Modal vs NVIDIA NIM: Token Pricing, Speed & Intelligence
Full comparison of Modal and NVIDIA NIM — live token pricing, latency, throughput, context window, strengths, weaknesses, and best use cases. Rates sourced from public APIs — verify before use.
Modal
Managed inference API — 10 shared-endpoint models with per-token pricing
Modal Shared Endpoints serve a fixed catalog of models from Modal-managed pools. Modal manages the hardware and autoscaling, and usage is billed per token. Shared Endpoints have model-specific concurrency limits on concurrent in-flight requests. Prices are published rates from modal.com/docs/guide/supported-models.md, re-read 2026-10-31.
NVIDIA NIM
Optimized inference microservices for NVIDIA GPUs — deploy any model on your own hardware
NVIDIA NIM (NVIDIA Inference Microservices) provides containerized, GPU-optimized inference microservices for deploying LLMs and other AI models on NVIDIA hardware. NIM packages models with TensorRT-LLM optimizations, enabling production-grade throughput on H100, A100, and RTX GPUs. Available via the NVIDIA API catalog for cloud access or as self-hosted containers for on-premises and private cloud deployments.
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Live token pricing
Strengths and weaknesses
Modal
NVIDIA NIM
Key differentiators
Modal Shared Endpoints provide managed per-token inference on a fixed 10-model catalog with published cached-input pricing and no infrastructure to manage.
An inference platform that lets you run the same TensorRT-LLM-optimized microservice both on NVIDIA's cloud API and on your own NVIDIA GPU infrastructure with consistent performance characteristics.
Frequently asked questions
Modal FAQs
What models does Modal offer on Shared Endpoints?
Modal Shared Endpoints currently serve 10 models: GLM 5.3 Flash NVFP4, DeepSeek V4.1 Flash 1017, DeepSeek V4.1 Flash, GLM 5.3 Flash, Muse Spark Open, GLM 5.3, Inkling NVFP4, Qwen3.8-Max, Qwen3.8-Max VL, and Kimi K3. Use the model table above for per-model pricing and capability details. Source: Modal's published Shared Endpoints catalog, re-read 2026-10-31.
How much does Modal charge per 1M tokens?
Modal Shared Endpoints input prices range from $0.15/1M (GLM 5.3 Flash NVFP4) to $3.00/1M (Kimi K3). All models include cached-input pricing. See the model table for full per-model rates.
Does Modal offer prompt caching?
Yes. All 10 models in the Modal Shared Endpoints catalog have published cached-input pricing, ranging from $0.005/1M (DeepSeek V4.1 Flash 1017) to $0.30/1M (Kimi K3). Depending on the model, the cached-input rate is 77.5% to 98% below the standard input rate. Source: Modal's published Shared Endpoints rates, re-read 2026-10-31.
NVIDIA NIM FAQs
What is NVIDIA NIM?
NVIDIA NIM (Inference Microservices) are containerized AI inference packages optimized for NVIDIA GPUs using TensorRT-LLM. They can be deployed on NVIDIA's cloud API catalog or self-hosted on any NVIDIA GPU infrastructure.
How do I access NVIDIA NIM models?
You can access NIM models via the NVIDIA API catalog at build.nvidia.com — free for prototyping. For production self-hosting, NIM containers are available through NVIDIA AI Enterprise licensing.
What models does NVIDIA NIM support?
NIM supports a broad catalog including Llama 4, Mistral, Qwen3, DeepSeek V3/R1, Nemotron, and NVIDIA's own optimized model variants. The catalog grows with each major open-weight model release.
Provider resources
Modal — Managed inference API — 10 shared-endpoint models with per-token pricing
Modal Shared Endpoints serve a fixed catalog of models from Modal-managed pools. Modal manages the hardware and autoscaling, and usage is billed per token. Shared Endpoints have model-specific concurrency limits on concurrent in-flight requests. Prices are published rates from modal.com/docs/guide/supported-models.md, re-read 2026-10-31.
Modal Shared Endpoints provide managed per-token inference on a fixed 10-model catalog with published cached-input pricing and no infrastructure to manage.
NVIDIA NIM — Optimized inference microservices for NVIDIA GPUs — deploy any model on your own hardware
NVIDIA NIM (NVIDIA Inference Microservices) provides containerized, GPU-optimized inference microservices for deploying LLMs and other AI models on NVIDIA hardware. NIM packages models with TensorRT-LLM optimizations, enabling production-grade throughput on H100, A100, and RTX GPUs. Available via the NVIDIA API catalog for cloud access or as self-hosted containers for on-premises and private cloud deployments.
An inference platform that lets you run the same TensorRT-LLM-optimized microservice both on NVIDIA's cloud API and on your own NVIDIA GPU infrastructure with consistent performance characteristics.
Key strengths compared
Modal
- Managed hardware and autoscaling — no infrastructure to operate
- Published per-token rates including cached-input pricing
- OpenAI-style per-token billing across all 10 models
- Small fixed catalog of 10 models
- Model-specific concurrency limits on shared pools
NVIDIA NIM
- TensorRT-LLM optimizations deliver maximum throughput on NVIDIA GPUs
- Self-hosted deployment option for air-gapped and private cloud environments
- Broad model catalog: Llama, Mistral, Qwen, DeepSeek, and NVIDIA-optimized variants
- Self-hosted path requires NVIDIA GPU infrastructure investment
- Cloud API catalog is primarily for evaluation — production at scale needs on-prem or cloud GPU contracts
Key weaknesses compared
Modal
- Small fixed catalog of 10 models
- Model-specific concurrency limits on shared pools
- Prices are published rates re-read 2026-10-31, not continuously collected
NVIDIA NIM
- Self-hosted path requires NVIDIA GPU infrastructure investment
- Cloud API catalog is primarily for evaluation — production at scale needs on-prem or cloud GPU contracts
- Less suited for teams without existing NVIDIA hardware
Provider category context
Modal is a inference api. NVIDIA NIM is a inference api, founded in 2024. Both are inference api providers — the comparison is primarily about pricing, model selection, and feature differentiation within the same tier.
How to choose between them
Both Modal and NVIDIA NIM host open-weight models. The key differentiators are latency, throughput, and which specific model versions each provider offers. Check the speed metrics above — inference API providers often differ significantly on tokens-per-second for the same model. Pricing is typically competitive between them; availability of specific model versions (e.g., Llama 3.1 405B, DeepSeek V3) may be the deciding factor.