OpenAI vs Anthropic: Anyone got hands-on experience with pricing for high volume workloads?

I haven’t used Anthropic yet, but with OpenAI, we've been able to negotiate better rates by contacting their sales team directly since our usage was quite high. It could be worth reaching out to both platforms to see if they offer custom pricing for high-volume clients. Anyone got insights on negotiating with Anthropic?

I haven't used Anthropic for high volume, but we did trial both. OpenAI’s API supports stronger integration tools which was a plus for us. I've heard Claude 2 actually charges less per model update if you frequently tune your models. Would be interesting to hear if others have had success using Anthropic’s models at scale. Tim, have you considered using OpenAI’s batch processing discounts? That might help bring down costs further.

Hey Tim, we've been running our app with similar query volumes. With OpenAI, what caught us off guard were the overages related to burst usage in peak times, which hiked our bills a couple of times. We managed costs by setting usage limits and using priority tiers, which help a lot in controlling the expenditure. We've also experimented with Anthropic, and you're right, their pricing can be less clear, but we found them somewhat more stable in terms of predictability without surprise fees.

Hey Tim, we're using OpenAI's GPT-4 for around 8 million queries per month, and honestly, one thing we found is keeping tight limits on token usage per request really helps control costs. The billed amount can skyrocket if you don’t monitor it carefully. We've set up internal tools that throttle usage and optimize prompts to ensure we stay within budget. Anthropic's pricing did seem less transparent when we checked, so we stuck with what we knew.

I’ve been testing between both for some time now. OpenAI's pricing transparency is definitely a plus, but I've noticed Claude’s token efficiency can sometimes result in less verbose outputs. Although it initially seemed more expensive with Anthropic, we ended up saving around 15% on some projects by needing fewer tokens. It's more about the nature of your workload than just the costs per token.

Has anyone here tried leveraging prompt engineering to manage costs more effectively? We've been experimenting with different approaches to grouping related queries together where possible, which sometimes helps reduce the number of calls we need to make. It'd be great to hear if others have tried similar strategies or if there are any neat tricks you've come across that work well with either OpenAI or Anthropic's options.

Hey Tim, I've been using OpenAI for some high-volume tasks. Admittedly, their pricing can add up quickly with high usage, but I've found that setting strict limits on token usage per request and caching frequent outputs have helped in managing costs effectively. As for hidden fees, the only surprise I've encountered was occasionally needing more compute resources which can be a bit costly. Balancing context size and quality is key, so you might want to run some A/B tests to see what truly fits your needs.

Hey Tim, I've been using OpenAI for a production app with a similar volume. While their pricing structure is fairly transparent, I found that prompt optimization really helps in cutting costs. One thing to watch out for is the 'prompt engineering' trap where iteration on prompts can rack up unexpected costs if not managed carefully. Anthropic's pricing, on the other hand, gave us a headache trying to pin down exact estimates before committing. It's definitely less predictable, but could be worth it if their model aligns better with your needs.

Interesting question, Tim! We opted for OpenAI's GPT-3 for a while, and ended up pivoting to an ensemble approach, leveraging smaller, more price-efficient models for routine tasks and reserving GPT-4 for complex queries only. This helped us reduce costs significantly. Curious if others have tried using Anthropic in a similar way?

Hey Tim, I've been using OpenAI in production for around 5 million queries a month. You're spot on about the pricing. One thing I did notice though is their billing cycles sometimes don't align well with high-volume workloads. We ended up deploying a queuing system to manage peak usage times effectively. There's a bit of a learning curve, but once you get the hang of it, it helps manage costs significantly!

Interesting discussion! We're currently evaluating both and leaning towards Claude for a trial. One thing to note is that Anthropic does offer volume-based discounts if you negotiate directly with them, which isn't apparent in public pricing details. It could help bring costs closer together. For us, slated for around 8 million monthly queries, the discount might make Claude 2 viable despite the higher base pricing. Just something to consider when finalizing your decision.

