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Fireworks AI vs Wafer
Wafer tunes inference stacks with agents and reports 2x or more over stock engines. Fireworks is the kind of already-tuned host Wafer's own caveats point to.
By The Subconscious Team · Updated
Fireworks AI vs Wafer: key differences
Each sells faster open models through custom serving, at very different stages. Fireworks is established, with 400+ models, a reported $1B+ run rate, and third-party measurements of 167 to 174 tokens per second on DeepSeek V4 Pro. Wafer came out of Y Combinator in 2025 and uses AI agents to profile a workload, try configs across batching, decoding, quantization and kernels, deploy the winner, then keep re-tuning as traffic changes. Wafer reports its tuned DeepSeek V4 Pro running 2x faster than a vLLM baseline. That comparison is against stock engines, and Wafer's own profile advises comparing it against tuned hosts like Fireworks before buying.
Delivery differs too. Wafer Serverless bills per token from prepaid credits and plugs into Claude Code, Cline and OpenHands. Fireworks bills per token or per GPU hour and adds managed SFT, DPO and RL plus SOC 2, HIPAA and ISO. Wafer also runs on NVIDIA and AMD, which helps teams hedge GPU supply. A developer running coding agents all day may still want to test Wafer's tuned stacks. A company that needs certifications, a broad catalog and custom training fits Fireworks.
What Fireworks AI and Wafer do
Fireworks AI
Fireworks AI was founded in 2022 by former Meta PyTorch engineers led by CEO Lin Qiao, and it sells speed on open models. Its custom serving stack has posted 167 to 174 tokens per second on DeepSeek V4 Pro in third-party measurements, several times what most GPU peers hit on the same model. The catalog holds 400+ models across text, vision, audio and embeddings, served through an OpenAI-compatible API. In July 2026 it raised a $1.505B Series D at a $17.5B valuation, with a reported $1B+ run rate and 40T+ tokens a day.
Example models: DeepSeek V4.1 Flash, GLM 5.3, Kimi K3
Full Fireworks AI profileWafer
Wafer builds AI agents that act as GPU performance engineers, then sells inference on the stacks those agents tune. The company came out of Y Combinator's Summer 2025 batch as a "Cursor for CUDA" that turned slow PyTorch into custom kernels. Founders Emilio Andere and Steven Arellano are based in San Francisco. Wafer raised a $4M seed led by Fifty Years in April 2026 and a $40M Series A co-led by Marathon and Chemistry in September 2026 at a $220M post-money valuation, with AMD Ventures and Wing joining. Its agents profile a workload, generate candidate configs across batching, decoding, quantization, engines, kernels and hardware, measure each one and deploy the winner.
Example models: GLM-5.3, DeepSeek V4.1 Flash
Full Wafer profileShould you choose Fireworks AI or Wafer?
Fireworks AI
Choose Fireworks AI for
- Independently measured speed on DeepSeek V4 Pro
- A 400+ model catalog rather than a small hosted list
- Managed fine-tuning with enterprise certifications
Wafer
Choose Wafer for
- Per-token access for coding agents
- Dedicated endpoints re-tuned to a strict latency SLO
- Hedging GPU supply across NVIDIA and AMD
Fireworks AI vs Wafer at a glance
| Attribute | Fireworks AI | Wafer |
|---|---|---|
| Model access | Open weights | Open weights |
| Flagship models | DeepSeek V4.1 Flash, GLM 5.3, Kimi K3 | GLM-5.3, DeepSeek V4.1 Flash |
| Speed | 167–174 tok/s on DeepSeek V4 Pro | ~176 tok/s on GLM-5.3; 2–2.8x vs stock vLLM or SGLang (vendor) |
| Price | Fine-tunes served at base price | Per token, prepaid credits |
| Customization | SFT, DPO, RFT; Training API | Agent-tuned dedicated deployments |
| Deployment | Serverless, dedicated GPUs | Serverless, dedicated |
| Long context | Full 1M on DeepSeek V4.1 Flash, GLM 5.3 | Up to 1M (GLM-5.3, DeepSeek V4.1 Flash) |
Frequently asked questions
What is the difference between Fireworks AI and Wafer?
Wafer tunes inference stacks with agents and reports 2x or more over stock engines. Fireworks is the kind of already-tuned host Wafer's own caveats point to.
When should I choose Fireworks AI over Wafer?
Independently measured speed on DeepSeek V4 Pro; A 400+ model catalog rather than a small hosted list; Managed fine-tuning with enterprise certifications.
When should I choose Wafer over Fireworks AI?
Per-token access for coding agents; Dedicated endpoints re-tuned to a strict latency SLO; Hedging GPU supply across NVIDIA and AMD.
Is Fireworks AI or Wafer cheaper?
Fireworks AI: Fine-tunes served at base price. Wafer: Per token, prepaid credits. The cheaper choice depends on the model and workload.
Which has more context, Fireworks AI or Wafer?
Fireworks AI: Full 1M on DeepSeek V4.1 Flash, GLM 5.3. Wafer: Up to 1M (GLM-5.3, DeepSeek V4.1 Flash).
Related comparisons
Running long-horizon agents?
If your agents run past 200K tokens, compare both against Subconscious. Our inference stack treats a long-horizon trace as the primary workload, so speed, cost, and accuracy hold up deep into the trace.