Why Graphlit?
Why developers choose Graphlit over DIY solutions and other memory platforms - TCO comparison, time savings, and competitive advantages
Graphlit is the data infrastructure layer for AI agents. Whether you're building with Mastra, Agno, Vercel AI SDK, or custom code, Graphlit handles the hard parts: ingesting data from 30+ sources, processing audio/video, storing semantic memory, and providing retrieval - so you can focus on your agent's logic and UX.
Building this infrastructure yourself means integrating 7+ services, maintaining sync pipelines, and staying current with AI models. Graphlit provides everything in one platform - and integrates with your existing agent frameworks via MCP.
The Hidden Cost of DIY
Most developers underestimate what it takes to build production-grade semantic memory. Here's what you're really signing up for:
DIY Stack Requirements
Infrastructure Services (you integrate and maintain):
Vector database (Pinecone, Weaviate, Qdrant) - $70-200/month
Document parser (Unstructured, LlamaParse) - $99-299/month
Audio transcription (Deepgram, AssemblyAI) - $50-200/month
Entity extraction (spaCy, custom NLP) - build yourself
Object storage (S3, Azure Blob) - $30-100/month
Search index (Elasticsearch) - $95-500/month
Embedding service (OpenAI, Cohere) - $20-100/month
Orchestration (LangChain, custom code) - maintain yourself
Development Time (before you ship):
Note: Times shown are person-weeks of effort. With multiple developers or AI coding assistants (Cursor, Windsurf, etc.), calendar time can be shorter - but the complexity and coordination overhead remains.
Basic RAG (file upload + vector search):
Vector DB integration: 1-2 weeks
Document parsing pipeline: 2-3 weeks
Embedding generation: 1-2 weeks
Search API: 1-2 weeks
Subtotal: 5-9 person-weeks (2-3 months calendar time with team)
Production-Ready RAG (multi-tenant, observability):
Multi-tenant architecture: 3-4 weeks
Logging & observability: 2-3 weeks
Security & encryption: 2-3 weeks
Usage tracking: 1-2 weeks
Subtotal: 13-21 person-weeks (3-5 months calendar time with team)
Graphlit-Equivalent (30+ feeds, audio/video, workflows):
OAuth connectors (30+ sources @ 1-2 weeks each): 30-60 weeks
Automatic sync infrastructure: 3-4 weeks
Audio transcription + diarization: 2-3 weeks
Video processing: 3-4 weeks
Custom workflows engine: 4-6 weeks
Knowledge graph: 4-6 weeks
Publishing features: 2-3 weeks
Subtotal: 48-86 person-weeks (12-20 months calendar time with team)
Realistic Timeline (with 2-person team + AI coding tools):
Basic RAG: 2-3 months
Production-ready: 3-5 months
Graphlit-equivalent: 12-20 months (if you even attempt it)
Ongoing Maintenance (every month):
Update dependencies: 4-8 hours
Tune vector search: 2-4 hours
Monitor performance: 4-8 hours
Debug data pipeline issues: 4-12 hours
Update to new models: 8-16 hours
Scale infrastructure: 4-8 hours
Total: 26-48 hours/month
Total First Year Cost (Production-Ready RAG):
Infrastructure: $8,000 - $20,000
Development time (4 months @ $150/hr, 2 engineers): $96,000
Ongoing maintenance (35 hrs/month @ $150/hr): $63,000
Grand Total: $167,000 - $179,000
Total First Year Cost (Graphlit-Equivalent):
You wouldn't do this. It would take 12-20 months and cost $400,000+.
Graphlit Approach
One Platform:
// Everything you need in one API
const graphlit = new Graphlit();
// 5 minutes to production-ready semantic memory
const feed = await graphlit.createFeed({ /* 30+ sources */ });
const content = await graphlit.ingestUri(/* audio, video, docs */);
const results = await graphlit.queryContents({ /* hybrid search */ });Actual Cost:
Platform: $0.10-0.08/credit (volume discounts available)
Development time: 1 day to MVP
Maintenance: Zero (we handle it)
Model updates: Automatic (GPT-5, Claude 4.5, etc.)
