Sub-10ms embedding generation on Fireworks AI's optimized infrastructure, paired with MVS's sub-50ms vector search. The full query path — embed, search, return ranked results — completes in under 100ms. Serverless scaling from zero to burst, pay-per-use.

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Measurable impact from day one

What teams see after connecting Fireworks AI to Mixpeek

<10ms

Embedding latency

Fireworks AI generates embeddings in under 10ms with optimized CUDA kernels and quantized models

<100ms

End-to-end query time

From user input to ranked search results — embedding, vector search, and retrieval in a single round trip

Cold starts

Serverless endpoints maintain warm instances — no cold-start penalties for latency-sensitive applications

1000s/sec

Batch throughput

Process thousands of embeddings per second for bulk indexing without impacting real-time query performance

Pay-per-use

Serverless pricing

Scale from zero to burst automatically — no idle GPU costs, no reserved capacity to manage

<5 min

Integration setup

Fireworks API key, MVS collection, retriever config — production-ready search in under 5 minutes

The Problem

Real-time applications need embeddings fast — autocomplete suggestions, live content moderation, instant recommendations. Self-hosted models add 50–200ms of latency per request, and that's before you factor in cold starts, batch queuing, and network hops. Standard embedding APIs target throughput over latency, returning results in 30–100ms. For user-facing features where every millisecond counts, that overhead breaks the experience. And once you have the embeddings, you still need a vector search layer that can keep up.

The Solution

Fireworks AI delivers sub-10ms embedding inference using hardware-optimized serving infrastructure with custom CUDA kernels and intelligent model quantization. Mixpeek Vector Store matches that speed with sub-50ms p99 vector search latency. Together, they form a real-time pipeline: embed with Fireworks, search with MVS, retrieve with Mixpeek — all under 100ms end-to-end. Serverless deployment means you pay only for the embeddings you generate, with automatic scaling from zero to burst capacity.

Pipeline Architecture

Hover over each step to see how the components connect

Fireworks Embedding

Sub-10ms Inference

Call the Fireworks AI embedding API with text or multimodal input. Optimized CUDA kernels and model quantization deliver vectors in under 10ms with no cold starts.

Vector Upsert

MVS Collection

Upsert the embedding vector and metadata to a Mixpeek Vector Store collection. MVS indexes the vector immediately — searchable within seconds.

High-Throughput Indexing

Batch + Real-Time

For bulk indexing, Fireworks processes thousands of embeddings per second. Real-time queries run on separate capacity — batch workloads don't impact query latency.

Retriever Configuration

Feature Search

Configure a Mixpeek retriever with feature search, metadata filters, and optional reranking. The retriever handles query embedding, search, and result assembly.

Real-Time Query

<100ms End-to-End

User query → Fireworks embedding (10ms) → MVS vector search (50ms) → ranked results with metadata. The full round trip completes in under 100ms.

Serverless Scaling

Zero to Burst

Fireworks scales from zero to thousands of requests per second automatically. No GPU provisioning, no capacity planning — pay per embedding generated.

Fireworks AI Integration Deep Dive

Call the Fireworks AI embedding endpoint with your text or multimodal input. Fireworks returns a vector in under 10ms using their optimized serving stack — no cold starts, no batch queuing. Upsert the vector to a Mixpeek Vector Store collection with associated metadata. For indexing workloads, Fireworks supports high-throughput batch embedding that processes thousands of documents per second without sacrificing latency on concurrent real-time queries. Configure a Mixpeek retriever with feature search stages pointing at your MVS collection. At query time, embed the user's query with the same Fireworks model, send it to the retriever, and get ranked results back in a single API call. For latency-critical paths, use Fireworks' serverless endpoints which maintain warm instances and eliminate cold-start delays entirely.

Quick Start

fireworks_ai_mvs.py

import fireworks.client
from mixpeek import Mixpeek

# 1. Generate embedding with Fireworks AI (<10ms)
response = fireworks.client.Embeddings.create(
    model="nomic-ai/nomic-embed-text-v1.5",
    input="find similar product images"
)
vector = response.data[0].embedding

# 2. Upsert to Mixpeek Vector Store
client = Mixpeek(api_key="YOUR_API_KEY")
client.vector_store.upsert(
    namespace="products",
    vectors=[{
        "id": "prod_042",
        "values": vector,
        "metadata": {"category": "electronics", "sku": "X-100"}
    }]
)

# 3. Real-time search (<100ms end-to-end)
results = client.vector_store.search(
    namespace="products",
    vector=query_vector,
    top_k=20,
    filters={"category": "electronics"}
)

See the full API reference in the Vector Store docs.