LlamaIndex vs Google Gemini API
Data framework for LLMs — RAG-first with LlamaCloud + LlamaParse
vs. Gemini 2.5 Pro, Flash, Flash-Lite — multimodal + 2M context
Pricing tiers
LlamaIndex
OSS (MIT)
MIT-licensed core. Python + TypeScript. Free forever.
$0 base (usage-based)
LlamaCloud — Free
Free tier of LlamaCloud. 1,000 pages/day via LlamaParse. Basic indexing.
Free
LlamaCloud — Paid
Pay-per-page parsing + usage-based indexing. $0.003 per page (Fast mode).
$0 base (usage-based)
LlamaCloud Enterprise
Custom. SSO, SOC2, higher rate limits, private index hosting.
Custom
Google Gemini API
Free Tier (AI Studio)
Generous free tier with rate limits. Good for dev + prototyping. Data may be used to improve Google products.
Free
Paid API (Gemini API)
Pay-as-you-go per-token. Data NOT used for training.
$0 base (usage-based)
Vertex AI (GCP)
Enterprise deployment via Google Cloud. Same pricing structure + GCP features (IAM, VPC-SC, CMEK).
$0 base (usage-based)
Gemini Enterprise
Custom. Gemini 2.5 Deep Think model access + Google Workspace + Agentspace.
Custom
Free-tier quotas head-to-head
Comparing oss on LlamaIndex vs free-tier on Google Gemini API.
| Metric | LlamaIndex | Google Gemini API |
|---|---|---|
| No overlapping quota metrics for these tiers. | ||
Features
LlamaIndex · 16 features
- Agents — Agent patterns: ReAct, function-calling, multi-agent workflows.
- Document Readers — 200+ readers for PDF, web, Google Drive, SharePoint, Notion, S3, Slack.
- Evaluations — Built-in eval framework: faithfulness, context precision/recall.
- LlamaCloud — Managed indexing + retrieval platform. File connectors, auto-chunking, retrieval…
- LlamaExtract — Schema-based structured extraction from unstructured docs.
- LlamaHub — Community marketplace of readers, tools, prompts.
- LlamaParse — Best-in-class PDF + complex document parser. Tables, math, layout preserved.
- Multimodal — Image + text models, image retrieval.
- Node Parsers — Document chunkers: token, sentence, semantic, hierarchical.
- Observability (OpenLLMetry) — OTel-based tracing baked in.
- Property Graph — Graph-based RAG (knowledge graphs from unstructured data).
- Query Engines — Retrieval + response synthesis combos — router, sub-question, tree, etc.
- RAG — End-to-end RAG patterns: ingest → index → retrieve → synthesize.
- Tools — 50+ pre-built tool integrations.
- Vector Store Integrations — 50+ vector DB integrations.
- Workflows — Event-driven agent workflows (AgentWorkflow).
Google Gemini API · 11 features
- Batch API — 50% discount for async processing.
- Code Execution — Python code interpreter tool (sandboxed).
- Context Caching — Cache system instructions + tools for up to 90% savings.
- File API — Upload large files (up to 2 GB) for multimodal prompts.
- Function Calling — JSON schema-based tool calling. Parallel supported.
- generateContent API — Core generation endpoint.
- Grounding with Search — Augment answers with Google Search results. Fact-checked citations returned.
- Model Tuning — Supervised fine-tuning via AI Studio.
- Multimodal Live API — Bidirectional streaming voice + video (WebSocket).
- Safety Settings — Configurable thresholds for harm categories.
- streamGenerateContent — Streaming variant with SSE.
Developer interfaces
| Kind | LlamaIndex | Google Gemini API |
|---|---|---|
| SDK | llama-index (Python), llamaindex (TS) | @google/genai, google-genai-go, google-genai (Python) |
| REST | LlamaCloud API, LlamaParse API | Gemini REST API, Vertex AI Endpoint |
| MCP | LlamaIndex MCP | Gemini MCP |
Staxly is an independent catalog of developer platforms. Outbound links to LlamaIndex and Google Gemini API are plain references to their official websites. Pricing is verified against vendor pages at publication time — reconfirm before buying.
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