Hey folks, I've been diving deep into LLM observability tools lately, specifically focusing on tracking spend across different API providers like OpenAI, Cohere, and Hugging Face. As our usage scales, the costs are adding up fast, and we need better insight into how much each model invocation is really costing us.
Here's what I've found so far:
Datadog: Great for general observability, but feels a bit overkill for just tracking LLM spend. Also, setting up custom metrics to capture API responses (including costs) can get pretty complex.
Prometheus + Grafana: Building a custom solution works if you're already invested in this stack, but you have to manually parse API responses to extract billing data. Doable, but not trivial.
FinOps Platforms: Some emerging startups are focusing specifically on cloud cost management across varying tools/services including AI services. These tools are super promising but can be pricey themselves.
Custom Scripts: Writing our own scripts that hit the provider's billing APIs and then push that data to something like AWS CloudWatch or Google Stackdriver. Downside is the maintenance burden.
What are you all using to keep tabs on your LLM API costs effectively? Any secret sauce tools or setups out there that have worked wonders, or is it more about mixing and matching stuff based on your infra? Would love to hear everyone’s thoughts!
I'm curious, how much flow vs basic usage are you encountering? We've hit around 500k calls monthly and I wonder how our costs compare. As for tools, I find that a combo of custom scripts pulling data into Google Looker Studio provides a nice visual breakdown without too much fuss.
Interesting discussion! Has anyone looked into using Snowflake for aggregating cost data across multiple providers? We ingest logs and usage data from providers into Snowflake and then run queries to track spending. It's actually worked quite well and allows for deeper analytics on usage patterns.
I'm with you on finding Datadog a bit much for just tracking API costs. We've actually been experimenting with a custom Grafana dashboard over Prometheus, pulling in data from AWS Cost Explorer. It's a pain to set up initially, but once it's running, it gives us pretty solid real-time insights. Not the cleanest solution, but it's working for now.
I've been using Prometheus with Grafana for a lot of my monitoring needs, but I completely agree with you on the complexity. Parsing billing responses into something useful takes a fair amount of setup. We tried using it for API cost tracking too, but eventually switched to integrating with a FinOps platform for simplicity. Definitely more pricey but saves us tons of time!
Curious, did you evaluate any specific FinOps tools, and if yes, which ones? I'd love to know how they stack up feature-wise.
Have you looked into CloudZero? They’ve been expanding their capabilities to include AI/ML services tracking. We're evaluating their platform right now because it supposedly provides real-time cost association with specifics like LLM model usage. Anyone else tried their service?
We've been using Prometheus with Grafana as well, and I agree it's not for the faint of heart. One trick that's worked for us is batching the API call responses and aggregating costs offline before feeding them into Grafana. It's an additional step but simplifies dashboards immensely.
We've been trying out a combination of Datadog and some custom scripts. I totally agree with you that Datadog feels like overkill just for tracking costs, but when combined with scripts that pull detailed billing data, it becomes quite powerful. The initial setup was painful, but once configured, it’s pretty smooth.
I've actually had some success with cost tracking using a tool called CloudZero. It's more on the expensive side but does a fantastic job of breaking down expenses, and they just added a feature specifically for AI spending. It might be overkill for small teams, though.
We've been using Prometheus + Grafana, and I agree, it's definitely not trivial to parse and visualize billing data. However, once we got it set up, it provided a lot more flexibility in our dashboards compared to other options. The key was automating as much of the data parsing as possible. Anyone stuck on a similar setup, feel free to ask — happy to share some Grafana config tips!
Great overview! Have you looked into tools like CloudZero? Although they're more cloud-focused, they claim to have insights for AI spend too. I haven't tried it myself, but it might be worth checking out if you're looking for a more streamlined solution.
Has anyone tried using New Relic for this? As much as I love the flexibility of custom scripts, maintaining them across updates from providers is a full-time job. New Relic offers some cool integrations that could simplify this, but I'd like to hear some real-world experiences.
I'm curious about the FinOps platforms you mentioned. Can you name a few that you've found promising? We’ve tried generic cloud cost management tools, but they often miss the nuances of LLM APIs and their specific usage patterns.
We've been sticking with Prometheus + Grafana mainly because our infra is already aligned with it, so it's a matter of building on what we know. Extracting billing data is definitely a hassle, but once it's set up, it beats waiting on third-party updates. Plus, Grafana dashboards are unbeatable for visual insights.
Have you looked into using CloudCost? It's designed for multi-cloud cost transparency, including AI API spend. The visualization tools are quite robust, and it might save you from having to set up complex monitoring with other tools. We managed to reduce our unplanned spend by 15% since we started using it.
I've been in the same boat; tracking costs across different providers is a headache! We've actually started using Excel for preliminary breakdown as a stopgap. We pull data via custom scripts from each API and then run some pivot tables on it. Our plan is to move this to a more scalable solution soon, though.
Have you checked out Kubecost? While it's primarily geared toward Kubernetes, we've been able to customize it for tracking AI-related spend. It takes some time to set up correctly, but once you tie in the API costs, it provides a decent budgetary overview alongside your other cloud costs. Plus, open-source means you're not locked in like with commercial tools.
