The Fireworks AI alternative that doesn't retire your model

For serverless open-model inference, Featherless is the closest like-for-like: the same OpenAI-compatible API, the same flagship models at level or lower list rates (Kimi K3 $3.00 / $15.00 on both; DeepSeek V4 Flash $0.42 against $0.88 per 1M in + 1M out, September 2026), plus 40,000+ open models that Fireworks serves only on rented GPUs or not at all. Fireworks remains the better choice for managed fine-tuning, batch inference at half price, speculative decoding on dedicated deployments, and Blackwell GPUs by the hour.

No. Chat is sold for interactive, human-driven use by the subscriber, not for API traffic, background automation, reselling or benchmarking. If an application is making the calls, you want the Developer plan. We're explicit about this because signing up for the wrong plan is the single most common source of frustration when people switch.

On list rates, mostly level. We match Fireworks Standard on Kimi K3, GLM 5.2, GLM 5.3 and gpt-oss-120B, undercut it on Kimi K2.6 (15% lower) and DeepSeek V4 Flash (52% lower), and lose on MiniMax M3 and on DeepSeek V4 Pro input. Two things change the sum in practice. Fireworks' Standard tier is shed first under load, so production traffic tends to end up on Priority or Fast at 20 to 50% more; we have one rate. And any model Fireworks has moved off serverless costs $8 per GPU-hour there, against cents per million tokens here.

For inference serving the gap with Nvidia is much narrower than it is for training, and on memory-bound workloads the MI325X's 256GB is an advantage: it sustains roughly 2.5x the GPT-OSS 120B throughput of an H100 on our infrastructure. Fireworks' Fast tier will beat our per-request speed on the handful of models it covers; if you need 100+ tokens a second on Kimi K3 today, that is a real advantage, priced at 1.5x. Where AMD matters more is model compatibility: most of the catalogue runs on it, not every model does. Ask before you migrate a specific model and we'll tell you straight. We also run Nvidia H100, B200 and B300 if that is the better fit.

Reserved, isolated GPU capacity plus the team to operate it, inference stack tuning, quantisation, batching, fine-tuning on your traffic, and on-call support. Not a separate professional services engagement. We benchmark your workload before you commit, then guarantee that performance level on the reserved capacity.

Yes, on qualifying models, applied automatically when a request reuses a prompt prefix of roughly 1,000+ tokens we served recently: Kimi K3 $0.30, GLM 5.2 $0.15, DeepSeek V4 Pro $0.20, DeepSeek V4 Flash $0.03, MiniMax M3 $0.06 and gpt-oss-120B $0.02 per million cached input tokens. Fireworks discounts more deeply on the DeepSeek models ($0.044 and $0.007), so if your workload is cache-heavy on DeepSeek, factor that in. Fireworks' cache also lives on a single replica and needs a session-affinity header to hit; ours needs no code change.

On Fireworks, the changelog announces the removal and points you at a successor; the last two rounds gave about two weeks' notice, and a model recommended as the migration target in June was itself removed in August. Our catalogue isn't curated for utilisation: public Hugging Face models with 100+ downloads are added automatically for supported architectures, less popular ones on request, and a model stays on the same API whether or not it is trending. If a model you depend on is on Fireworks' deprecation list, search our catalogue before you migrate. It is very likely already there.

Fine-tuning is available on the Business tier, on your dedicated capacity, and public full-weight fine-tunes of a supported architecture on Hugging Face are served at the base model's per-token rate (merge LoRAs first). We don't offer a discounted batch API, managed SFT or DPO with published training prices, GPU clusters by the hour, or Kimi K3 at its full 1M context; we serve 256K. If those are your main requirements, Fireworks is the better fit and we'd rather say so before you migrate.