Inferless
Serverless GPU inference with per-second billing and zero idle costs.
Inferless delivers genuine cost savings for spiky inference workloads with per-second billing and auto-scaling. The $30 free credit and easy deployment from Hugging Face or Git make it accessible, but first-request cold starts of 10-20 seconds and a need for Docker expertise may be friction points. A solid pick for custom model deployment without infrastructure headaches.
Verified 17d ago · liveness 70/100 · cite: rightaichoice.com/tools/inferless
- Deploying custom open-source LLMs and diffusion models with minimal infrastructure management
- Teams needing auto-scaling for sporadic inference workloads to minimize GPU costs
- Startups wanting to avoid fixed GPU costs with per-second billing and $30 free credit
- Developers who prefer deploying from Hugging Face or Git repositories
- Teams needing static GPU allocation with predictable monthly costs
- Users needing extensive pre-built model library – Replicate has more curation
- Real-time user-facing apps sensitive to first-request cold starts (10-20s latency)
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Skip Inferless if you need to train models, require on-premises deployment, or have latency-sensitive real-time applications that can't tolerate 10-20 second cold starts.
Exceeding the free 50GB NFS storage incurs $0.30 per GB per month, so large model sets can add cost.
Inferless's per-second pricing is ideal for spiky or low-volume inference. At $0.33/hr for a shared T4, it's cheaper than many traditional GPU providers for intermittent use, but for steady high-volume workloads, dedicated GPU reservations (e.g., AWS) may be more cost-effective. The $30 free credit lets you test without risk.
In short
Inferless — Serverless GPU inference with per-second billing and zero idle costs. Best for Deploying custom open-source LLMs and diffusion models with minimal infrastructure management, Teams needing auto-scaling for sporadic inference workloads to minimize GPU costs, Startups wanting to avoid fixed GPU costs with per-second billing and $30 free credit. Free to start; paid plans from $0.000555/mo.
What's new in Inferless
Checked 17 days agoAcross the latest 3 updates: 1 launch and 2 news mentions.
Model Inference Explained: Key Concepts and Applications
Educational post covering latency, throughput, and deployment strategies for model inference.
Effortless Autoscaling for Your Hugging Face Application
Guide on deploying Hugging Face models with automatic scaling from zero to many GPUs.
Introducing Inferless New UI
Launched redesigned interface with improved model management, monitoring, and deployment workflows.
Viability Score
How likely is Inferless to still be operational in 12 months? Based on 4 signals — momentum (how recently it shipped), wrapper dependency, revenue model, and web presence.
Last calculated: July 2026
How we score →Key Features
- Deploy from Hugging Face, Git, Docker, or CLI
- Serverless auto-scaling from zero to hundreds of GPUs
- Per-second billing with zero idle costs
- Dynamic batching for higher throughput
- NFS-writable volumes (50GB free)
- Private endpoints with scale-down and timeout settings
- Sub-second cold starts for large models
- SOC-2 Type II certified
- AES-256 encryption
- Automated CI/CD with auto-rebuild
- Detailed call and build logs
- Supports Nvidia T4, A10, A100 GPUs (shared/dedicated)
- Fractional dedicated GPU instances
- $30 free credit to start
- New UI for model management and monitoring (Aug 2024)
About Inferless
Inferless is a serverless GPU inference platform that enables you to deploy custom machine learning models without managing infrastructure. Deploy from Hugging Face, Git, Docker, or CLI with automatic CI/CD rebuilds. It auto-scales from zero to hundreds of GPUs based on demand, with per-second billing and zero idle costs. Features include dynamic batching for higher throughput, NFS-writable volumes, private endpoints, and sub-second cold starts for large models. The platform is SOC-2 Type II certified, with isolated Docker containers and AES-256 encryption. Ideal for teams deploying open-source LLMs, diffusion models, or custom NLP models who want to avoid managing GPU clusters. Compared to traditional GPU hosting, Inferless offers lower costs for spiky workloads and a generous $30 free credit to start.
Behind the Verdict
You need a GPU inference platform that doesn't penalize you for having idle time. Inferless charges per second, so if your model sits idle, costs are zero. This is a lifesaver for startups and teams with unpredictable traffic. The $30 free credit lets you test with no risk. We'd reach for this when deploying custom open-source models from Hugging Face or Git repos. The new UI (Aug 2024) streamlines monitoring and management. But where it bites: the 10-20 second cold start on first request makes it a poor fit for latency-sensitive real-time apps. And you'll need Docker comfort to set up custom runtimes. Compared to Replicate or Fal.ai, Inferless is less about curated models and more about your own. For teams with ML engineers who can Dockerize a model, Inferless is a cost-saver. For non-technical teams, stick with managed APIs.
