Hey fellow developers,
I wanted to share my recent dive into managing costs for AI/LLM projects while setting up our startup. We initially started using OpenAI's GPT-3.5-turbo for our NLP needs. It's a powerful model, but as our usage grew, so did the costs – reaching nearly $10,000 monthly! 😳
To tackle this, I explored a mix of solutions:
Managed Services: During our hunt for cost efficiency, we tested both Sagemaker and Azure's AI services. While Sagemaker provided better native integration with our AWS stack, Azure had more competitive pricing for light usage.
Optimizing Calls: We revised our API call strategy. Previously, we included redundant context in prompts which bloated costs. By implementing prompt compression and caching frequent responses, we reduced API calls by around 30%.
Experimenting with Open-Source: We evaluated open-source models like GPT-J and LLaMA-2 for some tasks. These models, when finetuned, offered an affordable alternative for specific in-house tasks, though setting up infrastructure took a bit more effort.
Monitoring Tools: I can't stress enough on the importance of observability. We set up Grafana dashboards to monitor our LLM API usage and costs in real time, enabling more proactive management.
I’d love to hear your experiences or any cost-saving tips you’ve come across. Also, is anyone else transitioning part of their workload to open-source due to pricing?
Looking forward to insightful discussions!
We've gone a slightly different route by utilizing Google's Vertex AI for some NLP components. The upfront integration effort was higher, but they offered better scaling options for our needs. We also observed about a 15% reduction in costs compared to Azure. In terms of open-source, we're still trying to evaluate the long-term support costs vs. cloud solutions. It's a tough call!
I've been down this road myself! We also started with GPT-3.5 but found costs skyrocketing over time—similar to your experience. Something that really helped us was deploying a hybrid architecture using a combination of open-source models and commercial APIs. For example, we use LLaMA-2 for simpler tasks and GPT-3.5 for more complex queries. It saved us about 40% on our total AI costs.
Could you share more details on how you implemented prompt compression? I'd imagine that you could lose some quality in responses if too much context is removed. Did you face any issues with maintaining the same level of service quality?
Great rundown on how you're tackling the costs! I agree with the point on caching. In our case, implementing a Redis-based cache reduced our API call costs by 50%. For open-source, we shifted to using Alpaca for some non-critical tasks, and it cut a noticeable portion of our expenses without compromising much on performance.
Thanks for sharing your experience! I'm curious about the open-source route – how hard was it to set up the infrastructure for LLaMA-2, and what kind of performance differences did you observe compared to GPT-3.5-turbo?
Great insights! We've also been struggling to keep costs down with LLMs. We recently switched to OpenAI's GPT-3.5-turbo as well, and it's good to know that prompt compression can make a difference. Did you use any specific tools or libraries for prompt compression, or was it more of a manual optimization?
