Why this comparison matters
Both LlamaIndex and Pathway are open-source Python projects for building retrieval-augmented generation (RAG) systems, so they end up on the same shortlists. They start from different assumptions, though. LlamaIndex is a data framework for LLM apps. It covers parsing, indexing, query engines and agents, and it has a paid cloud layer (LlamaParse, LlamaExtract) for hard documents. Pathway is a live data processing engine. Its pitch is that your index stays in sync with the sources as files change, with no batch re-indexing job to schedule.
The real question for a builder is how often your data changes and how messy your documents are. Answer those two and the choice mostly makes itself. The rest of this piece sets out what each vendor's docs and pricing pages say, and where each one fits.
Scores at a glance
These are scored.tools rubric scores (rubric v1, scored 2026-09-29), based on public docs, repos and pricing pages.
| Criterion | LlamaIndex | Pathway |
|---|---|---|
| Overall rating | 8.2 | 6.8 |
| Output quality | 8 | 6 |
| Ease of use | 8 | 5 |
| Pricing value | 8 | 9 |
| API and integrations | 9 | 8 |
| Problem fit | 8 | 6 |
LlamaIndex wins on docs and breadth. Pathway wins on pricing value, because its free tiers cover a lot of ground and nothing in its template repo needs a paid dependency.
Feature comparison
| Feature | LlamaIndex | Pathway |
|---|---|---|
| Core purpose | Data framework for LLM apps: parsing, indexing, retrieval, agents | Live data framework for real-time RAG and ETL pipelines |
| License | MIT (framework) | Core engine under BSL 1.1 per the pricing page; llm-app templates are MIT |
| GitHub traction | 52,344 stars on run-llama/llama_index | 58,872 stars on pathwaycom/llm-app (the templates repo, not the core engine) |
| Languages and APIs | Python and TypeScript frameworks; per the docs, SDKs for Python, TypeScript, Go and Java plus a REST API for the cloud services | Python, SQL and REST APIs |
| Data connectors | Google Drive, SharePoint, S3, Confluence and a large community connector library | Kafka, S3, Google Drive, SharePoint, local files; Pathway claims 300+ sources |
| Keeping the index fresh | Supported through ingestion pipelines and document management, but you schedule and run the refresh | Incremental by design: changes in sources flow through to the index as they happen |
| Hard document parsing | LlamaParse (paid) for PDFs, tables and multimodal content | Parsers included in templates; no paid parsing service comparable to LlamaParse |
| Configuration | Python or TypeScript code | YAML templates for low-code setup, Python for customisation |
| Query and agent layer | Query engines, retrievers, agent orchestration and workflows | Question-answering and search templates; Adaptive RAG, which Pathway says cuts token costs up to 4x |
| Local/private RAG | Works with local models through integrations | Template for private RAG with Mistral and Ollama |
| Getting started | pip or npm install, five-line starter example in the docs | pip, poetry or Docker; documentation is thinner |
What the table leaves out
The licensing difference matters more than it looks. LlamaIndex's framework is MIT, full stop. Pathway's templates are MIT, but its pricing page lists the engine under BSL 1.1, and the free Community tier is capped at 8 GB RAM and 4 cores. A free Scale tier with a license key raises that to 16 GB RAM. Past that you are on Enterprise. For a single team running one RAG service, those caps may never bite. For a platform team planning to scale out across nodes, read the license before you commit.
The two also work together. LlamaIndex lists a Pathway retriever integration, so you can use Pathway to keep a live vector index in sync and LlamaIndex to build the query and agent layer on top. If you like both, you don't have to pick.
