Hey folks, I've been diving deep into the RAG (Retrieval-Augmented Generation) pipelines, and I wanted to discuss costs related to each part of the setup: generating embeddings, storing/accessing a vector database, and running inference.
For embeddings, I'm using OpenAI's Ada model. It roughly costs me $0.0001 per 1,000 tokens. Not too bad for small datasets, but it adds up quickly when scaling.
I'm leveraging Pinecone for the vector DB, and their pricing is based on index size and number of queries. I'm currently at a few million vectors, costing me about $150/month, including extra for additional queries and throughput.
Inference is the priciest part. Depending on the complexity, I'm using a combination of GPT-3.5 Turbo and sometimes GPT-4 for more nuanced queries. Running inference amounts to another $200/month.
Tips for cutting these costs? Particularly for embeddings and inference? I'm exploring using local models like Sentence Transformers, but curious about the community's strategies here.
Would love to hear experiences from anyone who has also navigated these waters!
Have you considered using AWS services for embeddings? They have some cost-effective options if you hit certain usage levels. Also, how do you handle caching for inference results? Sometimes caching frequent query results can drastically cut down on redundant inference calls, saving both time and money.
How are you handling storage and scalability issues with Pinecone? Does the $150 cover any redundant backup or is this something you'd need to handle separately? I've been considering Pinecone for a project, but I'm worried about unexpected growth shootups and their impact on cost.
I've been in a similar boat. I started using Sentence Transformers for embeddings, and it saved me quite a bit. The upfront cost for setting up a capable local GPU can be steep, but it pays off in the long run if you're processing embeddings at scale. For inference, distilling a model could help; I've been experimenting with LLama and Alpaca models for some tasks, and they're surprisingly effective for the cost.
I've been through similar challenges. Switching to local embeddings with Sentence Transformers can definitely cut down on the costs. I found using all-MiniLM-L6-v2 model locally worked well for my use case and significantly lowered our expenses compared to OpenAI's Ada model. You might initially invest time in setting up but it's worth it in the long run.
Have you considered batching your queries for inference? I found that by structuring my queries to process in batches, I reduced the number of individual requests to GPT, cutting down my costs by about 20%. Just depends on how your use case can handle the batch processing.
Regarding the vector database, are you using any batch processing or caching strategies? It helped us cut down the number of expensive, high-throughput queries. For example, we use batch updates and cache common queries' results to reduce redundant computations. If you're not doing this yet, it might be worth looking into!
Have you looked into using FAISS for the vector database? If you're fine with an on-premise solution and can manage the infrastructure, it might be a cheaper alternative to Pinecone. In my case, setting it up required some elbow grease, but it completely eliminated third-party server costs.
I also started with Ada for embeddings, but ended up switching to Sentence Transformers running locally. It's significantly cheaper, though it might require a bit more upfront time to set up. For inference, have you looked into running the smaller Llama models locally? The initial setup can be a bit of a hassle, but once it's up, the cost savings are substantial.
I'm in a similar boat, and I've been experimenting with running sentence-transformers locally. It cuts down on the embedding cost significantly for me. I initially trained them on a smaller subset and then scaled more incrementally as I needed more vectors. It doesn't come close to OpenAI's Ada in terms of pure prowess out of the box, but with some domain-specific fine-tuning, it really gets the job done efficiently.
I've had a similar experience with the costs stacking up as the data scales. For embeddings, I've switched to using OpenAI's Ada model and supplementing them with Sentence Transformers for less critical tasks, which helped cut costs nearly by half. For inference, you might consider experimenting with quantized models running on local hardware to decrease your GPT-4 dependency.
Totally feel you on the costs! I've been running a similar setup but managed to cut costs using Sentence Transformers for local embeddings. It uses less GPU time, and while not as accurate as OpenAI's models on complex queries, it's surprisingly efficient for simpler tasks.
Have you considered using FAISS for your vector database needs? It's open-source, so you avoid those monthly costs from Pinecone, though you'll need to handle the infrastructure yourself. Also curious to know if anyone has numbers on how FAISS compares with Pinecone in terms of retrieval speeds.
One approach that worked for me was batching the inference requests, minimizing model invocations. Also, using local models with distillation techniques might save some bucks. Vulkan-accelerated Sentence Transformers are quite promising — they offer better performance for lower latency and cost if you have compatible hardware.
I've been in a similar boat trying to optimize costs. For embeddings, I've switched from OpenAI's models to using local models like Sentence Transformers, which helped cut costs significantly. You need a decent GPU, but the savings over time are worth it, especially for large-scale operations. As for inference, maybe batching prompts together, if possible, could also reduce costs?
I totally get where you're coming from. I've been experimenting with local embeddings using Sentence Transformers and it's been quite cost-effective. Although setup took some time, my inference costs went down by about 60%! Just make sure your hardware is up to the task, because you'll need a decent GPU for speed.
I've been using Hugging Face Transformers for embeddings with BERT-base models locally. It significantly cut our embedding costs, but it took some compute power to manage larger datasets. Also, we've been experimenting with milvus.io for the vector database as an open-source alternative, and it's been quite efficient if you're okay hosting your own.
Have you tried opting for a hybrid approach where less critical parts use smaller models like BERT? Also, what kind of throughput are you averaging with Pinecone? Is there a way to adjust query frequency to minimize costs?
Have you looked into optimizing query costs by batching queries when using Pinecone? I had a significant reduction in costs by tweaking query batch sizes, effectively reducing the number of individual requests. It didn't affect the latency as much as I expected, so it might be worth a shot if simultaneous query resolution isn't a hard constraint for your use case.
How much data are you processing to hit $200/month for inference? That seems a bit high unless you’re doing substantial volume. Also, have you considered batching requests? We saved around 20% by aggregating queries before sending them off for inference.
