Decart vs Modal
Relationship
Cogito and Modal Inference do comparable work on model hosting; both also serve buyers who need to serve a model in production; same layer and the same scale band (growth-stage private).
Assembled from the recorded fields for this pair, not hand-checked. The comparison below is read from each company’s own profile.
3 of 8 capabilities — Shares model hosting, model inference and model training.
Ludbee capability tags · from the product recordsShared product type — Both ship infrastructure service.
Ludbee product recordsSame scale band — Both growth-stage private.
Ludbee scale bands · from valuation and funding figuresAligned comparison
Capability overlap
Shared · 3
Not verified for Modal · 5
Recorded for Decart. Modal’s product records say nothing either way — a missing record is not a missing capability.
Not verified for Decart · 2
Recorded for Modal. Decart’s product records say nothing either way — a missing record is not a missing capability.
Products, side by side
Algorithmic pairing — assembled from recorded fields, not hand-checked
Decart
Infrastructure service
The Decart Optimization Stack (DOS) is Decart's inference and training optimization infrastructure, spanning hardware-aware model design, kernel tooling, proprietary compilers and inference optimization. It is sold to hardware providers and AI teams as engagements covering benchmark optimization, customer-defined kernel and compiler work, cross-workload efficiency gains, and profiler and simulator licensing, and is marketed as hardware-agnostic across GPUs, TPUs, Trainium and AMD accelerators.
Modal
Infrastructure service
Serverless GPU compute: a Python decorator puts a function on an accelerator, scales it from zero to thousands of containers and stops billing when it stops running — aimed at inference, fine-tuning and batch jobs rather than reserved clusters.
Batch execution of large jobs across Modal's fleet, described as one line of code on the product page.
Serve, scale and optimise model inference on Modal's runtime, with sub-second cold starts and autoscaling across regions.
Isolated, instantly-started containers for running untrusted or agent-generated code at scale — the primitive behind AI app-generation products.
Managed training runs on Modal's fleet, configured in Python alongside the rest of a team's code.
No counterpart
Decart sells these in a stack layer with no product recorded for Modal yet — nothing on the other side to compare them against.
Application
Delulu is Decart's consumer mobile app for AI photo transformation, published on iOS and Android by Decart.AI, Inc. Its store listing describes it as a way to 'Turn any photo into something fun, weird, or just really good-looking, with a single tap', browsing preset styles and creating stickers to share.
Real-time video editing platform that edits and transforms live video at streaming speed, for enterprises, brands, creators and live/streaming/gaming use cases; built on Decart's DOS infrastructure.
API service
Interactive world model for Physical AI, generating realistic, controllable real-time simulation environments for training autonomous systems, distributed as an API/SDK product built on Decart's Optimization Stack (DOS) infrastructure.
Model API
Cogito is Decart's OpenAI-compatible LLM inference API, serving frontier open-weight models including Kimi K2.6, Kimi K2.7 Code, GLM-5.2, Qwen3 235B and GPT-OSS 120B across Trainium, TPU and GPU capacity. A self-serve Standard tier is billed per token, while an ultra-fast reserved tier advertises 1,000+ tokens per second. It is built on Decart's own DOS optimization stack.
Modal sells these in a stack layer with no product recorded for Decart yet — nothing on the other side to compare them against.
Developer tool
Hosted notebooks backed by Modal's GPUs, for profiling and experimenting without provisioning a machine.