💸

Memflare - Carbon AI Shutdown and Migration

RAG applications in

minutes.

3:00

Intelligent memory for AI. Build smarter, more contextual AI applications.

Create Collection

Upload Data

Process

Store

Query/Stream

API PIPELINE

Collection

{
  "name": "my_documents",
  "dimension": 768,
  "metric": "cosine",
  "userId": "user_123"
}

New

Context-aware AI

Add relevant context to your AI for more accurate and informed responses. Let us handle the complexity for you.

Coming soon

Offline Intelligence

Experience AI capabilities without an internet connection. Local processing ensures privacy and instant responses.

Beta

Vector Search Optimization

Lightning-fast document retrieval with our optimized vector search engine. Process millions of documents in milliseconds.

AI Infrastructure
On Your Terms

Run over 220+ powerful AI models complete with custom RAG and document processing. Upload your own documents and call our API to integrate into your existing workflows.

Data Privacy

Your data never leaves our infrastructure.

Advanced RAG

In-house RAG algorithms for accurate, context-aware responses.

High Performance

Optimized to reduce latency and improve performance.

Enterprise Ready

Built for scale with no limits. Anywhere.

How Memflare Works

Build powerful RAG applications with our high-performance vector database and AI integration.

Vector/File Database

Upload files or plain text without limits. Separate your data, and create as many collections as you want.

POST/v1/collections

{
  "name": "jfk_files",
}

Document Processing

We process documents automatically, and deploy dynamic algorithms based on your data.

POST/v1/collections/:collection_name/files

{
  "collection": "jfk_files",
  "document": { ... }
}

Semantic Search

Perform lightning-fast semantic searches with sub-50ms response times.

GET/v1/collections/:collection_name/query

{
  "query": "who killed jfk?",
  "collection": "jfk_files"
}

Chat Integration

Query your data with any LLM. All pricing is pass-through. Don't worry about managing multiple API-keys.

POST/v1/chat/completions

{
  "query": "who killed jfk?",
  "collection": "jfk_files"
}

* All endpoints support additional parameters and options to unlock more advanced functionality.

Less code. More power.

Half the code. Half the cost. Twice the performance.

Carbon AI

48 LOC

carbon-example.js

1import axios from 'axios';
2
3const carbonAI = axios.create({
4  baseURL: 'https://api.carbon.ai',
5  headers: { Authorization: `Bearer ${YOUR_API_KEY}` }
6});
7
8const openAI = axios.create({
9  baseURL: 'https://api.openai.com/v1', 
10  headers: { Authorization: `Bearer ${YOUR_LLM_API_KEY}` }
11});
12
13async function retrieveAndProcess() {
14  try {
15    const queryResponse = await carbonAI.post('/collections/my-collection/query', {
16      vector: [0.1, 0.2, 0.3, ...],
17      topK: 5,
18      includeMetadata: true
19    });
20
21    const results = queryResponse.data;
22
23    const prompt = `Here are the relevant documents:\n${results.vectors
24      .map((v, i) => `${i + 1}. ${v.metadata.name}: ${v.metadata.content}`)
25      .join('\n')}\n\nSummarize these documents.`;
26
27    const llmResponse = await openAI.post('/completions', {
28      model: 'gpt-4-turbo-instruct',
29      prompt,
30      max_tokens: 500,
31      temperature: 0.7
32    });
33
34    console.log(llmResponse.data.choices[0].text.trim());
35  } catch (error) {
36    console.error(error.response?.data || error.message);
37  }
38}
39
40retrieveAndProcess();
41

Memflare

18 LOC

memflare-example.js

1import axios from 'axios';
2
3const memflare = axios.create({
4  baseURL: 'https://memflare.com/api',
5  headers: { Authorization: `Bearer ${YOUR_API_KEY}` }
6});
7
8const response = await memflare.post('/v1/chat/completions', {
9  model: 'meta-llama/llama-3.2-3b-instruct:free',
10  messages: [
11    { role: 'user', content: 'What is the capital of France?' }
12  ],
13  collection_name: 'my_docs'  // Optional: Use context from collection
14});
15

Simple and Powerful API

Experience the simplicity of our REST API in realtime.

const response = await fetch('https://api.memflare.com/v1/collections', {
  method: 'POST',
  headers: {
    'Content-Type': 'application/json',
    'Authorization': 'Bearer YOUR_API_KEY'
  },
  body: JSON.stringify({
    name: 'my_collection',
    quality: 'maximum',
    technique: 'traditional'
  })
});

Output

Click "Run" to fetch a response from Memflare.

