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Modal vs Inception
Modal rents serverless GPUs for any model you bring. Inception sells per-token access to its own fast diffusion LLMs.
By The Subconscious Team · Updated
Modal vs Inception: key differences
Modal has no catalog or per-token price. Teams deploy their own weights and code on GPUs from T4 to B300, billed per second, with an H100 at $3.95 an hour list. Inception hosts Mercury 2.5, a closed reasoning model with tool use and 260K context at $0.20 in and $0.75 out. Inception reports 1,100+ tokens per second; Artificial Analysis measures about 640.
Modal fits custom models, fine-tunes and batch jobs where you control the stack. Inception fits teams that want fast output from an API with nothing to deploy. Inception's weights are closed, so private deployments go through Inception directly.
What Modal and Inception do
Modal
Modal is serverless compute with GPUs attached. A developer decorates a Python function with the hardware it needs, such as gpu="H100", and Modal builds the container, schedules it, autoscales it and scales it back to zero. Billing runs per second with no minimum increment, from $0.59 an hour for a T4 to $3.95 for an H100 at list. Containers can hold up to 8 GPUs across T4 through B300.
Example models: none hosted by default; teams deploy their own, such as open LLMs on vLLM or Whisper
Full Modal profile
Inception
Inception builds diffusion large language models (dLLMs). Every other LLM on this list generates text one token at a time; Inception's Mercury models start from a rough draft of a whole block and refine it over a few passes, producing many tokens in parallel. That ties speed less to output length and lets the models run fast on ordinary NVIDIA GPUs rather than custom chips. The company was founded by professors from Stanford, UCLA and Cornell behind core work on diffusion, Flash Attention and DPO, and is led by Stanford's Stefano Ermon. It raised a $50M seed in November 2025 led by Menlo Ventures, with Mayfield, M12, NVentures, Snowflake and Databricks, plus Andrew Ng and Andrej Karpathy as angels.
Example models: Mercury 2.5, Mercury 2
Full Inception profileShould you choose Modal or Inception?
Modal vs Inception at a glance
| Attribute | Modal |  Inception |
|---|---|---|
| Model access | Bring your own weights | Closed weights (Mercury dLLMs) |
| Flagship models | None hosted | Mercury 2.5, Mercury 2 |
| Speed | ~1s container boot | 1,100+ tok/s (vendor); ~640 measured |
| Price | Per second; H100 $3.95/hr list | $0.20 in, $0.75 out (Mercury 2.5) |
| Customization | Run any training code | Fine-tuning on request |
| Deployment | Serverless GPU containers | Serverless API, Bedrock, Azure, private |
| Long context | Depends on the model you deploy | 260K |
Frequently asked questions
What is the difference between Modal and Inception?
Modal rents serverless GPUs for any model you bring. Inception sells per-token access to its own fast diffusion LLMs.
When should I choose Modal over Inception?
Serving your own or fine-tuned models; Bursty GPU jobs billed per second; Inference, training and batch on one platform.
When should I choose Inception over Modal?
Fast output with nothing to deploy; Code autocomplete and voice agents; Low per-token prices.
Is Modal or Inception cheaper?
Modal: Per second; H100 $3.95/hr list. Inception: $0.20 in, $0.75 out (Mercury 2.5). The cheaper choice depends on the model and workload.
Which has more context, Modal or Inception?
Modal: Depends on the model you deploy. Inception: 260K.
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.