reliability of cloud APIs can sometimes offset unexpected labor costs when running into issues with local deployments. Just something to think about if uptime is critical for your applications.

I went down this road a few months ago and found that while initial costs seem intimidating for self-hosting, over time it pays off, especially if you have a dedicated devops team. For instance, we saved about 30% annually on model inference once we fully transitioned to self-hosting LLaMA on our rack of A100s. One thing to watch out for though is the jargon-laden nightmare of model optimization and regular updates, which can add hidden costs in dev time.

Can someone provide any benchmarks on how much electricity costs for running these models? I've heard that while GPU time might be accounted for, the electricity costs can be unpredictable, and it impacts the TCO quite a bit depending on where your data center is located. Also, how does the cost of retraining models compare to simply using the latest iterations from a service like OpenAI?

I've gone through a similar debate, and we ultimately decided to self-host. Beyond just the operational costs, keep in mind the complexity added by maintenance and updates. You're looking at not just hardware costs but also the manpower for managing server uptime, security patches, and potential migration complexities. Our team took about a month to fully optimize the setup with GPT-J, and ongoing maintenance takes 10-20% of our DevOps resources. So, while costs can be brought down with a self-hosted model, make sure you factor in all these soft costs!

I went through a similar situation a few months ago. While self-hosting can lead to savings, it really depends on your workload stability and resource predictability. In my case, we were able to cut API costs by 40% by switching to a self-hosted model because our usage was quite stable and predictable. However, the initial engineering setup and ongoing maintenance shouldn't be underestimated, especially keeping the model updated and optimizing it for your specific hardware setup.

I've been in a similar situation and ended up going with a self-hosting approach. Our monthly API costs were around $3,000, and with the spare GPUs we had, it made sense to switch. One often overlooked cost is the engineering time required to keep the model updated and optimized. We saved money in the long run, but we did spend quite a bit upfront on setting up the infrastructure and tuning the model to our needs. I'd say if you've got the hardware, it might be worth experimenting with prototyping your own deployment and seeing where your breakeven point might be.

I've been on the same boat and ended up setting up GPT-J on-premise. The initial setup was definitely a headache, but once you get it running, the costs are more predictable. For us, running a couple of A100s keeps our costs at around $3,500/month including the electricity and occasional hardware maintenance. The trade-off was mainly in deploying updates and scaling flexibly, which was a smooth process with APIs. So, it's worth considering what your peak usage looks like and how often you'd need to scale up or down.

We considered self-hosting GPT-J, but the operational readiness wasn't there for us. We really valued the rapid scaling without additional engineering resources, which the API offered. Our load can be highly variable, and managing that with hardware would have been a nightmare, honestly. The extra convenience was worth the $4k/month for us versus worrying about self-hosting and potential downtime.

Has anyone here tried hybrid approaches? Like using API for spike loads and self-hosting for regular traffic? I imagine balancing both could mitigate the drawbacks — does it actually lead to savings or just add more complexity?

I've run a TCO analysis for our setup, but we're using a GPT-3 API instead. Even when factoring in our NVIDIA A100 resources, the ops costs added up quickly—electricity, cooling, and the manpower to manage everything. We saved about 20% in costs switching to self-hosting, but it required a solid initial investment in infrastructure and time. If you're already spending $5,000/month, I'd say weigh the initial costs heavily and expect a gradual payoff.

I've been in a similar boat. We opted to self-host GPT-J on a cluster of RTX 3090s. Our average operational cost turned out to be around $3,000/month, but it was a headache to set up and maintain. The main savings were in predictable costs and not worrying about API rate limits, but debugging was an unexpected labor sink.

Has anyone factored in the costs for redundancy and failover systems when self-hosting? With API models, you rely on the provider's infrastructure for reliability, but self-hosting might require additional investments to ensure uptime and mitigations in case of server failure. Wondering how others are handling that and what kind of costs are involved.

Have you considered a hybrid approach? We’re using API models for tasks with unpredictable traffic and self-hosted models for stable, high-volume requests. This way, we balance out the costs while minimizing risks like downtime, and it also helps in scaling during high demand without over-committing to either path. Just my two cents!

Could anyone using self-hosted models share their experiences with deploying updates? I'm curious about how you handle model updates and retraining cycles compared to the relatively seamless API updates. Do you find it troublesome?

Have you considered doing a hybrid approach? Maybe keep using the API for peak loads and self-host during off-peak hours? I've seen teams use this strategy to balance cost and performance. Plus, it keeps you from being too reliant on a single service, which could be a strategic advantage.

I've been down this path before and the trade-offs can be tricky. Setting up and maintaining a self-hosted model does save costs in terms of API fees, but you have to factor in upkeep like software updates, bug fixes, and tuning the model to fit your specific needs. In our case, we found out that once we included engineering salaries, the savings weren't as significant as we hoped because it required dedicated time from our team. Make sure you evaluate these hidden costs!

Would you have enough A100s to handle peak load times? One downside of self-hosting could be potential latency issues if your available hardware isn't scaled to your peak performance needs. Maybe start small and run some tests with current capacity while evaluating how frequently you'd need to spin up additional resources.

I'm curious about the ongoing engineering hours you mentioned. How much time do you estimate your team would need weekly to maintain a self-hosted setup? It’s one aspect I’m trying to factor in but struggling with due to the variance in tasks like model updates and infra scale-ups. Also, what do you consider a reasonable buffer for unforeseen expenses in a self-hosted setup?