Neureus vs Pinecone

The Full RAG Stack vs.
Just a Vector Database

Pinecone is a best-in-class vector store. But to build RAG you still need an embedding model, a chunking pipeline, an LLM, retrieval logic, and glue code. Neureus ships all of it — vector storage included on every plan, starting free.

What you need besides Pinecone to build RAG

Pinecone stores vectors. The rest is on you to build and pay for.

Pinecone

Vector storage

$70+/mo

You build

Embedding generation

OpenAI: $0.13/1M tokens

You build

Document chunking

LangChain or custom code

You build

Retrieval + context formatting

Custom application code

Separate

LLM for answers

OpenAI / Anthropic costs

Reality

Total RAG stack cost

$150–500+/mo before features

vs. Neureus: 2 API calls, $0/mo (free tier), $29/mo (Builder)

Five steps vs. two calls

Pinecone RAG — 5 steps (Python)

# Step 1: Generate embedding (OpenAI call)
embedding = openai.embeddings.create(
    input=text, model="text-embedding-3-small"
).data[0].embedding

# Step 2: Upsert to Pinecone
index.upsert(vectors=[{
    "id": doc_id,
    "values": embedding,
    "metadata": {"text": text}
}])

# Step 3: Generate query embedding
query_emb = openai.embeddings.create(
    input=question, model="text-embedding-3-small"
).data[0].embedding

# Step 4: Retrieve from Pinecone
results = index.query(
    vector=query_emb, top_k=5, include_metadata=True
)
context = "\n".join([r.metadata["text"]
                      for r in results.matches])

# Step 5: Call LLM with context
response = openai.chat.completions.create(
    messages=[{"role": "user",
               "content": f"Context: {context}\n\n{question}"}]
)

Neureus RAG — 2 API calls

# Step 1: Ingest
# (auto-chunks, auto-embeds, stores in Vectorize)
curl -X POST https://app.neureus.ai/rag/ingest \
  -H "Authorization: Bearer nr_key" \
  -H "Content-Type: application/json" \
  -d '{"url": "https://your-docs.com/page"}'

# Step 2: Query
# (retrieves, formats context, calls LLM,
#  returns answer + sources)
curl -X POST https://app.neureus.ai/rag/query \
  -H "Authorization: Bearer nr_key" \
  -H "Content-Type: application/json" \
  -d '{
    "query": "What is the cancellation policy?",
    "model": "@wai/llama-3.3-70b"
  }'
# { "answer": "...", "sources": [...] }

Pricing comparison

Plan	Pinecone	Neureus
Free	Serverless (very limited capacity)	500 Neurons/mo, 50 RAG documents
Entry paid	$70/mo — 1 index only	$29/mo — unlimited RAG + agents + workflows
Standard	$125+/mo — additional indexes	$99/mo — BYOK, SSO, RBAC, MCP server
Also need	+ OpenAI Embeddings + LLM costs + app hosting	Nothing — embeddings, LLM routing, and edge hosting included

Full feature comparison

Feature	Pinecone	Neureus
Vector storage	✓	✓
Embedding generation	✗ (bring your own)	✓ (Workers AI, automatic)
Document chunking	✗ (build it)	✓ (auto-chunking on ingest)
RAG query pipeline	✗ (build it)	✓ (POST /rag/query)
LLM routing (10 providers)	✗	✓
Agent framework	✗	✓
Workflow engine	✗	✓
Auth + multi-tenancy	✗	✓
Free tier	Serverless (limited)	500 Neurons/mo (50 documents)
Starter paid plan	$70/mo (1 index)	$29/mo (unlimited RAG + agents)
Global edge / 0 cold starts	Hosted regions	300+ CF locations, 0ms cold start

When Pinecone is still the right choice

Massive scale: If you need hundreds of millions of vectors with custom HNSW parameters and dedicated pod infrastructure, Pinecone's enterprise tier handles scale that Cloudflare Vectorize doesn't yet match.
Search products (not RAG): If you're building semantic search, recommendation systems, or multi-modal vector search (images, audio), Pinecone's query flexibility and filtering is more powerful.
ML team control: If your ML team wants explicit control over index sharding, replication strategy, and ANN algorithm parameters, Pinecone's lower-level API is the right tool.
Already on Pinecone with large stores: If you have millions of vectors already indexed, migration cost may outweigh the savings.

The Full RAG Stack vs.Just a Vector Database

What you need besides Pinecone to build RAG

Five steps vs. two calls

Pricing comparison

Full feature comparison

When Pinecone is still the right choice

RAG without the vector DB management

The Full RAG Stack vs.
Just a Vector Database