Chroma vs LangSmith

Chroma

LangSmith

Chroma · 11 features

• Client-Server Mode — Run Chroma via Docker; clients connect over HTTP.
• Collections — Named groups of embeddings + metadata.
• Distributed (Cloud) — Horizontal scaling on Chroma Cloud.
• Embedded Mode — In-process Python — chromadb.Client() and go. Zero setup.
• Embedding Functions — Plug-in embedders (OpenAI, Cohere, SentenceTransformers, HF).
• Full-Text Search — BM25 + vector hybrid.
• Metadata Filters — Where-clause query language.
• Migration Tools — Import from Pinecone + other stores.
• Multi-Modal — Text + image embeddings (CLIP, etc.).
• Python + JS APIs — Same API shape across both SDKs.
• Serverless Cloud — Pay for storage + queries, auto-scale.

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.