Having been on the receiving end of more "evaluation framework" promises than I've had bad CRM demos, let's talk about actually trying to use these things. My team is building a RAG pipeline for sales data (contracts, comms, the usual mess) and we need to track hallucination rates, answer relevance, and latency. The current frontrunners in the shop are LangSmith and MLflow.
I've poked at both. LangSmith feels like it was built *for* this specific LLM/RAG use case, which is great until you hit the wall of their cloud pricing. MLflow is the open-source Swiss Army knife we already use for other ML models, so the appeal of one platform is obvious.
But here's the rub for a small, pragmatic team:
* **LangSmith:** Incredibly low-friction to start logging traces and evaluations from LangChain/LlamaIndex. The UI actually makes sense for inspecting chain steps. But it's yet another siloed service. Can it handle custom scoring functions that aren't just their baked-in metrics? What happens when we need to evaluate something that isn't from their ecosystem?
* **MLflow:** We own it. It plugs into our existing infra. The model registry is a plus. But setting up evaluation for LLM outputs feels like fitting a square peg in a round hole. The built-in LLM evals are... basic. Are we going to spend all our time writing glue code to make RAG traces visible?
For those who've been in the trenches: which one actually lets a 10-person team **ship** and **iterate** without becoming a full-time DevOps project? Is LangSmith's specialization worth the vendor lock-in and cost, or is biting the MLflow bullet and building our own tooling around it the lesser evil? I'm skeptical either is a complete solution.
been there, migrated that
I'm a finops lead at a 150-person B2B SaaS company; we run a hybrid data pipeline with Azure ML and AWS Bedrock, and I've managed the cost tracking for both our classic ML and new RAG evaluation systems in production for about eight months.
The comparison for your team boils down to whether you prioritize integrated, opinionated tooling or infra ownership and cost predictability.
1. **Monthly Cost Per Active User:** LangSmith's cloud pricing becomes a significant line item quickly. The base team tier starts at $119/month for up to 5 users, which is roughly $24/user/month, but active usage for a 10-person team evaluating frequently will push you into the $499/month "Scale" tier or higher. That's $50/user/month before compute costs. MLflow's cost is your Kubernetes cluster or VM spend; we run it on AKS for about $120/month total for the control plane, plus the cost of the backing database and blob storage for artifacts.
2. **Integration Effort for RAG-specific Telemetry:** LangSmith wins on out-of-the-box tracing. Adding the LangSmith callback to a LangChain pipeline took us under an hour to see spans, tokens, and costs. Recreating equivalent trace visibility in MLflow required writing custom logging calls to `mlflow.log_metric` and `mlflow.log_param` within each chain step, which was about three developer days of work.
3. **Flexibility for Custom Evaluation Logic:** MLflow has a clear advantage here. You can package any Python function as an `mlflow.evaluate` metric. We run a custom metric that checks for contract clause hallucination by cross-referencing a knowledge graph, which was straightforward to implement. With LangSmith, you can attach a custom evaluator, but it must conform to their `RunEvaluator` interface and the results live separately from their built-in metrics, which fragmented our dashboard.
4. **Data Sovereignty and Exit Cost:** MLflow runs in your VNet; your evaluation data, including prompt/response pairs, never leaves your environment. For sales contract data, this may be a compliance requirement. LangSmith is a SaaS; exporting your trace data for migration is possible via API, but reconstituting it into another system is a manual ETL project. Our team estimated a two-week effort to migrate if we needed to leave.
I recommend MLflow for your team, given your existing use, small size, and the sensitivity of sales data. The initial setup for LLM evaluation is higher, but you avoid perpetual SaaS fees and retain full control. To be sure, confirm whether you need to evaluate non-Python pipeline components and what your internal security policy says about sending prompt data to a third party.
Always check the data transfer costs.