Memgraph vs Spider Cloud
Side-by-side comparison of features, pricing, and ratings
At a glance
| Dimension | Memgraph | Spider Cloud |
|---|---|---|
| Pricing | Free Community Edition; Enterprise pricing upon contact | Free tier (100 pages/mo); paid from $20/mo for 1,000 pages |
| Core Function | In-memory graph database for real-time AI & analytics | Web crawling & scraping API for AI agents |
| Query Language | Cypher (compatible with Neo4j), MemGQL federated engine | REST API, Python SDK, Browser AI WebSocket commands |
| AI Features | Vector search, GraphRAG, AI memory (semantic/episodic/procedural) | AI Studio, AI extraction, Silk model, Browser AI commands |
| Integrations | Kafka, Pulsar, Redpanda, Python, Node.js, Neo4j | LangChain, LlamaIndex, CrewAI, S3, GCS, Supabase |
| Deployment | Docker, Kubernetes, Linux on-prem; Memgraph Zero for federated queries | Cloud API + open-source self-host option |
Choose Spider Cloud if you need real-time web data for RAG pipelines and AI agents, with minimal coding and a pay-per-page model. Choose Memgraph if you need an in-memory graph database for GraphRAG, AI memory, and real-time analytics, and you're comfortable with Cypher. They solve fundamentally different problems: Spider Cloud gets data from the web; Memgraph stores and queries graph data.
In-memory graph database for real-time GraphRAG, AI memory, and connected analytics.
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AI web scraping API that turns any site into markdown or JSON for AI agents, pay-as-you-go or flat-rate.
Visit WebsiteWhat real users say: Memgraph vs Spider Cloud
Not marketing copy and not our opinion — a structured sweep of public discussion (reviews, forums, communities and video comments), showing what people praise and what they complain about for each tool.
Memgraph
31 mentions across 2 sources · 70% positive
Hacker News, Lemmy
What users praise
- • Sub-millisecond query responses for real-time analytics and AI workloads.
- • Unified architecture for GraphRAG and graph analytics on one in-memory store.
- • Cypher-compatible, making migration from Neo4j straightforward.
- • Native vector search enables semantic AI without separate vector database.
What frustrates them
- • Early versions lacked native vector search, pushing users to competitors.
- • Complex Cypher queries can sometimes require manual optimization.
- • Smaller ecosystem and community compared to Neo4j.
- • Memory-based pricing can become expensive for large datasets.
Researched Jul 3, 2026
Spider Cloud
41 mentions across 2 sources · 10% positive — critical
YouTube, Lemmy
What users praise
- • One endpoint for scraping, crawling, search, and browser automation.
- • Converts sites to markdown, JSON, JSONL, CSV, XML—flexible outputs.
- • Rust engine and stealth browser claim strong anti-bot bypass.
- • Silk AI model handles captchas and HTML-to-structured data on GPUs.
What frustrates them
- • No real user reviews to validate performance or reliability.
- • Brand name confuses with Spider-Man, hurting discoverability.
- • Pricing details are vague—hidden costs may apply.
- • Learning curve for non-developers could be steep.
Researched Aug 18, 2026
Who should pick which
- Solo founder building an AI agent that needs web contextPick: Spider Cloud
Spider Cloud's simple API, browser AI commands, and pay-per-page pricing are ideal for quick web data ingestion into LLMs, with no complex setup.
- Data scientist analyzing real-time fraud networksPick: Memgraph
Memgraph's in-memory sub-millisecond graph traversal and MAGE library enable real-time anomaly detection on streaming data from Kafka.
- Developer building a GraphRAG system for enterprise knowledgePick: Memgraph
Memgraph's built-in GraphRAG pipelines, vector search, and AI memory features provide a unified graph database optimized for LLM context retrieval.
- Team needing to scrape 1,000+ pages daily for competitive analysisPick: Spider Cloud
Spider Cloud's scaling pricing and 99.9% success rate make it cost-effective for high-volume scraping, with data connectors to S3 and GCS for downstream use.
- Organization migrating from Neo4j to a faster graph DBPick: Memgraph
Memgraph is Cypher-compatible, offers in-memory performance, and with MemGQL can query distributed data live, easing migration and improving speed.
Frequently Asked Questions
Memgraph vs Spider Cloud: which should you choose?
Choose Spider Cloud if you need real-time web data for RAG pipelines and AI agents, with minimal coding and a pay-per-page model. Choose Memgraph if you need an in-memory graph database for GraphRAG, AI memory, and real-time analytics, and you're comfortable with Cypher. They solve fundamentally different problems: Spider Cloud gets data from the web; Memgraph stores and queries graph data.
Can I use both Spider Cloud and Memgraph together?
Yes. Spider Cloud can scrape web data, then you can pipe the extracted content (e.g., via a data connector to S3) into Memgraph for graph storage and GraphRAG.
Does Spider Cloud support CAPTCHA solving?
Yes, Spider Cloud's Silk model and AI extraction handle captcha solving, as noted in its features.
Is Memgraph free for commercial use?
Memgraph Community Edition is free and open-source, but for commercial production with high availability and enterprise features, you need a paid Enterprise license.
Which tool is better for real-time data?
Memgraph is designed for real-time graph analytics with stream connectors and sub-millisecond queries. Spider Cloud is a scraping API, not a real-time streaming platform.
Can I self-host Spider Cloud?
Yes, Spider Cloud's core is open-source and available on GitHub for self-hosting. The cloud version offers additional features like AI Studio.
Does Memgraph support vector search?
Yes, Memgraph has built-in vector search for semantic AI, useful for GraphRAG and AI memory.
What are the pricing tiers for Spider Cloud?
Free: 100 pages/mo. Paid: from $20/mo (1,000 pages) scaling up. AI Studio add-on: $6/mo. Only successful pages are billed.
Can Memgraph be used for simple document storage?
Not ideal. Memgraph is a graph database designed for connected data, not document or key-value storage. Use a document DB for that purpose.
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Last reviewed: July 3, 2026