Savings vs Production-Ready RAG: $160,000+ in Year 1 Savings vs Graphlit-Equivalent: $400,000+ (and 12-20 months faster to market)
Time to Value Comparison
Building a Slack Search Assistant
With Graphlit (5 minutes):
// Step 1: Connect Slack (30 seconds)
const feed = await graphlit.createFeed({
name: 'Team Slack',
type: FeedTypes.Slack,
slack: { type: FeedListingTypes.Past }
});
// ✅ OAuth automatic, messages syncing
// Step 2: Search (30 seconds)
const results = await graphlit.queryContents({
search: 'Q4 roadmap decisions',
feeds: [{ id: feed.id }]
});
// ✅ Hybrid search across all messagesDIY Stack (2-3 weeks):
Week 1: Build Slack OAuth flow, handle token refresh
Week 1-2: Build polling infrastructure (handle rate limits, pagination)
Week 2: Parse messages, store in database
Week 2: Generate embeddings, index in vector DB
Week 3: Build search API, tune relevance
Week 3: Handle edge cases (threads, reactions, files)
Graphlit advantage: 2-3 weeks saved, production-ready from line 1
Audio Transcription with Speaker Identification
With Graphlit (1 API call):
const audio = await graphlit.ingestUri(
'https://example.com/meeting.mp3',
'Team Meeting',
workflow.id, // Includes transcription + diarization
undefined,
true
);
// ✅ Speaker #1, #2, #3 identified
// ✅ Fully searchable transcriptDIY Stack (1 week):
Integrate Deepgram or AssemblyAI SDK
Handle audio format conversion
Implement diarization
Store and index transcripts
Build search interface
Graphlit advantage: 1 week saved per audio feature
Multi-Source Search (Slack + Gmail + Google Drive)
With Graphlit (10 minutes):
// Connect all sources
const slackFeed = await graphlit.createFeed({ type: FeedTypes.Slack, ... });
const gmailFeed = await graphlit.createFeed({ type: FeedTypes.Email, ... });
const driveFeed = await graphlit.createFeed({ type: FeedTypes.GoogleDrive, ... });
// Search across all sources
const results = await graphlit.queryContents({
search: 'Q4 budget approval',
feeds: [
{ id: slackFeed.id },
{ id: gmailFeed.id },
{ id: driveFeed.id }
]
});
// ✅ Unified search across 3 sourcesDIY Stack (3-4 weeks):
Build OAuth for 3 services (1 week each)
Unify data schemas (1 week)
Build cross-source search (1 week)
Handle sync for all 3 (ongoing)
Graphlit advantage: 3-4 weeks saved
What You Don't Have to Manage
The "Zero Ops" advantage - here's what Graphlit handles so you don't have to:
Infrastructure Management ❌
You DON'T manage:
❌ Vector database configuration (indexes, sharding, replication)
❌ Embedding model selection (we benchmark and choose best)
❌ Chunking strategy optimization (we've tested 20+ approaches)
❌ Storage scaling (automatic as you grow)
❌ Search performance tuning (sub-second queries at scale)
❌ Backup and disaster recovery
❌ Security patches and updates
❌ Monitoring and alerting infrastructureStaying Current with AI ❌
You DON'T track:
❌ New LLM releases (GPT-5, Claude 4.5, Gemini 2.5)
❌ Better embedding models (we test and switch)
❌ Improved transcription services (Deepgram v4, etc.)
❌ New vision models (GPT-4V updates)
❌ Prompt engineering best practices
❌ Token optimization techniquesWith Graphlit: Call the same API. Get the latest models automatically. Your agent framework (Mastra, Agno, etc.) just calls Graphlit via MCP - no updates needed.
// Today: Uses GPT-4 Turbo
// Next month: Automatically uses GPT-5 (zero code changes)
const conversation = await graphlit.createConversation({
name: 'Q&A'
});Data Pipeline Maintenance ❌
You DON'T build:
❌ OAuth connector for each service (30+ services = 30+ integrations)
❌ Polling infrastructure (rate limits, retries, exponential backoff)
❌ Data transformation (PDFs, audio, video, emails, Slack threads)
❌ Deduplication logic (content hashing, similarity detection)
❌ Error handling and retry logic
❌ Monitoring dashboardsGraphlit vs Memory-Only Platforms
Platforms like Mem0 and Zep provide memory storage but require YOU to build everything else.
What They Provide
✅ Vector storage
✅ Memory retrieval APIs
✅ (Zep) Temporal knowledge graph
✅ (Mem0) Open-source flexibility
What YOU Have to Build
❌ All data connectors (Slack, Gmail, Google Drive, etc.)
