Postgresml vs Spider Cloud

Side-by-side comparison of features, pricing, and ratings

Analysis reviewed Live tool data as of 2026-09-01
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At a glance

DimensionPostgresmlSpider Cloud
PricingFreemium; cloud service pricing varies, self-hosted freeFreemium; $0.03/1k pages + AI Studio $6/mo add-on
Core FunctionIn-database GPU-accelerated ML & AIWeb crawling/scraping API for AI agents
Data HandlingOperates on data within PostgreSQLExtracts web data to structured formats
AI CapabilitiesEmbeddings, LLM generation, fine-tuning, regressionBrowser AI commands, AI extraction, Silk captcha solver
DeploymentCloud service + open-source self-hosted extensionCloud API + open-source self-host option
Best ForPostgreSQL users adding ML without new servicesAI agents needing real-time web data

Choose Spider Cloud if your priority is fetching live web data for AI agents (RAG, scraping) with pay-as-you-go pricing and recent Browser AI commands. Choose PostgresML if you already use PostgreSQL and want GPU-accelerated ML (embeddings, LLM generation) directly in your database without new infrastructure.

Try PostgresmlTry Spider Cloud
Postgresml
Postgresml

Run GPU-accelerated machine learning and AI inside PostgreSQL with SQL.

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Spider Cloud
Spider Cloud

AI web scraping API: crawl, scrape, search any site into markdown or JSON at 10k req/min.

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Pricing
Freemium
Freemium
Plans
$0/mo
From $7.50/query hour
$0.60/instance hour
Custom
$1/GB + $0.001/min compute
$40/mo (2 concurrency)
$6/mo
Popularity
4 views
7.5k views
Skill Level
Intermediate
Intermediate
API Available
Platforms
WebAPIPlugin
WebAPICLI
Categories
🗄️ Vector Databases & Retrieval📊 Data & Analytics⚙️ Developer Infrastructure
🌐 Web Scraping & Search APIs🖱️ Browser & Computer-Use Agents
Features
SQL API for all ML operations (pgml.embed, pgml.transform, pgml.train, pgml.predict)
Text generation with Llama 3.1 (8B, 70B, 405B), Llama 3.2, Mistral, Mixtral, Phi-3
Embedding generation with e5-small-v2, gte-base-en-v1.5, gte-large-en-v1.5, mxbai-embed-large-v1
Vector index with HNSW or IVFFlat for fast KNN and ANN search
Fine-tune LLMs on your own data within PostgreSQL (pgml.tune)
Supervised learning: regression, classification, clustering (pgml.train)
Model deployment monitoring and versioning (pgml.deploy)
Streaming inference via pgml.transform_stream()
Built-in data preprocessors for splitting and chunking
Colocate data and compute—embed, serve, and store in one process
Python and JavaScript SDKs (Korvus) for RAG pipelines
Self-hosted open-source deployment
Serverless cloud with burst GPU capacity
Dedicated instances on major cloud providers
VPC deployments for enterprise
Scrape any website into markdown, JSON, or raw HTML
Full-site crawling at 100K+ pages/sec
10,000 core API requests per minute default
Web Search API: SERP + scraping + extraction in one call
/ai/search endpoint with relevance gate to skip irrelevant pages
Silk AI model: HTML-to-structured data and captcha solving on GPUs
Browser Cloud: full browser sessions over CDP
AI commands (Act, Extract, Observe) via WebSocket with AI Studio
Multiple output formats: HTML, raw, plain text, markdown, JSON, JSONL, CSV, XML
Stealth browser layer and Unblocker for anti-bot sites
Proxy pool with 215M+ residential and ISP IPs across 199+ countries
Robots.txt compliance on by default, disable per-request
data_connectors parameter: pipe results to S3, GCS, Google Sheets, Azure Blob, Supabase
extraction_schema parameter: AI output conforms to JSON schema
1,000+ ready-made scraper examples across 32 categories
Integrations
Hugging Face
PyTorch
TensorFlow
Flax
Scikit-Learn
XGBoost
LightGBM
CatBoost
Apache Airflow
dbt
Dagster
Kafka
AWS
Azure
Google Cloud
LangChain
LlamaIndex
CrewAI
FlowiseAI
AutoGen
Agno

What real users say: Postgresml 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.

