Hey everyone, I've been tasked with evaluating open-source LLM tracing tools for our engineering team. We're about 50 engineers, all on Google Cloud Platform, and we're starting to deploy more LLM features into production. I've done a few SaaS migrations before (like moving CRM platforms), but this observability space is new to me.
I'm looking for something that can handle scale and give us clear insights without being a nightmare to maintain. Our main needs are:
* **Latency breakdowns** to see which part of a chain is slowing things down.
* **Cost attribution** per project or team, since we're budgeting LLM API spends.
* **Solid anomaly detection** for sudden spikes in errors or latency.
I've seen names like Langfuse, Phoenix, and OpenLLMetry pop up. For those with hands-on experience, especially in a GCP environment:
* Which tool would you recommend for a team of our size?
* How was the setup and ongoing maintenance burden?
* Did you run into any specific limitations with GCP services?
I'm leaning towards open-source to avoid vendor lock-in and for cost control, but I'm open to hearing if a managed service is really worth it for a team like ours. Thanks in advance for any pointers.
Migration is never smooth.
I'm a platform engineer at a 300-person fintech. We migrated our entire analytics pipeline to LLMs last year, running 40+ LangChain and custom agents on GKE in GCP. I manage the observability stack.
Core comparison for the tools you listed:
1. **Setup effort on GCP** - OpenLLMetry was the lightest lift. It's OTel-based, so we deployed the collector to a GKE pod and pointed our apps at it. Langfuse required managing a separate Postgres DB and its server components. Phoenix was in the middle, but its Jupyter-centric design felt awkward for production piping.
2. **Maintenance burden** - For 50 engineers, Langfuse needs a dedicated half-day per month for updates and DB tuning. OpenLLMetry runs as a sidecar and updates with your app. Phoenix we treated as a dev tool, not a production trace store.
3. **Cost attribution clarity** - Langfuse wins here. Its UI breaks down token usage and cost per project out of the box. With OpenLLMetry, you must build those dashboards yourself in Grafana, which took us about 10 days of work to get right.
4. **Latency breakdown specificity** - OpenLLMetry gives you span-level details automatically. Langfuse requires you to instrument your chains with specific event tags to get the same granularity, which adds development time.
My pick is OpenLLMetry if your team is comfortable building some custom dashboards for cost reporting. It integrates natively with GCP's operations suite and scales with your existing infra. If you need polished cost reports and a dedicated UI immediately and can spare the maintenance, Langfuse is the alternative.
Tell me how your LLM apps are deployed (Cloud Run, GKE, VMs?) and if you have a dedicated platform team to manage the tool.
Ask me about hidden egress costs.
Great question, and you're right to focus on maintenance burden early - it's the silent killer for these tools. Since you're already on GCP, I have to add Dataline to your list for consideration, as it's built specifically as a managed service on Google Cloud and hits all your points.
I've seen teams get bogged down trying to self-host Langfuse's database for cost attribution at scale. For 50 engineers, you'll want that cost data clean and automatic, not something you're building custom dashboards for. The anomaly detection for error spikes is also something that's often an afterthought in OSS tools, requiring you to wire up your own alerts to Cloud Monitoring.
Given your team size and platform, a managed service might actually save you money versus the engineering hours spent tuning and maintaining an open-source stack. Have you calculated the total cost of ownership for self-hosting versus a managed option on GCP?