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Wafer vs Particle.AI
Wafer tunes serving stacks to run big open models faster. Particle.AI sells cheap Flash-class models with 1M context through Vercel AI Gateway. Speed and size versus price per token.
By The Subconscious Team · Updated
Wafer vs Particle.AI: key differences
Wafer and Particle.AI are both very early companies with small catalogs, but they compete on different numbers. Wafer's pitch is speed on the same weights: its agents tune batching, decoding, quantization and kernels, and it reports GLM 5.1 and DeepSeek V4 Pro each running 2x faster than a vLLM baseline. Access comes through a per-token serverless API or dedicated deployments built around your SLO. Particle's pitch is cost. On its own API it lists GLM 5.3 Flash at $0.15 in and $0.50 out and DeepSeek V4.1 Flash at $0.25 in and $1 out, with 1M context and $0.03 cache reads.
The workloads rarely overlap. Wafer suits a coding agent that wants a large model like GLM-5.3 to respond at interactive speed. Particle suits high-volume, per-token calls on small Flash models, or a price-optimized fallback route inside a gateway, with no new contract to sign. Particle's weak spot is latency, about 1.9 seconds to first token on Particle-1 via Vercel AI Gateway. Wafer's weak spot is proof: its speedups are measured against stock baselines, not against other tuned hosts.
What Wafer and Particle.AI do
Wafer
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 profile
Particle.AI
Particle AI is an early San Francisco infrastructure startup with a mission to make intelligence as cheap and abundant as electricity. The team works on post-training, inference optimization and distributed systems, all aimed at pushing down cost per unit of intelligence. It is still hiring its founding team and works fully in person. Mixpanel founder Suhail Doshi started it, with backing from Y Combinator, 8VC, Caffeinated Capital and angels including Patrick Collison, Adam D'Angelo and Aaron Levie.
Example models: Particle-1, DeepSeek V4.1 Flash, GLM 5.3 Flash
Full Particle.AI profileShould you choose Wafer or Particle.AI?
Wafer
Choose Wafer for
- Interactive coding agents on large open models.
- OpenAI and Anthropic compatible APIs for coding tools.
- Custom dedicated deployments tuned to a latency target.
Particle.AI
Choose Particle.AI for
- Cheap high-volume calls on DeepSeek and GLM Flash models.
- Full 1M context on a small model at low per-token prices.
- Adding a fallback route through Vercel AI Gateway with no contract.
Wafer vs Particle.AI at a glance
| Attribute | Wafer |  Particle.AI |
|---|---|---|
| Model access | Open weights | Open weights plus its own Particle-1 tune |
| Flagship models | GLM-5.3, DeepSeek V4.1 Flash | Particle-1, DeepSeek V4.1 Flash, GLM 5.3 Flash |
| Speed | ~168 tok/s on GLM-5.3; 2–2.8x vs stock vLLM or SGLang (vendor) | ~110 tok/s on Particle-1 (Vercel AI Gateway) |
| Price | Per token, prepaid credits | $0.15 in, $0.50 out (GLM 5.3 Flash); $0.25 in, $1 out (Particle-1) |
| Customization | Agent-tuned dedicated deployments | Unknown |
| Deployment | Serverless, dedicated | Serverless API, Vercel AI Gateway |
| Long context | Up to 1M (GLM-5.3, DeepSeek V4.1 Flash) | 1M |
Frequently asked questions
What is the difference between Wafer and Particle.AI?
Wafer tunes serving stacks to run big open models faster. Particle.AI sells cheap Flash-class models with 1M context through Vercel AI Gateway. Speed and size versus price per token.
When should I choose Wafer over Particle.AI?
Interactive coding agents on large open models; OpenAI and Anthropic compatible APIs for coding tools; Custom dedicated deployments tuned to a latency target.
When should I choose Particle.AI over Wafer?
Cheap high-volume calls on DeepSeek and GLM Flash models; Full 1M context on a small model at low per-token prices; Adding a fallback route through Vercel AI Gateway with no contract.
Is Wafer or Particle.AI cheaper?
Wafer: Per token, prepaid credits. Particle.AI: $0.15 in, $0.50 out (GLM 5.3 Flash); $0.25 in, $1 out (Particle-1). The cheaper choice depends on the model and workload.
Which has more context, Wafer or Particle.AI?
Wafer: Up to 1M (GLM-5.3, DeepSeek V4.1 Flash). Particle.AI: 1M.
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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.