/v1/collections

Compare and Choose

See how Memflare stacks up against traditional vector databases.


Easy API Integration
Scalable Cloud Infrastructure
Advanced Vector Search
Real-time Updates
Cost-effective
AI Model Integration
Flexible Collection Management
Document-level Operations
Efficient Query System
Easy Data Cleanup
Open-source Flexibility
High Performance at Scale

Unleash the power of RAG

Crystal-clear pricing with our intuitive monthly cost calculator.

Query Operation

10,00010,000,000

Chunk Operations

1,0001,000,000

Upload Storage

0.1100

File Storage

Cost Breakdown

Query Cost:$0.00

Chunking Cost:$0.00

Upload Cost:$0.00

Total:

$0.00$0.00

Save $0.00•10% off

Coming Q1 2025

OfflineAI

Traditional Bureaucracy

As organizations grow, bureaucracy takes over. Critical information gets lost and communication slows to a crawl, crippling company-wide efficiency.

Stakeholders

Executive

Third Party

Legal

Management

Human Resources

Marketing

Sales

Engineering

Customer Service

Finance

Paralyzed Decisions

Bureaucracy kills agility when speed matters most.

Error Cascade

Complex chains multiply mistakes across departments.

Change Resistance

Rigid hierarchies block rapid market adaptation.

Resource Drain

Bloated management layers strangle efficiency.

AI-first Enterprise

Break down silos and accelerate communication with AI-powered intelligence that connects teams and streamlines workflows.

Instant Communication

AI eliminates bottlenecks by enabling direct, intelligent communication between teams.

Old way: Days of email chains and meetings.

Smart Automation

Automate routine tasks and decisions while maintaining human oversight where it matters.

Old way: Manual approvals at every step.

Marketing

Sales

Engineering

Human Resources

Customer Service

LLM

Finance

Management

Third Party

Legal

Research

Shareholders

Executive

Unified Knowledge

Central AI hub ensures consistent information sharing across departments.

Old way: Siloed information in departments.

Adaptive Organization

Real-time insights enable rapid response to market changes and opportunities.

Old way: Slow to adapt and evolve.

Trusted by Industry Leaders

Enterprise-Grade Security

Your data never leaves your infrastructure. Process sensitive information with complete confidence.

End-to-End Security

Air-gapped Operation for Maximum Protection

Complete isolation from external networks ensures maximum security for your sensitive data processing needs.

Zero Data Sharing

Complete Isolation

Your data remains exclusively within your control, and never mixed with other users' data.

Access Control

Full Ownership and Access Management

Maintain complete control over data access and AI system usage.

Data Visibility

Total Transparency and Auditability

Full visibility into all AI operations and data processing, with detailed audit trails and logging.

Product Roadmap

See what's coming next and track our progress as we build the future of AI infrastructure.

Drag to explore timeline

Q1 2025

completed

Local Model Support

Run popular open-source LLMs directly on your hardware

Llama 2 integration
Mistral support
CPU/GPU optimization

Q2 2025

in-progress

Developer Tools

Complete SDK and CLI for local AI development

Python SDK
Command-line interface
VSCode extension

Q3 2025

upcoming

Enterprise Features

Advanced features for large-scale deployments

Role-based access control
Audit logging
Custom model training

Q4 2025

planned

Cloud Hybrid Mode

Optional cloud connectivity for enhanced capabilities

Model synchronization
Distributed training
Cloud backups

Community Showcase

Unleash the power of memflare. Build without boundaries, deploy anywhere.

Offline Chat App

1240

A desktop chat application built with offline-ai and Electron.

by Sarah Chen

Code Assistant

892

VSCode extension for offline code completion and explanation.

by Alex Rivera

Document Analyzer

567

Local-first document analysis and summarization tool.

by Marcus Kim

Frequently Asked Questions

Get answers to common questions about Memflare's features, capabilities, and implementation.

