Perplexity API Alternative

Why build your own RAG instead of using Perplexity's API

Perplexity's API is a convenient, ready-made search-plus-answer endpoint — but it's a closed stack: fixed models, international billing, and no way to point it at your own private documents. For Indian teams building retrieval-augmented generation (RAG) — support bots, internal search, research assistants — unoblox's LLMs, embeddings, and reranker give you the same shape of product with your own knowledge base and ₹-native billing.

RAG in four steps

1. Embed your documents with Qwen3-Embedding (₹0, native /v1/embeddings).
2. Store the vectors in Postgres+pgvector, Pinecone, or Weaviate — your choice, your data.
3. Retrieve and rerank the top matches for a query using Qwen3-Reranker (also ₹0, native /v1/rerank).
4. Synthesize an answer by passing the retrieved context to GPT-4o, Claude, or DeepSeek.

from openai import OpenAI

client = OpenAI(
    api_key="ub-gw-...",
    base_url="https://api.unoblox.ai/v1"
)

# 1. Embed docs (one-time, ₹0)
embedding = client.embeddings.create(
    model="qwen/Qwen3-Embedding-0.6B",
    input=["Your knowledge base text..."]
)

# 2. Retrieve similar docs from your own vector store
# 3. Rerank top candidates via /v1/rerank (₹0)
# 4. Synthesize the answer
response = client.chat.completions.create(
    model="openai/gpt-4o",   # ₹252 / ₹1008 per 1M tokens
    messages=[{"role": "user", "content": f"Context: {docs}\nQuestion: {q}"}]
)

What it costs, in ₹

Step	unoblox	Notes
Embed 1,000 docs	₹0	Qwen3-Embedding free tier
Rerank top candidates	₹0	Qwen3-Reranker, native /v1/rerank
Synthesize an answer	₹8–₹25 per query	Depends on model and context size
10,000 queries/month	roughly ₹80k–₹250k	All-in, one GST invoice

Where unoblox wins over Perplexity's API

• You own the knowledge base. Add private documents, proprietary data, or live feeds — Perplexity's API can't search your internal wiki.
• Any model, not one stack. Swap between GPT, Claude, DeepSeek, and Qwen for synthesis without changing your retrieval layer.
• ₹-native billing. One GST invoice, input-tax credit eligible, no international payment method needed.
• A real reranking step. Qwen3-Reranker sits natively on the same key, so retrieval quality doesn't depend on raw vector similarity alone.
• Full latency control. Embedding and retrieval run on your own infrastructure timeline; only synthesis calls out to the model.

When Perplexity's API still makes sense

If you need general open-web search-and-cite out of the box, with zero infrastructure to run, Perplexity's hosted product ships faster on day one. RAG on unoblox pays off once you need private data, model flexibility, or predictable ₹ costs at scale.

Frequently asked questions

Do you offer a ready-made search API like Perplexity's? Not yet — building RAG on our LLMs and embeddings is the current path. A /v1/search abstraction is on the roadmap.

How much latency does a query add? Roughly 50ms to embed the query, 100ms for vector search, and under 500ms to synthesize — around 700ms end to end, comparable to or faster than crawl-based search.

Can I support multi-turn conversations? Yes — pass prior turns to the synthesis call as normal chat history. Keep embedding lookups single-query for speed.

Should I use RAG or fine-tuning? RAG suits frequently updated knowledge (docs, news, policies). Fine-tuning suits domain reasoning style. Many production systems on unoblox use both.

Do you crawl the web for me? No — pair an external crawler (Firecrawl, Apify, or your own) with unoblox for embeddings and synthesis; we handle the AI layer, not the crawling.

Get started in rupees → https://unoblox.ai/sign-in