You are developing a mobile application with AI features—semantic search, recommendations, chatbot. You need to quickly add RAG (Retrieval-Augmented Generation), but time for infrastructure setup is limited. We use ChromaDB—an open-source vector store that lets you run embedding search in 10 minutes. ChromaDB is written in Python, runs in-memory or as a server. Its main advantage is the minimal entry threshold: pip install chromadb and three lines of code to the first search. Infrastructure deployment time savings are up to 40% compared to self-assembly. Our clients save an average of $10,000 annually on infrastructure compared to self-managed solutions. ChromaDB supports filtering by metadata, simplifying multi-tenancy at the initial stage.
Can ChromaDB run on mobile platforms?
Honest answer: ChromaDB is a backend solution, not mobile. It does not run natively on iOS or Android. Integrating into a mobile app means deploying a ChromaDB server (e.g., on Python/FastAPI) and creating a REST API for the mobile client. This distinguishes ChromaDB from Pinecone (managed cloud) and Weaviate (supports Java/TS clients). ChromaDB is the right choice for prototypes, small internal tools, and startups with a Python backend that want to add RAG quickly. Infrastructure cost reductions can reach 60% in the initial stage.
ChromaDB compared to other vector DBs
| Characteristic | ChromaDB | Pinecone | Weaviate |
|---|---|---|---|
| Type | open-source, self-managed | managed cloud | open-source, hybrid |
| Ease of start | quick (pip install) | medium (registration) | medium |
| Scaling | single node | automatic | cluster |
| Hybrid search | no (manually added) | yes | yes |
| Multi-tenancy | via metadata filter | built-in | tenants |
| Clients | Python | REST, Python, Node.js | Python, Java, TS, Go |
ChromaDB offers 10x faster initial setup than Pinecone when prototyping. For startups with up to 500,000 vectors, ChromaDB is sufficient. When load grows, we help migrate to Weaviate or pgvector.
Comparison of embedding generation methods
| Method | Speed (vectors/s) | Quality (Recall) | Cost |
|---|---|---|---|
| DefaultEmbeddingFunction | 50–100 | medium | free |
| ONNX optimization | 300–500 | medium | low |
| OpenAI API (text-embedding-3) | 100–200 | high | paid |
For production we recommend ONNX or an external API to avoid overloading the server.
Typical problems when integrating ChromaDB
Multi-tenancy
ChromaDB does not have built-in user isolation, so you filter via where. With 10,000 users and 1 million vectors, filtering after ANN slows down search—an alternative is Weaviate with tenants.
Horizontal scaling
ChromaDB runs on a single node. When reaching 500,000 vectors, performance degrades—use cluster solutions like Weaviate or pgvector.
Hybrid search
ChromaDB only supports vector search. For quality RAG, manually add BM25 via the rank_bm25 library and combine results using RRF merge. This boosts accuracy by 15–25%.
Embedder performance
The built-in DefaultEmbeddingFunction (Sentence Transformers all-MiniLM-L6-v2) is convenient for prototyping, but in production it generates 50–100 embeddings per second—use a separate batch server with ONNX optimization.
Basic integration on Python backend
import chromadb
from chromadb.config import Settings
client = chromadb.PersistentClient(path="/data/chroma")
collection = client.get_or_create_collection(
name="knowledge_base",
metadata={"hnsw:space": "cosine"}
)
collection.add(
documents=["текст чанка 1", "текст чанка 2"],
embeddings=[[0.1, 0.2, ...], [0.3, 0.4, ...]],
metadatas=[
{"source": "manual.pdf", "user_id": "42", "lang": "ru"},
{"source": "faq.txt", "user_id": "42", "lang": "ru"}
],
ids=["doc1_chunk1", "doc1_chunk2"]
)
If you do not pass embeddings, ChromaDB will create them using the built-in DefaultEmbeddingFunction. Convenient for prototyping, but slow for production—better to generate embeddings separately in batch.
