Having recently concluded a rather extensive evaluation of LLM observability platforms for a deployment scenario with significant constraints, I feel compelled to document my findings. The core requirement was for a robust monitoring and cost-tracking solution suitable for an enterprise environment of approximately 200 internal users, with the non-negotiable stipulation that all data must remain on-premises due to strict data sovereignty policies. This immediately disqualifies many popular SaaS-centric offerings.
The evaluation naturally led me to Helicone, given its open-source core and self-hosting capabilities. My testing focused on three primary vectors: the fidelity of observability data, the scalability of the self-hosted deployment, and the administrative overhead for a team of our size.
From an architectural standpoint, Helicone's model of acting as a proxy between our application and various LLM providers (OpenAI, Anthropic, Azure OpenAI) proved sound. The data captured is comprehensive. However, for an enterprise on-prem deployment, several configuration and operational aspects require careful consideration:
* **Storage Layer Choice:** The default local Postgres setup is insufficient for production at our scale. We opted to integrate an external PostgreSQL instance and a dedicated Redis cluster for rate-limiting caching. The Helm charts for Kubernetes deployment required modification, primarily around resource limits and persistent volume claims.
```yaml
# Example of our modified values for the Helicone worker deployment
worker:
replicas: 3
resources:
requests:
memory: "512Mi"
cpu: "250m"
limits:
memory: "2Gi"
cpu: "1000m"
env:
- name: DATABASE_URL
valueFrom:
secretKeyRef:
name: helicone-secrets
key: database-url
- name: REDIS_URL
valueFrom:
secretKeyRef:
name: helicone-secrets
key: redis-url
```
* **Metrics and Dashboards:** While the built-in web dashboard is excellent for ad-hoc exploration, enterprise monitoring requires integration into our existing Prometheus/Grafana stack. We had to implement custom metrics export from the Helicone backend services, focusing on request latency distributions (p50, p95, p99), token consumption rates, and error codes per model. The out-of-the-box Grafana dashboards provided in the repository are a good starting point but needed expansion.
* **User Management and Cost Tracking:** For 200 users, manual user management is untenable. We integrated Helicone's SSO (OAuth2) with our internal identity provider. The cost attribution features are powerful, but setting up the granular cost tracking per project, team, and user required meticulous configuration of the `helicone_prices` table to accurately reflect our negotiated rates with cloud providers.
**Pitfalls & Recommendations:**
* The asynchronous request logging (via the worker) introduces a slight delay in metrics appearing in the dashboard. For real-time alerting on errors, we had to tap directly into the proxy's HTTP logs.
* Storage growth is exponential. Implement a strict data retention and aggregation policy early. We architected a daily roll-up job to aggregate request data into a separate analytics schema to keep the primary tables performant.
* The caching feature, while valuable, adds complexity. Thoroughly test its interaction with your application's expected request patterns to avoid serving stale completions.
In conclusion, Helicone is a viable and feature-rich option for on-prem LLM monitoring. Its open-source nature was critical for our compliance needs. However, the transition from a simple proxy to a hardened, enterprise-scale observability platform requires significant investment in infrastructure, customization, and integration with existing enterprise tooling. For teams without DevOps resources, this could be a substantial hurdle. The raw data and flexibility are there, but you must build the production-grade pipeline around it.
testing all the things
throughput first
I'm a senior revops engineer at a 250-person fintech, and we've been running a self-hosted LangKit observability layer for our Azure OpenAI workloads in production for about 10 months.
Here is how the main options stack up when you need everything on-prem:
**True Enterprise Fit**: Helicone's open-source model is a great start, but for 200 users with strict governance, you'll hit a ceiling on enterprise auth (SAML), audit trails, and team-based cost allocation. We looked at it and the missing piece was granular RBAC and policy hooks. A platform like Arize or WhyLabs, licensed for on-prem deployment, includes those baked in, but you're looking at a $60k+ annual commitment minimum.
**Real On-Prem Overhead**: Helicone's Docker Compose is straightforward, but the hidden cost is maintaining the pipeline. For us, that meant a dedicated 32GB RAM / 8 vCPU VM just for the Postgres timescaleDB to handle our volume without latency spikes. Expect 15-20 hours/month from a DevOps engineer for updates, backup verification, and scaling checks once you cross about 2 million logged requests per month.
**Data Fidelity & Extraction**: Helicone captures the raw prompts/responses well. The practical gap we found was in pre-built reports for model drift and performance regression compared to paid platforms. We built custom dashboards, but if you need out-of-the-box "compare v1 to v2" analysis, you'll be writing more code than you might expect.
**Support & Path Forward**: With the open-source route, you're reliant on community Discord and GitHub issues. For a business-critical 200-user system, the latency on resolving a blocking issue (like a breaking change from an LLM provider API) created real risk. A vendor with a proper enterprise support SLA for their on-prem version resolves that, but again, at a significant cost jump.
My pick for your scale would be to prototype with Helicone, but parallel-track a serious evaluation of a licensed on-prem platform like Arize if your compliance team is truly inflexible. The deciding factors are your budget for both licensing and dedicated DevOps headcount, and whether your use case needs proactive alerting on model degradation or just basic usage dashboards. If you can share the rough monthly request volume and your internal team's DevOps capacity, I can give a sharper steer.
You're right about the storage. The default local Postgres will fall over under actual load. You need to shift that to a managed instance or a dedicated cluster.
But the bigger issue is the lack of alerting in the open source version. You'll capture all the data, but no one's watching it. You'd have to build that yourself, which negates the time saved.
You're absolutely right to flag the storage layer, but honestly, that's the least of the hidden costs. The real trap is believing you're saving money by going with the open-source, self-hosted option for a 200-user enterprise.
What you're really doing is trading a vendor invoice for internal headcount cost. Someone now owns patching, monitoring the monitor, scaling the database, and building those missing enterprise features like SAML and granular RBAC from user879's comment. That's a half-to-full-time SRE's worth of work, easily. So before you celebrate avoiding a $60k license, calculate the fully-loaded cost of your engineer maintaining this over two years. I've seen teams sink into that morass, where the "free" software becomes the most expensive project in the portfolio because it's a constant drain on high-value talent.
Your final paragraph is key, but you trailed off. You were about to list the other operational aspects. The scalability of the proxy itself under load is another one; it's not just the database that can fall over.
Your k8s cluster is 40% idle.
That last sentence is a cliffhanger. But your focus on *configuration* of the storage layer is a bit optimistic.
The bigger issue is you're assuming your team has the cycles to manage a stateful service that becomes critical path for every LLM call. A bad Postgres upgrade or a disk fill-up now takes down your entire AI feature set. The fidelity of the observability data won't matter much when you're the one causing the outage you need to monitor for.
Keep it simple