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Real-world workflow fit
Concrete scenarios for the personas Inferless actually fits — and what changes day-one when you adopt it.
You have a custom NLP model on Hugging Face. You paste the model URL into Inferless, choose an A10 GPU, and set auto-scaling from 0 to 2 replicas. Inferless rebuilds the model image automatically and provides a private endpoint.
Outcome: Within minutes, you have a scalable API endpoint, and you only pay for actual inference seconds. No server management.
You previously ran a Stable Diffusion service on an expensive dedicated GPU. You migrate the Docker image to Inferless, enable dynamic batching and auto-scaling down to zero.
Outcome: Your monthly bill drops by ~80% because you no longer pay for idle time. The service scales up during peak hours and down during quiet periods.
You need to deploy a proprietary LLM with private endpoints, SOC-2 compliance, and isolated Docker containers. You configure Inference in Enterprise mode with a dedicated A100 GPU and set 365-day log retention.
Outcome: You get a secure, compliant inference endpoint with guaranteed performance, and you only pay for compute seconds used.
Use Cases
- Deploy Llama 2 13B models with serverless GPUs and auto-scaling
- Scale computer vision inference from zero to hundreds of concurrent requests
- Run custom embedding models for document processing, paying per-second
- Deploy NLP models from Hugging Face without manual setup
- Use dynamic batching to increase throughput for high-QPS APIs
- Set up private endpoints with configurable scale-down and timeout for enterprise security
Models Under the Hood
as of 2026-07-14
Limitations
- Cloud-only, no on-premises deployment.
- Cold start optimized for sub-second responses.
- GPU instances shared or dedicated with varying RAM.
- Enterprise features require custom pricing.
- Starter plan includes 10 hours free credit.
as of 2026-06-30
12-month cost
Project the real annual outlay, including the implied monthly cost when only an annual tier is published.
Vendor list price only. Add-on usage, seat overages, and contract minimums are surfaced under Hidden costs & gotchas.
Plans compared
For each published Inferless tier: who it actually fits, and what it adds vs. the previous tier. Cross-reference the cost calculator above for projected annual outlay.
Starter
$0.000555/sec (variable by GPU)
Ideal for
Independent developers and small teams deploying low-to-moderate volume models with dynamic auto-scaling.
What this tier adds
Pay-per-second billing with no upfront cost; includes shared and dedicated GPU instances; 50GB free NFS storage per month.
Enterprise
Custom
Ideal for
Fast-growing startups and large organizations needing high volume, dedicated support, and longer log retention.
What this tier adds
Custom pricing with discounted rates; minimum 10,000 or 100,000 inference requests per month; GPU concurrency of 5 or 50; 15 or 365 day log retention; private Slack support.
Where the pricing makes sense
The company stage and team size where Inferless's pricing actually pencils out — and where peers do it cheaper.
Inferless's per-second pricing is ideal for spiky or low-volume inference. At $0.33/hr for a shared T4, it's cheaper than many traditional GPU providers for intermittent use, but for steady high-volume workloads, dedicated GPU reservations (e.g., AWS) may be more cost-effective. The $30 free credit lets you test without risk.
Setup time & first value
How long it actually takes to get something useful out of Inferless — broken out by persona, not the marketing-page minute.
For a Hugging Face deployment: 5 minutes to paste URL and configure GPU. For custom Docker: 15-30 minutes to build and test the container. The $30 free credit eliminates the first financial hurdle.
Switching to or from Inferless
How to bring data in from common predecessors and how to get it back out — written for the switcher, not the buyer.
- →From AWS SageMaker: Package your model as a Docker container and deploy via Inferless CLI or Git; auto-scaling replaces your manual instance management.
- →From Replicate: Export your model weights and dependencies; create a Dockerfile and deploy via Inferless's custom runtime.
- →From Banana: Migrate your model's Docker image directly; adjust environment variables and redeploy.
- ↗To AWS EC2: Export your Docker image and run on your own GPU instances; you'll lose auto-scaling and pay for idle time.
- ↗To Kubernetes: Migrate your Docker container to a Kubernetes cluster with GPU nodes; more complex but gives full control.
Integrations
Resources & Guides
Official links
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