What are ChromaDB's practical size limits and performance characteristics at scale?

ChromaDB does not impose hard document count limits, but practical performance degrades at different thresholds depending on storage mode, hardware, and HNSW configuration. Understanding these helps you plan capacity and know when to consider alternatives.

import chromadb
import time

client = chromadb.Client()
col = client.create_collection(
    "scale_test",
    metadata={
        "hnsw:space":           "cosine",
        "hnsw:construction_ef": 200,   # higher quality index
        "hnsw:search_ef":       100,   # higher recall at query time
        "hnsw:M":               32,    # more connections per node
    },
)

# Batch insert 50,000 documents
BATCH = 500
for i in range(0, 50_000, BATCH):
    col.add(
        documents=[f"Document about topic {j % 100}" for j in range(i, i+BATCH)],
        ids=[str(j) for j in range(i, i+BATCH)],
    )

print(f"Collection has {col.count()} documents")

# Measure query latency
start = time.perf_counter()
results = col.query(query_texts=["topic 42"], n_results=10)
elapsed = time.perf_counter() - start
print(f"Query latency: {elapsed*1000:.1f}ms")

# Memory footprint estimate:
# 384-dim float32 vectors: 384 * 4 bytes = 1.5 KB per doc
# 50K docs * 1.5 KB = ~75 MB just for vectors
# HNSW graph adds ~20-30% overhead → ~100 MB total for 50K docs

Memory rule of thumb: each 384-dim vector requires ~1.5 KB. A 1M document collection with 384-dim embeddings needs ~1.5 GB just for vectors, plus HNSW graph overhead (~25%). Plan memory accordingly when deploying the ChromaDB server.