Pricing comparison
LlamaIndex
The open-source framework is free under MIT. You pay for the hosted services, mainly LlamaParse and LlamaExtract, which run on credits. Per the LlamaParse pricing page, credits cost $1.25 per 1,000. Parsing costs vary by tier:
- Fast: 1 credit per page (spatial text only)
- Cost-effective: 3 credits per page
- Agentic: 10 credits per page
- Agentic Plus: 45 credits per page
That works out to roughly $1.25 per 1,000 pages at the Fast tier, $12.50 at Agentic and about $56 at Agentic Plus, before add-ons like layout extraction (+3 credits per page). Hosted indexing costs 2 credits per exported page and 1 credit per retrieval query, per the same page. The docs also note a 48-hour cache, so re-parsing the same file within that window is free. Pick your parse tier per document type and costs stay predictable. If you send everything through Agentic Plus, they won't.
Pathway
Per Pathway's pricing page, there are three tiers:
- Community: free, self-hosted, up to 8 GB RAM and 4 cores, no license key needed.
- Scale: free or paid with a license key, self-hosted, up to 16 GB RAM, adds 20+ app templates.
- Enterprise: price on request. Adds horizontal scaling (up to 24 TB RAM, 128 cores across 40 nodes), high availability, Kubernetes support, 24/7 support and a managed option.
Pathway has no per-page or per-query fee, so your costs are your own infrastructure plus whatever LLM and embedding APIs you call. If Adaptive RAG performs anywhere near Pathway's own figures, token spend could drop too, but that 4x number is a vendor claim with no independent benchmark behind it. The weak spot is Enterprise: no public price, so budget planning past the free tiers means a sales call.
Pricing verdict
For a self-hosted pipeline, both cost nothing to start. LlamaIndex's bill grows with document volume once you lean on LlamaParse. Pathway's grows with hardware, and only becomes a licensing question above 16 GB RAM. Teams parsing hundreds of thousands of complex PDFs will feel LlamaParse costs. Teams with clean text sources and big memory needs will hit Pathway's tier caps first.
Use case scenarios
Pick LlamaIndex when...
- Your documents are hard to parse. Scanned PDFs, financial filings, slide decks with tables. LlamaParse is the main reason to choose LlamaIndex here, and Pathway has nothing comparable as a managed service.
- You need more than retrieval. Agents, multi-step workflows, routing between query engines, structured extraction with custom schemas. LlamaIndex covers all of this in one framework.
- Your team is new to RAG. The five-line starter, larger community and broader docs make it easier to get unstuck. The project's own cons list flags a steep learning curve for complex setups, but the on-ramp is gentler than Pathway's.
- You work outside Python. TypeScript support in the framework and Go and Java SDKs for the cloud services give you options Pathway doesn't.
Pick Pathway when...
- Your sources change constantly. SharePoint folders people edit all day, Kafka streams, ticket systems. Pathway's incremental engine keeps the index current without a re-index cron job, which is the problem it was built to solve.
- You want a working pipeline from a template. The llm-app repo ships ready-to-deploy RAG and enterprise search templates configured in YAML. If one matches your case, you skip most of the plumbing.
- You need private, local RAG on modest hardware. The Mistral and Ollama template plus the free Community tier make an all-local setup cheap to stand up.
- You care about per-query cost at volume. No usage fees from Pathway itself, plus Adaptive RAG as a lever on token spend.
Use both when...
You have live sources and also need agents or complex query logic. Let Pathway own ingestion and indexing, then call it from LlamaIndex through the retriever integration.
Verdict
LlamaIndex is the better default for most RAG projects, and its 8.2 rating against Pathway's 6.8 reflects that. It has the wider feature set, clearer docs, an MIT license with no resource caps, and the strongest paid parsing option of the two. Choose it for document-heavy RAG, agentic apps, and teams that want one framework from ingestion to answer.
Pathway is the clear winner for one specific job: RAG over data that changes all the time. Its incremental engine and live connectors solve freshness at the architecture level, and the free tiers are generous for a single service. Expect thinner documentation (it scored 5 on ease of use), check the BSL 1.1 terms and the 16 GB free ceiling before you plan to scale, and treat the Adaptive RAG savings as a claim to verify on your own data. If stale answers are your biggest RAG problem, start with Pathway. Otherwise, start with LlamaIndex.