What is Memflare's vector database?

Memflare is a high-performance vector database designed for AI applications, offering seamless integration with large language models. It provides efficient similarity search, real-time updates, and enterprise-grade security features.

How does pricing work?

We offer a flexible pay-as-you-go model with no upfront costs. Pricing is based on storage usage and query volume. Free tier includes 1M vectors and 100K queries per month. Enterprise plans with custom limits are available.

What's the maximum vector dimension supported?

Memflare supports vectors up to 4096 dimensions, suitable for most modern embedding models including OpenAI's text-embedding-ada-002 and other popular embedding models.

What integration options are available?

We provide SDKs for Python, JavaScript, Go, and Java. Our REST API enables easy integration with any programming language. We also offer direct integrations with popular AI frameworks and LLM platforms.

How does Memflare compare to other vector databases?

Memflare offers superior performance with sub-10ms query times, better cost efficiency, and easier scaling. Unlike competitors, we provide real-time updates, automatic indexing, and integrated AI capabilities without complex setup.

Is there a limit on collection size?

There's no hard limit on collection size. Our architecture automatically scales to handle billions of vectors while maintaining consistent performance. Enterprise plans can be customized for even larger workloads.

npm install @memflare/client

Start Building with Memflare

→Low Latency API

→Unlimited storage

→Enterprise-grade security

Deploy production-ready AI memory management with minimal latency.

Get API Key

$ curl -X POST https://api.memflare.com/v1/chat/completions \-H "Authorization: Bearer $MEMFLARE_API_KEY" ...

1import axios from 'axios'; 2 3const carbonAI = axios.create({ 4 baseURL: 'https://api.carbon.ai', 5 headers: { Authorization: `Bearer ${YOUR_API_KEY}` } 6}); 7 8const openAI = axios.create({ 9 baseURL: 'https://api.openai.com/v1', 10 headers: { Authorization: `Bearer ${YOUR_LLM_API_KEY}` } 11}); 12 13async function retrieveAndProcess() { 14 try { 15 const queryResponse = await carbonAI.post('/collections/my-collection/query', { 16 vector: [0.1, 0.2, 0.3, ...], 17 topK: 5, 18 includeMetadata: true 19 }); 20 21 const results = queryResponse.data; 22 23 const prompt = `Here are the relevant documents:\n${results.vectors 24 .map((v, i) => `${i + 1}. ${v.metadata.name}: ${v.metadata.content}`) 25 .join('\n')}\n\nSummarize these documents.`; 26 27 const llmResponse = await openAI.post('/completions', { 28 model: 'gpt-4-turbo-instruct', 29 prompt, 30 max_tokens: 500, 31 temperature: 0.7 32 }); 33 34 console.log(llmResponse.data.choices[0].text.trim()); 35 } catch (error) { 36 console.error(error.response?.data || error.message); 37 } 38} 39 40retrieveAndProcess(); 41

1import axios from 'axios'; 2 3const memflare = axios.create({ 4 baseURL: 'https://memflare.com/api', 5 headers: { Authorization: `Bearer ${YOUR_API_KEY}` } 6}); 7 8const response = await memflare.post('/v1/chat/completions', { 9 model: 'meta-llama/llama-3.2-3b-instruct:free', 10 messages: [ 11 { role: 'user', content: 'What is the capital of France?' } 12 ], 13 collection_name: 'my_docs' // Optional: Use context from collection 14}); 15

const response = await fetch('https://api.memflare.com/v1/collections', { method: 'POST', headers: { 'Content-Type': 'application/json', 'Authorization': 'Bearer YOUR_API_KEY' }, body: JSON.stringify({ name: 'my_collection', quality: 'maximum', technique: 'traditional' }) });

Easy API Integration

Scalable Cloud Infrastructure

Advanced Vector Search

Real-time Updates

Cost-effective

AI Model Integration

Flexible Collection Management

Document-level Operations

Efficient Query System

Easy Data Cleanup

Open-source Flexibility

High Performance at Scale