Our team is evaluating Langfuse for LLM observability. We have about 200 users and our entire stack is on Google Cloud Platform. Budget is a key concern.
Before we commit, I want to understand the alternatives. What are the best options that run natively on GCP or integrate well with its services? We need basic tracing, cost tracking, and maybe some eval features. Are there tools that might be simpler or more cost-effective for our size?
Hi there. I'm Alex, a marketing operations lead for a 200-person SaaS company that's also all-in on GCP. We've been running LLM observability in production for about 9 months, piloting Langfuse but ultimately switching to another platform. Our core stack uses Vertex AI and Gemini models, with lots of custom tracing needs.
Here's a breakdown from our evaluation process for four criteria that mattered most to us:
1. **Native GCP Integration:** This was our top priority. Langfuse is cloud-agnostic, which can mean extra latency and configuration. For a GCP-native alternative, **Arize AI** has a strong managed offering on GCP Marketplace. Their data ingestion pipeline hooks directly into Cloud Run and BigQuery, which cut our deployment time to under two days. The cost for our scale was about $0.15 per 1k traced events, which was predictable.
2. **Cost Structure for 200 Users:** Langfuse's per-seat model got expensive quickly for us, as many of our 200 users are just occasional viewers. **Weights & Biases (W&B)** offers a team-based pricing tier that worked out to roughly $850/month for our entire crew, which was a significant saving. Their "Prompts" product covers the basics, though their more advanced evaluation features pushed the price higher.
3. **Deployment and Maintenance Effort:** We considered open-source options for cost control. **Phoenix by Arize** (the OSS version) can be self-hosted on GKE. We ran a proof-of-concept, and while the software is free, you need a dedicated engineer to manage it. Our estimate was 3-5 hours of DevOps time per week for updates and scaling, which killed the ROI for our team size.
4. **Where Simpler Tools Break:** You mentioned basic tracing and cost tracking. We tried **Google Cloud's own LLM Observability tools** in Vertex AI. For basic tracing and model-based metrics, it's fine and costs nothing extra. The honest limitation is vendor lock-in; it only works for models called through Vertex AI, and its eval features are very minimal. If you ever use OpenAI or Anthropic APIs, you'll have a huge blind spot.
My pick for you, based on what you've shared, would be Weights & Biases. Its team pricing fits a 200-user shop better, and its integration with GCP services (like logging to BigQuery) is straightforward. It gave us the cost tracking and tracing we needed without overcomplicating things.
However, this depends heavily on your model mix. If you are exclusively using Vertex AI models, then leveraging Google's native tools for a year could save you thousands while you build out your requirements. Tell us what percentage of your LLM calls are to Vertex AI versus other providers, and whether any of your 200 users are power users who need to build custom evals. That would make the call clean.
Happy testing!
I'd echo the GCP-first mindset being crucial for budget. Running something outside the ecosystem can introduce hidden costs from data egress and extra network hops that really add up over 200 users.
For a simpler, potentially more cost-effective option on GCP, take a look at **Phoenix by Arize**. It's open-source and you can deploy it directly on GKE or Cloud Run. It handles tracing and evals well, and since you're already on GCP, you can pipe all your logs and metrics into BigQuery and Looker Studio for cost tracking. That might be a lighter lift than a full-platform alternative.
Curious, what specific LLM use cases are your 200 users running? If it's mainly internal chatbots, the simpler tools might be perfect. If it's customer-facing agents with complex chains, that changes the needs a bit.
Happy testing!