Top Serverless Inferencing Providers in 2025: A Market Comparison

As AI continues to power everything from search engines to healthcare diagnostics, inferencing — the act of running trained models in production — has become the real engine behind intelligent applications. In 2025, businesses are now adopting serverless inferencing for its ease, scalability, and cost-efficiency.

This blog explores the top serverless inferencing providers in 2025, comparing their features, pricing, performance, and ideal use cases — so you can make an informed choice.

What is Serverless Inferencing?

Serverless inferencing refers to running AI model predictions without managing the underlying infrastructure. You don't need to provision or manage GPU instances manually — you simply upload your model, send requests via an API, and pay only for what you use.

It's ideal for:

1. Applications with variable workloads
2. Scaling LLMs (Large Language Models)
3. Real-time recommendation engines
4. Startups needing GPU power on demand

Key Factors for Comparison

Before diving into the providers, here are the core factors to evaluate:

1. Scalability & Auto-scaling Latency
2. GPU Hardware Support (e.g., H100, L40S, A100)
3. Model Framework Compatibility (PyTorch, TensorFlow, ONNX, etc.)
4. Ease of Deployment (CLI, API, SDKs)
5. Pricing (pay-per-request, per-second, per-token)
6. Security, Region Availability & Compliance

Top Serverless Inferencing Providers in 2025

1. Cyfuture AI

1. Supported GPUs: NVIDIA H100, L40S, A100
2. Latency: Low; optimized for real-time LLM serving
3. Deployment: API-first, with developer SDKs and CLI
4. Extra: IDE Lab, RAG as a Service, GPU as a Service, Fine tuning
5. Pricing: Pay-as-you-go, competitive GPU-hour pricing
6. Region Focus: India-first, global reach

Best For: AI-first startups, enterprises needing LLM & RAG inferencing at scale in India or SE Asia.

2. AWS SageMaker Serverless Inference

1. Supported GPUs: A10G, A100
2. Latency: Medium to high (cold start issues)
3. Deployment: Integrated with AWS ecosystem
4. Pricing: Per-second usage + data transfer

Best For: Enterprise workloads already embedded in AWS.

3. Google Vertex AI (Predictions)

1. Supported GPUs: A100, T4
2. Latency: Medium
3. Deployment: Notebooks, APIs, GCP-integrated
4. Pricing: Per-request & by deployed compute tier

Best For: Data-heavy teams using GCP's end-to-end AI stack.

4. Azure ML Serverless Containers

1. Supported GPUs: V100, A100
2. Latency: Moderate
3. Deployment: YAML, CLI, Azure Portal
4. Pricing: Based on container runtime

Best For: Enterprises with heavy regulatory or compliance needs.

Read More: https://cyfuture.ai/blog/what-is-serverless-inferencing

5. Modal Labs

1. Supported GPUs: A100, A10G
2. Latency: Low
3. Deployment: Python scripts + Modal API
4. Pricing: Per-request or time-based

Best For: Fast iteration, ML developers, Python-native workflows.

6. Banana.dev

1. Supported GPUs: A100
2. Latency: Low
3. Deployment: Simple REST APIs
4. Pricing: Per-call

Best For: Small teams and indie developers deploying models like SD or LLaMA.

7. Replicate

1. Supported GPUs: Shared GPU clusters
2. Latency: Low
3. Deployment: Web UI or API
4. Pricing: Per-run

Best For: Open-source ML model demos, hobbyists, creative tools.

8. NVIDIA Inference Microservices (NIMs)

1. Supported GPUs: H100, A100
2. Latency: Extremely low
3. Deployment: Kubernetes or NVIDIA AI stack
4. Pricing: Enterprise licensing

Best For: Enterprises deploying mission-critical LLMs on-prem or in private clouds.

9. Anyscale Endpoints

1. Supported GPUs: A100, H100
2. Latency: Low
3. Deployment: Python SDK, REST API
4. Pricing: Pay-per-request or reserved capacity

Best For: Teams running distributed ML workloads or LLM-based microservices.

10. OctoML

1. Supported GPUs: A100, T4, V100
2. Latency: Very low (optimized runtime)
3. Deployment: API-driven with automation tools
4. Pricing: Consumption-based pricing

Best For: Organizations wanting maximum efficiency for model serving with automated optimization.

Interesting Blog: https://cyfuture.ai/blog/inferencing-as-a-service-explained

Serverless Inferencing Comparison Table

Provider	GPU Support	Cold Start	Pricing Model	LLM Support	Best For
Cyfuture AI	H100, L40S, A100	✅ Low	Pay-as-you-go	✅ Yes	Startups & Enterprises in Asia
AWS SageMaker	A10G, A100	❌ High	Per-second	✅ Yes	Large Enterprises
Google Vertex	T4, A100	⚠ Medium	Tiered/Per request	✅ Yes	GCP-aligned teams
Azure ML	V100, A100	⚠ Medium	By usage/time	✅ Yes	Regulated industries
Modal Labs	A100, A10G	✅ Low	Per job/call	✅ Yes	Python developers
Banana.dev	A100	✅ Low	Per call	✅ Yes	Indie ML apps
Replicate	Shared GPUs	✅ Low	Per run	✅ Yes	OSS demos & hobbyists
NVIDIA NIMs	H100, A100	✅ Low	Enterprise pricing	✅ Yes	AI-heavy enterprises
Anyscale	A100, H100	✅ Low	Pay-per-request	✅ Yes	Distributed ML workloads
OctoML	A100, T4, V100	✅ Low	Consumption-based	✅ Yes	Optimized model serving

Key Takeaways

1. Cyfuture AI leads with high-end GPU support (H100, L40S), India-first hosting, and developer-friendly APIs — all while staying cost-effective.
2. AWS, Azure, and Google offer enterprise-grade reliability, but are often more expensive and complex.
3. Anyscale and OctoML are strong niche players, focusing on distributed compute and model optimization.
4. Modal, Banana.dev, and Replicate are great for lightweight, fast-to-deploy models and experimentation.
5. NVIDIA NIMs offer the lowest latency, best suited for mission-critical enterprise deployments.

Read More: https://cyfuture.ai/blog/gpu-as-a-service-for-machine-learning-models

Final Thoughts

Choosing the right serverless inferencing platform in 2025 depends on your needs:

1. Want low-latency and fine-tuned GPU control? → Cyfuture AI
2. Need full cloud-stack integration? → AWS, Azure, or Google
3. Just deploying a hobby project? → Replicate or Banana
4. Building distributed AI apps? → Anyscale
5. Need cost-optimized serving? → OctoML