LlamaIndex
7.1#4 in llm application development frameworksFrom $50 a monthFree planWebAPILinuxSelf-hosted
VisitLlamaIndex vs txtai
What both publish
| Fact | LlamaIndex | txtai |
|---|---|---|
| Deployment | Enterprise customers can deploy LlamaParse in private VPCs, and the pricing page also lists SaaS or hybrid cloud deployment for Enterprise. | txtai API instances and workflows can run locally, on a server, on a cluster, or serverless; documented serverless options include AWS Lambda, Google Cloud Functions, Azure Cloud Functions, and Kubernetes with Knative. |
| Integrations | LlamaIndex documents community integrations for data loaders, agent tools, LLMs, vector stores, observability, and application frameworks. | The documentation describes agent support for Hugging Face and llama.cpp models, and OpenAI, Claude, and AWS Bedrock through LiteLLM. |
| Purpose | LlamaIndex helps developers and enterprises automate document processing and build AI workflows around documents. | txtai is an all-in-one AI framework for semantic search, LLM orchestration, and language model workflows. |
| Security | The pricing FAQ says LlamaParse is certified for SOC 2 Type II, GDPR, and HIPAA. | The API is open over HTTP by default; the documentation describes HTTPS options, API-token authorization, and a safeopen option enabled by default for API and application runs. |
| Support | The listed support levels are community for Free, basic email support for Starter, priority Slack Connect for Pro, and dedicated support for Enterprise. | NeuML offers implementation guidance, development support, and AI consulting, with meetings or email as contact options. |
LlamaIndex plans
- Starter$50 a month
- Pro$500 a month
- EnterprisePriced on request
- Free—
txtai plans
- txtaiFree
Pick LlamaIndex if…
LlamaIndex is for developers and enterprises automating document workflows and knowledge work. Its parsing, extraction, classification, and RAG indexing capabilities suit teams building AI applications around document content.
Pick txtai if…
txtai suits developers and teams building semantic search or language-model workflows who want to run locally or manage their own deployment. It also fits projects that need embeddings across text and media or integrations with APIs and language bindings.