❌ Automatic sync infrastructure
❌ Audio transcription pipeline
❌ Video processing pipeline
❌ Document parsing (PDFs, Word, etc.)
❌ OAuth flows for every service
❌ Multi-format handling (audio, video, images)
❌ Publishing capabilities (audio generation, summaries)
❌ Content intelligence alerts (notify on specific content)
Example: Building Slack Search
With Mem0/Zep:
// YOU build all of this (2-3 weeks):
// 1. Slack OAuth integration
const slackToken = await buildOAuthFlow(); // 3-5 days
// 2. Polling infrastructure
const poller = new SlackPoller(slackToken); // 2-3 days
poller.onMessage(async (message) => {
// 3. Parse and transform
const parsed = parseSlackMessage(message); // 2 days
// 4. Generate embeddings
const embeddings = await openai.embeddings.create({ /* */ }); // 1 day
// 5. Store in memory platform
await mem0.add(parsed, embeddings); // 1 day
});
// 6. Handle rate limits, retries, errors (ongoing)With Graphlit:
// 5 minutes:
const feed = await graphlit.createFeed({
type: FeedTypes.Slack,
slack: { type: FeedListingTypes.Past }
});
// Done. Everything else automatic.Verdict: Mem0/Zep are excellent memory storage engines. Graphlit is a complete platform. If you're building production apps, you need the complete platform.
Graphlit vs Limited Integration Platforms
Platforms like Supermemory and Hyperspell have some data connectors but limited scope.
Supermemory (3 OAuth Connectors)
What They Have:
Google Drive, Notion, OneDrive connectors
Hybrid search (vector + keyword)
Knowledge graph
What They DON'T Have:
❌ Only 3 connectors (vs Graphlit's 30+)
❌ No Slack, Gmail, GitHub, Linear, Jira (you build these)
❌ Claims audio support but rejects MP3 files (tested)
❌ No video transcription
❌ No audio transcription with diarization
❌ No publishing (audio generation, summaries, exports)
❌ No custom workflows with vision models
❌ No content intelligence alerts
Example: Want to search your Slack + Gmail?
Supermemory: Build Slack OAuth yourself (1-2 weeks), build Gmail OAuth yourself (1-2 weeks)
Graphlit: 10 minutes for both
Hyperspell (Similar Limitations)
What They Have:
Slack, Gmail, Google Drive, Notion, Calendar connectors
Focus on privacy and compliance (SOC 2, GDPR)
What They DON'T Have:
❌ Basic connectors only (not OAuth feeds with auto-sync)
❌ No audio transcription
❌ No video processing
❌ No custom workflows
❌ No publishing capabilities
❌ Fixed pipeline (can't customize extraction)
Verdict: Supermemory and Hyperspell are great for basic document/message search. If you need audio, video, custom workflows, or 30+ data sources, you need Graphlit.
Production-Ready from Day 1
Graphlit isn't just a memory layer - it's a production platform with enterprise features built-in.
Multi-Tenant Architecture ✅
// Day 1: Per-user data isolation
const user = await graphlit.createUser({
identifier: 'user_123'
});
// Scope all operations to this user
const scopedGraphlit = new Graphlit({ userId: user.id });
// User A never sees User B's data
const userContent = await scopedGraphlit.queryContents({ /* */ });With competitors: You build multi-tenancy yourself (2-4 weeks)
Content Intelligence Alerts ✅
// Day 1: Get notified when specific content arrives
const alert = await graphlit.createAlert({
name: 'High-priority mentions',
filter: {
observations: [{ observable: { name: 'urgent' }}]
},
integration: { type: IntegrationServiceTypes.Slack }
});
// Sends Slack message when content with 'urgent' entity is ingestedWith competitors: You build content filtering + webhook infrastructure (1-2 weeks)
Usage Tracking & Billing ✅
// Day 1: Track customer usage
const usage = await graphlit.getUsage({
userId: customer.id
});
// Bill customers based on actual usage
const credits = usage.totalCredits;With competitors: You build metering infrastructure (1-2 weeks)
The Zine Proof Point
Graphlit isn't just a platform - it's battle-tested in production.
Zine is a production SaaS built on Graphlit:
Thousands of active users
20+ OAuth data sources (Slack, Gmail, Calendar, Notion, Linear, etc.)
