LangSmith vs Qdrant

LangSmith

Qdrant

LangSmith · 14 features

• Alerts — Threshold alerts on latency, cost, eval metrics.
• Annotation Queues — Human-review workflows for trace quality rating.
• Custom Dashboards — Aggregate metrics dashboards per project/tag.
• Datasets — Collect examples → use as eval sets or training data.
• Evaluations — LLM-as-judge, embedding similarity, custom Python evaluators, offline batch eval…
• LangChain Integration — Auto-trace any LangChain/LangGraph run with env var.
• LangGraph Integration — First-class trace + eval for LangGraph agents.
• LLM Tracing — Automatic trace every LLM call + tool call + chain step.
• OpenTelemetry Export — Export traces as OTLP to Datadog/Honeycomb/etc.
• Playground — Test prompts + models inline before deploying.
• Prompt Canvas — Visual prompt editor with live test + eval.
• Prompt Hub — Public + private prompt library with versioning.
• Self-Hosted (Enterprise) — Docker + k8s deployment in your infra.
• Threads + Sessions — Group traces into conversational sessions.

Qdrant · 13 features

• BYOC (Hybrid Cloud) — Managed Qdrant in your cloud account.
• Cloud Inference — Hosted embedding models for free tokens.
• Cluster Monitoring — Prometheus metrics + health.
• Collections — Typed collections with named vectors + payload schema.
• Distributed — Horizontal sharding + Raft replication.
• Hybrid Search — Sparse + dense + keyword in one query.
• Multi-Vector — Multiple vectors per point (text + image, etc.).
• Open Source — Apache 2.0 licensed.
• Payload Filters — Rich filter DSL with indexed fields.
• Quantization — Scalar + product + binary for memory reduction.
• RBAC — API-key scopes + roles.
• Snapshots + Restore — Backup + DR primitives.
• Sparse Vectors — BM25 + SPLADE sparse embeddings natively.