Baseten vs Fireworks AI
A detailed comparison to help you choose between Baseten and Fireworks AI.
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Verdict: Fireworks AI wins
Fireworks AI scores 4.3/5, edging out Baseten at 4.1/5. Developers and ML teams shipping open-weight models where serving latency and fine-tuning cost determine the product economics.
Baseten Model inference infrastructure for developers | 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 | ML engineers and platform teams deploying custom or fine-tuned models to production who want autoscaling GPUs and code-owned deployment artifacts. | 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 tierapi access | api accessfree tier |
| Visit Baseten → | Visit Fireworks AI → |
Baseten
Pros
- + Truss packaging (weights + model.py + config.yaml) gave me a deployable endpoint I could version in git like any other service
- + Scale-to-zero is real: my idle test endpoint stopped billing entirely between requests
- + Rolling deployments kept the old replica serving during promotion — zero dropped requests in my test
- + SOC 2 Type II and HIPAA compliance included on the base tier (baseten.co/pricing)
- + Per-minute GPU pricing is published: H100 80GB at $0.10833/min, T4 from $0.63/hr at time of writing (inferencehub.org)
Cons
- - An always-on H100 replica at list computes to roughly $4,680/month — min_replica choices dominate your bill
- - Model APIs cover open-weight models only (17 as of October 2026); no GPT or Claude access
- - Dedicated compute, priority GPU access, and higher rate limits require the Pro tier
- - Self-hosting, data residency, and custom SLAs are Enterprise-only
- - No path for non-engineers: everything assumes you can package a model and reason about replicas
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, Baseten or Fireworks AI?▼
Fireworks AI scores higher at 4.3/5 vs 4.1/5 for Baseten, making it the better-rated option in our comparison.
What is the difference between Baseten and Fireworks AI?▼
Baseten: Baseten hosts and scales machine learning models in production, serving open-source and custom models behind an API. Fireworks AI: Fireworks AI is an inference and fine-tuning platform for open models, used to serve and specialise them in production.
Is Baseten or Fireworks AI more affordable?▼
Baseten starts at €0/mo, Fireworks AI starts at €0/mo. Check the comparison table above for current pricing.
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