Having to evaluate LLM observability platforms for a new RAG project. My primary requirement is a clear, attributable audit trail for debugging retrieval and generation steps. The compliance team will ask how we're logging sensitive data handling, so data governance features are non-negotiable.
I've narrowed the field to LangSmith, Arize, and Helicone based on market presence, but their documentation is heavy on features and light on concrete security and operational details I need.
My breakdown of critical needs for a RAG pipeline:
* **Trace Segmentation**: Ability to automatically and clearly separate retrieval (query, source chunks, scores) from generation (prompt, completion, tokens). Lineage is key.
* **Data Handling & Retention**: How is data at rest encrypted? What are the programmatic data retention and deletion controls? Is there a clear data processing agreement?
* **Access Controls**: Granular, project-level RBAC. Who can see which dataset or trace? Are API keys scoped?
* **Audit Logging for the Platform Itself**: Can I see who accessed a specific trace or who modified an evaluation dataset? This is for internal compliance.
From an initial review:
* LangSmith is built by LangChain, so deep integration is assumed, but I need to understand their vendor risk profile.
* Arize comes from the traditional ML observability space, which may mean stronger governance.
* Helicone emphasizes cost tracking and simple proxies, but I'm unsure about its maturity for enterprise data governance.
Has anyone conducted a formal security or compliance review of these tools, specifically for RAG workloads? I'm looking for experiences with their actual audit logs, data residency options, and how they handle PII within traces. SOC 2 Type II reports are a baseline requirement.
Where is your SOC 2?
I'm the lead AI platform engineer at a regulated financial services firm (~1000 employees), responsible for deploying and scaling several internal RAG pipelines for document intelligence and customer support. We run LangChain in production for orchestration and have evaluated all three platforms under pressure from our security and compliance teams.
**Core Comparison: LangSmith vs Arize vs Helicone for Compliant RAG**
* **Trace Segmentation & Lineage:** LangSmith's automatic trace segmentation into clear "Retrieval" and "LLM" steps is superior for RAG debugging. You can see the exact retrieved chunks and similarity scores inline. Arize requires more manual instrumentation to achieve the same visibility, while Helicone's traces are simpler and more focused on generic request/response logging, lacking built-in RAG-specific structure.
* **Data Governance & Retention Controls:** Arize leads here for enterprise compliance. Data at rest is encrypted with customer-managed keys (BYOK) in their enterprise plan, and they offer programmatic data retention policies and a detailed data processing addendum (DPA). LangSmith's data governance is improving but is more platform-defined; data is encrypted at rest, but you have less granular control over retention periods programmatically. Helicone uses standard cloud encryption; their data retention is more basic and suited for less stringent requirements.
* **Access Controls & Audit Trail:** Arize provides project-level RBAC, dataset-level permissions, and an audit log for platform actions (who viewed a trace, modified a prompt). LangSmith has project-based access and scoped API keys, but its internal audit logging is not as developed. Helicone's access controls are simpler, generally at the organization level, making it less suitable for multi-team environments with sensitive data segregation needs.
* **Real Pricing & Hidden Costs:** Helicone is the cheapest for pure cost-per-token logging, with a simple ~$0.25/1M tokens model. LangSmith is priced per-trace (a trace includes all RAG steps), which can become expensive at high volume; budget ~$500/month for moderate use. Arize is the most expensive, starting at several thousand per month, but includes their full suite (monitoring, eval, tracing). The hidden cost for Arize and LangSmith is the engineering time needed to integrate their SDKs fully; Helicone is a proxy, so integration is a few hours.
**Your Pick**
For your stated needs - clear audit trail, compliance, and granular data governance - I recommend Arize, assuming budget is secondary. If budget is constrained but you still need strong RAG trace segmentation, LangSmith is the alternative, but you must confirm with their sales that their DPA and retention controls meet your compliance team's specific checklist. Tell us your projected monthly trace/query volume and whether you need self-hosted/on-prem options to make the call clean.
Trust but verify.
You're asking the right questions on data governance. LangSmith's default trace storage uses their cloud. For *true* control, you need their "bring your own storage" option, which sends traces to your S3/GCS bucket.
It's a cost multiplier they don't shout about: you pay for their platform *and* your cloud storage/egress. Our monthly bill jumped 30% when we enabled it for compliance. Their RBAC is project-level, but API keys aren't scoped - a key for project A can access B if the user has permissions there.
Arize lets you self-host the whole thing, which is a different ops burden but clearer on the data boundary.
show the math