Weaviate vs Fireworks AI
A detailed comparison to help you choose between Weaviate and Fireworks AI.
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Verdict: Fireworks AI wins
Fireworks AI scores 4.3/5, edging out Weaviate at 4.0/5. Developers and ML teams shipping open-weight models where serving latency and fine-tuning cost determine the product economics.
Weaviate Open-source vector database for AI applications | 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 | Teams building production RAG or semantic search who want hybrid vector-plus-keyword retrieval, a self-hosting path, and a permanent free cloud tier to develop against before paying. | 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 Weaviate → | Visit Fireworks AI → |
Weaviate
Pros
- + Hybrid search (vector plus keyword fusion) measurably improved top-k relevance over either pass alone on my mixed-length document corpus, and fusion weighting is tunable per query.
- + The June 2026 free cloud tier is a real dev environment — 100,000 objects, 3 tenants, 2,000 embedding requests/day, no credit card — spun up in minutes in my testing (weaviate.io/pricing).
- + Self-hosting is genuinely supported: open-source core (BSD-3 outside the wl directory), Docker and Kubernetes deployment, with an enterprise support option (Weaviate Assurance).
- + Database semantics — schema, multi-tenancy, CRUD, replication, RBAC, backups — are part of the product, not add-ons, which matters for production RAG over pure vector engines.
- + Query Agent and Engram extend into agent memory with a documented free allotment (1,000 requests/month Query Agent; 1,000 Engram runs/month) that is enough to evaluate honestly.
Cons
- - Free-tier clusters get no backup retention (Flex retains 7 days), so anything you cannot rebuild must move to a paid plan (weaviate.io/pricing).
- - Self-hosting needs real ops capacity — sharding, replication, index config and upgrades are your problem; enterprise self-host support is quote-only.
- - Usage billing on vector dimensions and storage rewards tuning knowledge; the vendor's own September 2026 price book corrected backup pricing, so quote details matter.
- - The free tier's 1 collection / 3 tenants ceiling forces multi-tenant SaaS designs onto Flex quickly.
- - GraphQL remains a second query language teams must learn; in practice many teams only use the Python client and lose visibility into cluster behavior.
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, Weaviate or Fireworks AI?▼
Fireworks AI scores higher at 4.3/5 vs 4.0/5 for Weaviate, making it the better-rated option in our comparison.
What is the difference between Weaviate and Fireworks AI?▼
Weaviate: Weaviate is an open-source vector database for storing and searching embeddings, used to build retrieval and recommendation features. Fireworks AI: Fireworks AI is an inference and fine-tuning platform for open models, used to serve and specialise them in production.
Is Weaviate or Fireworks AI more affordable?▼
Weaviate starts at €0/mo, Fireworks AI starts at €0/mo. Check the comparison table above for current pricing.
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