Hey Tim, I've used both for different projects and from my experience, if you're running high volume workloads like yours, optimizing token usage is crucial. With OpenAI, watch out for longer completions — they can really spike your costs if you're not careful. We implemented a system to measure and optimize token usage on a per-query basis, which helped bring down our average costs by about 20%. Also, Anthropic offers discounts for higher volume commitments, so it might be worth inquiring about that if you decide to go with them.

Have you considered setting up some usage alerts or limits if the APIs support that? It might help you keep costs in check by giving you a heads up if something starts to go over your budget. Also, are there noticeable differences in the quality of responses you're getting from either model that could impact long-term user satisfaction for your app?

Hey Tim, we’re running a similar scale operation with GPT-4. One key thing to watch is their token count calculation, especially for complex prompts. In practice, we’ve seen monthly costs hover around $3500-$4000 at your volume, but we optimized by batching requests when possible and reducing unnecessary token usage in trainings. No hidden fees per se, just more derived from usage as you ramp up. Never used Anthropic though, so curious if anyone else can share numbers on that?

Just curious, are you factoring in the potential need for tuning or customizing the models in your cost analysis? Sometimes, especially with highly specialized applications, the cost of integrating and customizing the models can overshadow the per-token pricing. Would love to hear if anyone has insights on the long-term costs beyond the upfront pricing.

We're in a similar boat, juggling high volumes! With Anthropic, we've mostly had to negotiate bespoke pricing for massive workloads, which ended up being more cost-effective in our case. It's worth initiating a conversation with their sales team for tiered pricing structures that might not be immediately visible on their regular accounts. Also, consider leveraging combination models if the extra functionalities in OpenAI aren't mission-critical for you. We've noticed negligible performance differences for general queries.

Hey Tim, we've been using OpenAI for a similar scale and what surprised us was the occasional spike in token usage due to more elaborate prompts or user queries. In our case, optimizing our prompt design helped mitigate some costs. We also implemented token usage tracking in our pipeline to avoid unexpected overages. As for Anthropic, I heard that Claude can potentially have lower inference times, which could be a plus depending on your latency needs.

Hey Tim, we’re running something similar with OpenAI’s GPT-3.5 for a healthcare app. In our case, the extra functionalities of OpenAI really helped justify some of the extra costs as we needed robust language capabilities. One tip: track your tokens meticulously. We had unexpected overages initially because we underestimated our token usage in edge cases. Setting up alerts can save you from a surprise bill!

I've been testing both platforms for about six months. One thing to keep in mind is Anthropic's billing seems less predictable, which can be challenging for budgeting. However, Anthropic sometimes offers discounts for large-scale projects if you negotiate directly. You might want to reach out to their sales team to see if they can cut you a deal. Don't rely solely on the surface prices you see online!

I'm actually curious about token efficiency. Has anyone done a side-by-side comparison of how many tokens different tasks actually consume between OpenAI and Anthropic's models? Wondering if one ends up being more cost-effective that way, even if raw pricing per token seems higher.

Hey Tim, we're using OpenAI's GPT-4 for a similar volume and approximate workload as yours. In addition to the base costs, I've noticed occasional overages mainly due to unexpected surge triggers, so setting up a system to monitor and control token usage has been crucial. While OpenAI's pricing might appear cheaper at first glance, you might need to consider potential fluctuations in usage. We've found cost management tools within their API quite helpful, though I can't speak much about Anthropic since we haven't switched over.

We're using OpenAI's API, and I've noticed that while their pricing for high volumes can seem steep, their support for fine-tuning and other additional features provide better value for certain applications. We process over 8 million requests monthly, and the performance hits the mark consistently. If performance consistency is a priority, it's worth weighing those additional functionalities against the raw token costs. Haven't used Anthropic yet, so I'd love to hear from others on how it compares in actual production scenarios.

Hey Tim, we've been using OpenAI for about 8 months at roughly the same volume you're looking at. I can confirm that the pricing you mentioned for GPT-4 aligns with our agreements. One thing to watch out for is prompt optimization. We've managed costs by further condensing our prompts to maximize required context in fewer tokens. As for unexpected costs, keeping a check on usage patterns has helped us avoid surprises. Not much experience with Anthropic, so curious if anyone else can share that side of things.