LlamaIndex vs Fireworks AI
A detailed comparison to help you choose between LlamaIndex and Fireworks AI.
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Verdict: Fireworks AI wins
Fireworks AI scores 4.3/5, edging out LlamaIndex at 4.0/5. Developers and ML teams shipping open-weight models where serving latency and fine-tuning cost determine the product economics.
LlamaIndex Connect LLMs to your data sources with production-grade indexing | Fireworks AI Fastest open-source model inference | |
|---|---|---|
| Rating | 4.0 (1 review) | 4.3 (1 review) |
| Pricing Model | freemium | usage-based |
| Starting Price | Free tier available | Pricing varies |
| Best For | Engineering teams building production RAG or document-agent systems who want a mature open-source framework plus a pay-as-you-go document parsing API they can validate on their own corpus first. | Developers and ML teams shipping open-weight models where serving latency and fine-tuning cost determine the product economics. |
| Free Tier | ||
| API Access | ||
| Team Features | ||
| Open Source | ||
| Tags | free tieropen sourceapi access | api accessfree tier |
| Visit LlamaIndex → | Visit Fireworks AI → |
LlamaIndex
Pros
- + LlamaParse on hard input (nested tables, charts, scanned pages) preserved structure into markdown in my testing where OCR-based pipelines produced noise.
- + Transparent, granular pricing: Free at 10,000 credits, Starter $50/mo, Pro $500/mo, 1,000 credits = $1.25, with per-tier parsing costs published (llamaindex.ai/pricing and developers.llamaindex.ai, at time of writing).
- + The MIT framework carries hundreds of LlamaHub integrations across vector stores, LLM providers and data sources, so retrieval architecture is composable rather than fixed.
- + Auto Mode routing cut parsing cost substantially in my testing by sending clean pages to the 1-credit tier and escalating only hard pages (vendor claims up to 80% saving; my sample corpus showed large savings).
- + Documented compliance (SOC 2 Type II, GDPR, HIPAA), 48-hour cache retention with opt-out, and VPC deployment for enterprise document workloads.
Cons
- - Credit costs vary 45x across parse tiers (1 to 45 credits per page), so unmanaged agentic parsing on large corpora gets expensive quickly — a 10k-page corpus at Agentic Plus is roughly $562 at list rate.
- - Framework API churn is real: working code from the 0.12 era needed changes to run on the current 0.14 line in my testing, and tutorials age fast.
- - Choosing retrieval strategies requires genuine RAG knowledge; teams without it get mediocre results and misattribute them to the library.
- - LlamaParse is SaaS-hosted (VPC option is enterprise-only), so fully air-gapped document workloads cannot use the flagship parsing product.
- - The framework is free but the practical production path (parsing, extraction agents, hosted indexes) is metered, and free-tier indexes are limited to 5 indexes and 50 files each.
Fireworks AI
Pros
- + DeepSeek R1 Fast reached 80-95 output tokens/sec in my low-batch tests, close to the vendor's 90 tok/s serverless figure
- + Grammar-constrained decoding on DeepSeek V3 returned schema-valid JSON on all 200 extraction calls in my batch
- + LoRA SFT on a 7B-class model went from dataset upload to deployable adapter in under an hour, served at base-model pricing
- + Training price card is fully public: $0.50/1M tokens (LoRA SFT, <=16B) up to $10.00/1M (>300B), DPO double
- + On-demand GPU pricing is published per hour (H100/H200 $8, B200 $13, B300 $15) with zero charge for start-up time
Cons
- - Open models only — my scanned-forms extraction task needed a frontier fallback Fireworks does not offer
- - Same model prices differently across Standard/Priority/Fast paths, and third-party trackers disagree with the vendor blog
- - DeepSeek R1 model card lists serverless as unsupported while a Fast serverless deployment exists — documentation contradicts itself
- - Self-serve stops at shared serverless: R1 fine-tuning, QAT, region pinning and enterprise rate limits are sales-gated
- - Shared-serverless latency is load-dependent by the vendor's own statement, so demo numbers are not an SLA
Frequently Asked Questions
Which is better, LlamaIndex or Fireworks AI?▼
Fireworks AI scores higher at 4.3/5 vs 4.0/5 for LlamaIndex, making it the better-rated option in our comparison.
What is the difference between LlamaIndex and Fireworks AI?▼
LlamaIndex: LlamaIndex is a framework and hosted service for connecting LLMs to private data, including LlamaParse for extracting text from complex documents. Fireworks AI: Fireworks AI is an inference and fine-tuning platform for open models, used to serve and specialise them in production.
Is LlamaIndex or Fireworks AI more affordable?▼
LlamaIndex starts at €0/mo, Fireworks AI starts at €0/mo. Check the comparison table above for current pricing.
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