Postgresml

10 mentions across 1 sources · 40% positive — mixed

Hacker News

What users praise

  • • GPU-accelerated ML models run directly in Postgres via SQL.
  • • Simplifies AI stack by colocating data and compute.
  • • Built-in embedding generation with open-source models like Llama and Mistral.
  • • Supports vector search (KNN/ANN) with HNSW and IVFFlat indexing.

What frustrates them

  • • Project is abandoned — no active development or support.
  • • Uncertain future for security patches and bug fixes.
  • • Naming caused confusion and backlash from Postgres community.
  • • Accused of copying prior art without proper credit.

Researched Jul 3, 2026

Spider Cloud

41 mentions across 2 sources · 0% positive — critical

YouTube, Lemmy

What users praise

  • • Competitive pay-as-you-go pricing at $1/GB with no expiry.
  • • Default rate limit of 10,000 requests per minute is generous.
  • • Broad output formats (HTML, markdown, JSON, CSV) cover diverse needs.
  • • Integrated Web Search API bundles SERP and extraction for AI agents.

What frustrates them

  • • No community feedback to confirm reliability or performance.
  • • Self-reported metrics lack independent verification.
  • • Stealth browser success may vary across real sites.
  • • Potential legal risks from scraping; compliance is user's responsibility.

Researched Aug 26, 2026

Who should pick which

  • AI developer building RAG pipeline
    Pick: Spider Cloud

    Needs real-time web data; Spider Cloud's crawling, AI extraction, and Browser AI commands directly feed LLMs; integrates with LangChain/LlamaIndex.

  • PostgreSQL DBA adding ML
    Pick: Postgresml

    Leverages existing Postgres data for embeddings, LLM generation, and vector search without new services; colocated compute reduces latency.

  • Data scientist doing NLP on DB
    Pick: Postgresml

    In-database fine-tuning and GPU-accelerated models (Llama, Mistral) via SQL; no data movement.

  • Startup needing web scraping for AI
    Pick: Spider Cloud

    Cost-effective $0.03/1k pages, 99.9% success rate, unblocker handles anti-bot measures; open-source fallback for self-hosting.

  • Enterprise requiring data privacy
    Pick: Postgresml

    Self-hosted on-premises keeps data inside PostgreSQL; no external API calls for ML tasks.

Frequently Asked Questions

Postgresml vs Spider Cloud: which should you choose?

Choose Spider Cloud if your priority is fetching live web data for AI agents (RAG, scraping) with pay-as-you-go pricing and recent Browser AI commands. Choose PostgresML if you already use PostgreSQL and want GPU-accelerated ML (embeddings, LLM generation) directly in your database without new infrastructure.

Can Spider Cloud extract data from JavaScript-heavy sites?

Yes, via Browser Cloud and Browser AI commands (Act, Extract, Observe) using WebSocket, which renders JavaScript.

Does PostgresML require GPU for all operations?

GPU is needed for acceleration, but some operations (e.g., basic vector search) can run on CPU, though performance suffers.

Can I use Spider Cloud without an API key?

No, the cloud API requires authentication. However, the open-source core can be self-hosted without an API key.

Does PostgresML support non-PostgreSQL databases?

No, it's exclusively for PostgreSQL.

What formats does Spider Cloud output?

Markdown, HTML, JSON, CSV, XML, and plain text.

Can PostgresML fine-tune open-source LLMs?

Yes, it supports fine-tuning models like Llama and Mistral on custom data.

Does Spider Cloud have a free tier?

Yes, it offers a freemium plan with limited credits; details not specified.

Is PostgresML open-source?

Yes, it's open-source and can be self-hosted.

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Explore each tool further

Postgresml
View Postgresml reviewPostgresml alternatives
Spider Cloud
View Spider Cloud reviewSpider Cloud alternatives

Browse these categories

Best AI Vector Databases & Retrieval toolsBest AI Web Scraping & Search APIs toolsBest AI Data & Analytics toolsBest AI Browser & Computer-Use Agents tools
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Last reviewed: July 3, 2026