Hey everyone, new here 👋 Been self-hosting a bunch of services for a while, but now my team's starting to use GPT-4 and Claude APIs for a couple of internal tools. The bill is creeping up and sometimes things just feel... slow.
I'm skeptical of big SaaS black boxes. What are you all using to actually see what's happening? I need to track latency per call, costs by project, and catch weird output. Open source or self-hosted options would be perfect, but I'll consider anything that gives real transparency. How are you handling this?
Self-host or die trying.
Hey OP, I run backend systems for a ~150 person SaaS company where we use both GPT-4 and Claude for various content generation and analysis features. We have these models integrated into several customer-facing workflows, so monitoring cost, latency, and output quality is mandatory.
1. **Fit:** Langfuse is built for teams instrumenting LLM apps. Its tracing is fantastic for debugging complex chains. Helicone is laser-focused on being a lightweight proxy for OpenAI/Anthropic calls, great for smaller teams that just need cost and latency dashboards fast.
2. **Real Pricing:** Langfuse's cloud tier is $25/month for 100k traced events, then $0.25 per 1k events after. Self-hosted is free. Helicone's cloud is free up to 1k requests/day, then starts at $20/month. The hidden cost is compute for self-hosting; you'll need a small Postgres instance and Redis.
3. **Deployment Effort:** For Helicone, you literally just change your API base URL and add an auth header. It's a 10-minute setup. Langfuse requires a bit more: you install an SDK and send traces, which took us an afternoon to instrument our main pipelines.
4. **Where It Breaks:** Langfuse can add noticeable overhead if you trace every single user message in high-volume, low-latency scenarios. Helicone's dashboard is simpler; you won't get the deep trace visualization for complex, multi-step LLM calls.
My pick is Langfuse if you have multi-step prompts or RAG and need to see exactly where in a chain things went wrong. Go with Helicone if you just need straightforward cost-per-project and latency charts with near-zero config. To decide, tell us if your "internal tools" are mostly simple API calls or complex chains, and roughly how many calls you make per day.
cost first, then scale
Hey! Been there with the creeping bill 😅
For your setup, I'd start with Helicone's open source version. You can self-host it in like 10 minutes with Docker. It's just a proxy, so you swap your API endpoint and boom - you get dashboards for latency per model and costs broken down by project. It's dead simple for internal tools.
I use it to spot weird output by tagging calls with the project name and a user ID. Then I can filter by high latency or cost and see the exact prompt/completion that caused it. No more guessing which script is burning cash.
The only catch is you'll need something else if you're doing complex multi-step LLM chains. For basic API calls, it's perfect.
Trial first, ask later.
Second the self-hosted Helicone for basic calls. That's our starting point too.
Its dashboards are fine, but you're right about the chain limitation. We hit that wall. For anything beyond a single request-response, we pipe the traces into a separate monitoring system. Lets us set alerts on custom metrics like cost spikes per user session.
The proxy overhead is negligible, but watch your log volume if you're debugging every single call. Can fill a disk fast.
Benchmarks or bust.
Oh, the log volume warning is really good. We're just starting and I hadn't even thought about that. What are you using for your separate monitoring system after Helicone? I'm worried about setting up something too complex.
And when you pipe traces, are you just sending the expensive or slow ones, or is it everything? Trying to figure out what we'd actually need to keep.
Welcome to the world of opaque AI API bills, I've been there. Starting with a self-hosted proxy like Helicone is the right move for immediate cost and latency visibility. It directly addresses your need to track calls by project.
The operational overhead everyone is hinting at is real. You mentioned being skeptical of SaaS black boxes. The irony is that you can easily build your own black box of logs with a proxy. I run my own Helicone instance and feed its metrics, not its logs, into a Prometheus stack. The key is to export only the structured numerical data - request count, token usage, latency histograms - using a custom exporter. This keeps volume manageable and lets you set Grafana alerts on cost per project spikes or P99 latency degradation.
For catching weird output, a proxy alone is insufficient. You need a sampling strategy. We send a 2% sample of all prompts and completions, tagged with metadata like project and user, to a separate object store. This gives us a queryable corpus for anomaly detection without the storage explosion. The rest of the trace data is discarded after metrics extraction.
Latency is a liability