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Side-by-side features, use cases and pricing — because the right pick depends on your job and budget, not just the ranking.

LlamaIndex logo
LlamaIndex
✓ verifiedFreemium

Developer framework and LlamaParse service for parsing documents and building AI agents and RAG workflows over them.

455K visits/mo1.9K saves
fal logo
fal
✓ verifiedPaid

Serverless platform for running and fine-tuning image, video, audio and 3D generative models via one fast API.

2.3M visits/mo
supermemory™ logo
supermemory™
✓ verifiedFreemium

Developer API that gives AI agents persistent memory, retrieval, and connectors, usable both as infrastructure and a personal app.

174K visits/mo
qdrant.io logo
qdrant.io
✓ verifiedFreemium

High-performance open-source vector database for production AI retrieval and RAG, for teams needing scale, hybrid search, or self-hosting.

166K visits/mo
Runware logo
Runware
✓ verifiedFreemium

Pay-as-you-go API aggregating thousands of image, video, audio and LLM models with custom inference hardware for lower per-request cost.

249K visits/mo
Pricing

No public pricing

H100 GPU: from $1.89/hr
B200 GPU: from $3.49/hr
Video (Wan 2.5): $0.05/second
Image (Seedream V4): $0.03/image
Free: $0/mo (~$5/mo of usage included)
Pro: $19/mo (~$20/mo of usage, unlimited storage, 2 teammates)
Max: $100/mo (~$130/mo of usage, 6x Pro headroom)
Scale: $399/mo (~$600/mo of usage, up to 10 teammates)

No public pricing

vCPU compute: $0.016/hr
RTX PRO 6000: $1.99/hr (as low as $0.99)
H100: $2.76/hr
H200: $3.18/hr
B200: $4.99/hr

Free trial available

Core features
  • LlamaParse document parsing and extraction
  • Open-source framework for AI agents and workflows
  • Document indexing for retrieval/RAG
  • Prebuilt solutions by industry and use case
  • Free starter credits for LlamaParse
  • 1,000+ generative model APIs
  • Serverless GPU inference engine
  • On-demand and dedicated GPU clusters
  • Model fine-tuning and custom deployments
  • Bring-your-own-weights and private endpoints
  • SOC 2 compliance and enterprise features
  • Persistent, structured memory built as a knowledge graph
  • Sub-300ms hybrid retrieval (RAG) with reranking
  • Native filesystem mount for agent memory access
  • Connectors to Slack, Notion, Drive, Gmail, GitHub, S3
  • Automatic extraction from PDFs, images, and audio
  • User profile and behavior tracking across sessions
  • Hybrid dense and sparse vector search (BM25, SPLADE, miniCOIL)
  • Advanced metadata filtering applied during search traversal
  • Multivector support for multimodal retrieval
  • Reranking with score boosting and late-interaction models (ColBERT, MMR)
  • Flexible deployment: cloud, hybrid, private, or edge
  • Rust-based engine optimized for low-latency, high-scale search
  • Single API for image, video, audio, 3D and LLM models
  • Standardized model addressing across hosted, partner and custom uploads
  • Support for LoRAs, ControlNets, VAEs and embeddings on open-source models
  • WebSocket and REST access with async webhook delivery
  • Pay-per-request billing with no infrastructure to manage
  • Raw serverless GPU/CPU compute for custom workloads
Use cases
  • Parse complex documents for AI apps
  • Build RAG and agent workflows
  • Automate invoice and claims processing
  • Search across technical documents
  • Adding image/video generation to an app
  • Running fast diffusion-model inference at scale
  • Training or fine-tuning custom generative models
  • Developers adding long-term memory to AI agents
  • Teams building agents that need to sync with existing tools
  • Individuals wanting one memory layer shared across multiple AI assistants
  • Building retrieval-augmented generation (RAG) pipelines
  • Powering AI recommendation and semantic search systems
  • Enterprises needing on-prem or hybrid deployment for compliance
  • AI agent platforms needing fast contextual retrieval at scale
  • Adding AI image or video generation to an app without managing infra
  • Batching multi-modal generation tasks in one API call
  • Running custom fine-tuned models via Model Upload
  • Cutting inference costs at high generation volume
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