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DeepInfra vs Wafer
Wafer tunes serving stacks with agents to run open models faster on the same weights. DeepInfra runs a huge catalog at the lowest price. Speed versus cost, roughly.
By The Subconscious Team · Updated
DeepInfra vs Wafer: key differences
Wafer and DeepInfra make different bets on the same open weights. Wafer uses AI agents as GPU performance engineers. They profile a workload, try configurations across batching, decoding, quantization, engines, kernels and hardware, and deploy the winner, then keep re-tuning on NVIDIA or AMD. Wafer reports its tuned Qwen 3.5 397B running 2.8x faster than stock SGLang, and GLM 5.1 and DeepSeek V4 Pro each 2x faster than a vLLM baseline. DeepInfra's bet is price. It serves 150+ models at or near the floor, with part of that edge coming from heavy default quantization.
The pricing models differ as much as the tech. Wafer Pass is a flat-rate subscription from $10 a week that covers every hosted model and plugs into Claude Code, Cline and OpenHands. DeepInfra bills per token with no minimums. Wafer is a very young company with a small hosted catalog, and its speedups are self-reported against stock baselines. Coding agents that want big open models at interactive speed, or teams with a strict latency SLO and no kernel engineers, lean Wafer. Bulk, cost-first jobs on a wide catalog lean DeepInfra.
What DeepInfra and Wafer do
DeepInfra
DeepInfra is the price floor for open-model inference. Developers treat it as the reference point for what a token should cost, with small models like Llama 3.1 8B at $0.02 per million and DeepSeek V4 Flash at $0.14 in and $0.28 out. The catalog covers 150+ open models across text, image and speech behind a fully OpenAI-compatible API. There are no minimums, setup fees or contracts on the shared API.
Example models: DeepSeek V4 Flash, Llama 3.1 8B
Full DeepInfra 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. 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: Qwen 3.5 397B Turbo, GLM 5.1 Turbo
Full Wafer profileShould you choose DeepInfra or Wafer?
DeepInfra
Choose DeepInfra for
- Cost-first bulk jobs on a wide open catalog
- Metered per-token billing for variable usage
- Access to many models Wafer does not host
Wafer
Choose Wafer for
- Flat-rate agentic coding in Claude Code or Cline
- Dedicated endpoints tuned to a strict latency SLO
- Hedging GPU supply across NVIDIA and AMD
DeepInfra vs Wafer at a glance
| Attribute | DeepInfra | Wafer |
|---|---|---|
| Model access | Open weights | Open weights |
| Flagship models | DeepSeek V4 Flash, Llama 3.1 8B | Qwen 3.5 397B Turbo, GLM 5.1 Turbo |
| Speed | ~33 tok/s on DeepSeek V4 Pro (FP4) | 2–2.8x vs stock vLLM or SGLang |
| Price | From $0.02 per 1M | Wafer Pass from $10 a week |
| Customization | No managed fine-tuning | Agent-tuned dedicated deployments |
| Deployment | Shared API, no contracts | Serverless pass, dedicated |
| Long context | 66K on FP4 DeepSeek V4 Pro | Varies by model |
Frequently asked questions
What is the difference between DeepInfra and Wafer?
Wafer tunes serving stacks with agents to run open models faster on the same weights. DeepInfra runs a huge catalog at the lowest price. Speed versus cost, roughly.
When should I choose DeepInfra over Wafer?
Cost-first bulk jobs on a wide open catalog; Metered per-token billing for variable usage; Access to many models Wafer does not host.
When should I choose Wafer over DeepInfra?
Flat-rate agentic coding in Claude Code or Cline; Dedicated endpoints tuned to a strict latency SLO; Hedging GPU supply across NVIDIA and AMD.
Is DeepInfra or Wafer cheaper?
DeepInfra: From $0.02 per 1M. Wafer: Wafer Pass from $10 a week. The cheaper choice depends on the model and workload.
Which has more context, DeepInfra or Wafer?
DeepInfra: 66K on FP4 DeepSeek V4 Pro. Wafer: Varies by model.
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.