Millions of documents indexed
Real-time semantic search across all sources
Multi-tenant architecture with per-user isolation
Zero downtime since launch
Why this matters: We built Graphlit to power our own SaaS. Every feature exists because we needed it in production. Every optimization exists because we felt the pain.
You get: Production-proven infrastructure, not a research project.
Developer Velocity at Scale
As your application grows, Graphlit's advantages compound:
Adding New Data Sources
Traditional approach (1-2 weeks per source):
Research API documentation
Build OAuth integration
Handle rate limits and pagination
Parse and transform data
Store and index
Monitor and maintain
Graphlit approach (5 minutes per source):
const jiraFeed = await graphlit.createFeed({ type: FeedTypes.Issue, ... });
const githubFeed = await graphlit.createFeed({ type: FeedTypes.Site, ... });
const notionFeed = await graphlit.createFeed({ type: FeedTypes.Notion, ... });10 data sources:
Traditional: 10-20 weeks
Graphlit: 50 minutes
Updating to New Models
Traditional approach (1-2 days):
Research new model (GPT-5, Claude 4.5)
Update code and parameters
Re-generate embeddings for existing content
Test and validate results
Deploy and monitor
Graphlit approach (automatic):
// No code changes needed
// New models available automatically
// Existing content re-indexed transparentlyScaling to Production
Traditional approach (2-4 weeks):
Set up observability (Datadog, New Relic)
Implement rate limiting
Add caching layer
Optimize database queries
Set up infrastructure alerting
Load testing and tuning
Graphlit approach (built-in):
Automatic scaling
Sub-second queries at any scale
Usage dashboard included
Content intelligence alerts available
Battle-tested at Zine scale
The Bottom Line
Choose Graphlit If You Want:
✅ Data infrastructure for your agents - Works with Mastra, Agno, Vercel AI SDK (via MCP) ✅ Ship fast - Days to production, not months ✅ Stay current - Automatic model updates ✅ Zero ops - No infrastructure to manage ✅ Production-ready - Multi-tenant, content alerts, encryption built-in ✅ Comprehensive - 30+ feeds, audio/video, publishing ✅ Proven - Battle-tested at Zine's scale ✅ Predictable costs - Pay only for usage
Build Your Own Data Layer If You Want:
To integrate 7+ services yourself (vector DB, storage, transcription, etc.)
To build OAuth connectors for 30+ data sources
To maintain sync infrastructure and data pipelines
To spend 3-20 months before shipping
To manage embedding models, scaling, and operations
Start Building Today
npm install graphlit-client5-minute quickstart: Your First Agent 30+ data sources: Feeds Live help: Discord Community
Frequently Asked Questions
Q: What if I need on-premises deployment? A: Graphlit is cloud-native by design (like Vercel, Netlify). We're exploring private Azure deployments for enterprise customers. This architecture enables automatic updates, zero maintenance, and superior reliability.
Q: Can I use my own vector database? A: Graphlit manages vector storage internally for optimal performance. This "opinionated" approach means you get battle-tested configurations without research/tuning. We've benchmarked 12+ vector DBs - you get the best one automatically.
Q: How does pricing compare to building myself? A: Starting at $0.10/credit (volume discounts to $0.08/credit, all-inclusive), you save $160,000+ in Year 1 building even basic production-ready RAG yourself. Building Graphlit-equivalent features would cost $400,000+ and take 12-20 months. See realistic TCO comparison above.
Q: What about data privacy and security? A: Encryption at rest and in transit, multi-tenant isolation, SOC 2 compliance in progress. For sensitive workloads, we're exploring private Azure deployments where data stays in your tenant.
Q: Can I customize workflows and extraction? A: Yes! Graphlit supports custom workflows with preparation stages (vision OCR) and extraction stages (entity extraction, summarization). You choose vision models (GPT-4V, Claude Vision, Gemini) and configure extraction rules.
Q: How do you compare to AI frameworks like Vercel AI SDK, Mastra, or Agno? A: These are excellent frameworks (Vercel AI SDK for UI integration, Mastra for TypeScript agents, Agno for Python agents) - and you can use Graphlit WITH them! Via our MCP server, frameworks with MCP support can access Graphlit's 30+ feeds, audio/video processing, and semantic search as tools. The difference: frameworks are code libraries where you manage infrastructure; Graphlit is a managed platform handling data ingestion, sync, storage, and scaling. Use frameworks for custom UI/workflows, use Graphlit for the entire data pipeline - or combine both via MCP integration.
Last updated: January 2025
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