Qdrant vs Fireworks AI
A detailed comparison to help you choose between Qdrant and Fireworks AI.
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Verdict: Fireworks AI wins
Fireworks AI scores 4.3/5, edging out Qdrant at 4.1/5. Developers and ML teams shipping open-weight models where serving latency and fine-tuning cost determine the product economics.
Qdrant Vector database for semantic search and AI applications | Fireworks AI Fastest open-source model inference | |
|---|---|---|
| Rating | 4.1 (1 review) | 4.3 (1 review) |
| Pricing Model | freemium | usage-based |
| Starting Price | Free tier available | Pricing varies |
| Best For | Engineers building semantic search, recommendation systems or RAG retrieval layers where metadata filtering must not degrade search quality, and who want an Apache-2.0 core with a managed cloud escape hatch. | 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 Qdrant → | Visit Fireworks AI → |
Qdrant
Pros
- + Filtered ANN is the real differentiator: filters participate in the HNSW traversal, and in my testing recall under mandatory metadata filters stayed high where post-filter approaches collapsed.
- + Apache-2.0 core with a production-grade self-host path (Docker, Kubernetes, snapshots, replication) and client libraries for Python, Rust, Go, Java and TypeScript.
- + Free cloud tier is usable — 0.5 vCPU / 1 GB RAM / 4 GB disk, about one million 768-dim vectors, no credit card (qdrant.tech/cloud, at time of writing).
- + Quantization options (scalar, product, binary, asymmetric since 1.15) cut memory substantially with negligible recall loss in my testing when originals are kept for reranking.
- + Built-in inference API means applications can send raw text for embedding instead of managing a separate embedding service — older 'no built-in embeddings' complaints are out of date.
Cons
- - It is a retrieval engine, not a RAG platform: chunking, parsing, prompting and reranking are all your own assembly, even with the inference API.
- - Distributed deployments need real capacity planning — RAM is the binding constraint for HNSW working sets, and the pricing calculator will not compute that for you.
- - Free cloud clusters suspend after one week of inactivity, which catches demo and staging users off guard (qdrant.tech/cloud).
- - Per-hour cloud pricing means idle clusters keep billing; small dev clusters forgotten over a month cost as much as the entry paid tier.
- - Teams already running pgvector well may not justify a new stateful system — the case is strongest for filter-heavy, latency-critical search.
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, Qdrant or Fireworks AI?▼
Fireworks AI scores higher at 4.3/5 vs 4.1/5 for Qdrant, making it the better-rated option in our comparison.
What is the difference between Qdrant and Fireworks AI?▼
Qdrant: Qdrant is an open-source vector search engine written in Rust, used for semantic search and retrieval in AI applications. Fireworks AI: Fireworks AI is an inference and fine-tuning platform for open models, used to serve and specialise them in production.
Is Qdrant or Fireworks AI more affordable?▼
Qdrant starts at €0/mo, Fireworks AI starts at €0/mo. Check the comparison table above for current pricing.
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