Have you looked into using Cloud Custodian? It’s more known for governance and policy enforcement but can be tailored to track cost anomalies with some tinkering. We've managed to save about 15% on our monthly spends by identifying and shutting down ghost runs on various providers. Interested to hear if anyone else has integrated it successfully for observability.
Totally feel you on this! We've been using a combination of Prometheus and Grafana for ages because we're already deep in that ecosystem. The initial setup took time, especially around parsing the billing API responses, but once you get over that hurdle, it’s smooth sailing. For us, the key was creating a couple of dedicated dashboards just for cost metrics. Worth the effort if you have the resources!
We're in the same boat as you, grappling with LLM costs. I totally agree about Datadog being overkill. We've had some luck using Grafana with Prometheus Alertmanager to get notifications when our spending hits specific thresholds. But yeah, it's not a plug-and-play solution at all.
We've been using Datadog for a while now, and I agree it's a bit much if you're just focusing on LLM spend. We found integrating Datadog with our existing workflows was helpful though, especially since we had alerts set up for sudden spikes in usage. But honestly, if I had to set it up from scratch just for cost tracking, I might look elsewhere.
We've been using a combination of custom scripts and Prometheus + Grafana. Initially, it was a bit of a headache to set up the API parsing, but once configured, it provides a lot of control and visibility over our costs. We've also considered looking into tools like CloudZero which specialize in cloud cost management, but we're still on the fence due to budget constraints.
Just chiming in to say we've been using custom scripts ourselves. We have a cron job that polls the billing APIs and pushes the data to Google Stackdriver. It's a bit of a pain to maintain, especially when API changes happen, but it gives us exactly what we need.
An alternative we've been eyeing is Harness, particularly their cloud cost management tools. Might be worth checking out!
Has anyone had success with the cloud provider's native tools for this purpose? Like using AWS Cost Explorer or similar on Google or Azure to track spending specifically on these APIs? Wondering how effective they are compared to third-party solutions.
Curious if anyone has tried Snowflake for this. We’re leaning towards consolidating our data pipelines into it and were wondering if its analytics capabilities could simplify cost tracking across LLM providers. Anyone with experience using Snowflake in this context?
Has anyone looked into using native billing dashboards provided by the API vendors themselves? I know OpenAI has a dashboard for spend, but it's pretty basic. Is there anything similar for Cohere or Hugging Face? Wondering if integrating those with a central tool might be simpler.
I'm right there with you on this. We've been using a combination of Prometheus and Grafana in our setup for tracking costs. It was definitely a challenge to get everything parsed and aggregated correctly, but it saved us quite a bit once it was running smoothly. The granularity is worth it, but yeah, takes some dev time to get right.
We've been using a combination of Prometheus + Grafana, and while it's definitely not the simplest approach, it does give us a lot of flexibility in terms of visualization and alerting. Parsing the billing data was a bit of a pain initially, but we wrote some scripts that handle this periodically, and it’s been working pretty well for us.
We've integrated a combination of FinOps platforms and custom scripts. We use a tool called Cloudhealth for overarching cloud spend analytics, but we complement that with some Python scripts to get more granular data directly from the LLM API billing endpoints. Yes, it's a bit of a patchwork, but it gives us both high-level and detailed insights.
For those using AWS heavily, have you tried utilizing AWS Cost Explorer with custom reports? While it doesn't give granular details like per model in real-time, you can get a better overall picture monthly or weekly. It might not be perfect for everyone, but it's less work than bespoke solutions if you're already in AWS.
Has anyone tried using tools like CloudZero or MetricFire? I heard they're really user-friendly for tracking spend across various services, but I'm curious if they handle AI/ML API costs without too much hassle. Also, any insights on how they compare cost-wise to the more traditional setups like Datadog?
I've been in a similar situation, and we ended up integrating our billing insights into a centralized Elasticsearch instance. It involved setting up pipelines to ingest billing data, but we gained more flexibility on the reporting side without incurring much overhead. Combining it with Kibana for visualization made a world of difference.
I'm curious about the FinOps platforms you mentioned. Are there any specific ones you'd recommend that offer flexibility for both cloud infrastructure and AI API management? Balancing cost and functionality is always a challenge for us!
I've had a similar issue with cost tracking as we've scaled our use of various LLM providers. We've ended up using a combination of custom scripts and Google BigQuery to aggregate and analyze the data from different sources. It does require maintaining the scripts, but the flexibility we get from custom queries on BigQuery for detailed spend analysis is worth it.
Anyone here tried CloudZero? It's supposed to offer real-time tracking of cloud costs with pretty good granularity. I'm curious about its applicability specifically for AI services—wondering if anyone's had success or faced challenges with it?
We've been using Nudg.io for a while now, which is a bit like Datadog but specifically tailored for AI/ML observability. It provides native integrations with major LLM providers and helps visualize cost trends per API call. It's definitely cheaper than trying to roll our own solution, although setup isn't entirely plug-and-play.