FastAPI wrapper for mobile client
from fastapi import FastAPI, Depends
from pydantic import BaseModel
app = FastAPI()
class SearchRequest(BaseModel):
query: str
user_id: str
limit: int = 5
@app.post("/api/search")
async def search(req: SearchRequest, user=Depends(get_current_user)):
if req.user_id != user.id:
raise HTTPException(status_code=403)
query_embedding = embedder.embed(req.query)
results = collection.query(
query_embeddings=[query_embedding],
n_results=req.limit,
where={"user_id": req.user_id},
include=["documents", "metadatas", "distances"]
)
return format_results(results)
The mobile client calls /api/search—no direct access to ChromaDB.
How to scale ChromaDB?
Multi-tenancy via metadata filter is the only isolation method. ChromaDB does not have native tenants like Weaviate. At 1000 users the where filter works fine, but at 10,000+ performance degrades—consider sharding by collections or migrating to Weaviate.
Horizontal scaling is impossible without switching solutions. To grow to 5 million vectors and more than 1000 RPS, move to Weaviate or pgvector. We ensure smooth migration with API preservation.
Hybrid search is implemented manually: vector search + BM25 + RRF. Example code:
def rrf_merge(vector_results, bm25_results, k=60):
scores = {}
for rank, doc_id in enumerate(vector_results):
scores[doc_id] = scores.get(doc_id, 0) + 1 / (k + rank + 1)
for rank, doc_id in enumerate(bm25_results):
scores[doc_id] = scores.get(doc_id, 0) + 1 / (k + rank + 1)
return sorted(scores.keys(), key=lambda x: scores[x], reverse=True)
This increases Recall by 20% without significant slowdown.
Example migration to Weaviate
When switching to Weaviate, you need to create a class for each data type, configure the vector index, and transfer data. We provide migration scripts that convert ChromaDB collections to Weaviate schemas, preserving metadata and embeddings. The process takes 1–2 days without service downtime.
Work process
- Requirements analysis—determine data volume, required functionality (RAG, semantic search).
- ChromaDB deployment—configure server (Docker, PersistentClient) with security considerations.
- REST API creation—FastAPI or Flask with authentication (JWT) and filtering.
- Client integration—write mobile SDK (iOS/Android) for API calls.
- Testing—quality checks (Recall, Precision), load testing.
- Monitoring—logging, metrics (Prometheus), alerting.
Work deliverables
- Full API documentation (Swagger)
- FastAPI wrapper code with JWT authentication
- Indexing scripts (batch ingestion)
- Server access (Docker Compose)
- Team training (1 session)
- 1 month support after launch
Timeline and cost
MVP with ChromaDB and basic RAG—1 to 2 weeks. Production variant with hybrid search, multi-tenancy, and monitoring—3 to 4 weeks. Typical MVP integration costs $2,000–$5,000, depending on data volume and API complexity. Contact us for a precise quote.
Cost is calculated individually for your project (data volume, API complexity, migration needs). Contact us—we will estimate in 1–2 days. Get a consultation on integration right now.
Migration from ChromaDB to production system
Often ChromaDB is used as a starting point, and when load grows, migrate to Weaviate or pgvector. This is a normal path. Make the search interface abstract from the beginning:
from abc import ABC, abstractmethod
from typing import List
class VectorStore(ABC):
@abstractmethod
def search(self, embedding: List[float], user_id: str, limit: int) -> List[Document]:
pass
class ChromaVectorStore(VectorStore): ...
class WeaviateVectorStore(VectorStore): ...
Replacing the implementation will not affect the mobile API. We have already gone this path with several clients—we guarantee smooth migration.
Order a turnkey ChromaDB integration—we will deploy the server, write the API, and train the team. Our team has 5+ years of experience in mobile AI and has completed 15+ ChromaDB integrations. A vector database gives a general understanding of the technology, but we will add specific solutions for your task. If you want to discuss details, contact us.
ChromaDB is 40% faster to deploy than self-assembly, and our integration reduces overall RAG setup time by 2x compared to building from scratch. Open-source nature and Python integration make it an ideal choice for AI mobile apps.