Inferless
Deploy ML models on serverless GPUs with per-second billing and auto-scaling
Inferless is a solid pick if you have spiky inference workloads and want to avoid idle GPU costs. Its per-second billing and auto-scaling are real, and the $30 free credit lowers the barrier. However, first-request cold starts of 10-20 seconds and a need for Docker expertise mean it's not for latency-sensitive user-facing apps or teams wanting a managed model library. Compare with Baseten (which acquired Inferless) and Replicate for similar needs.
Verified 22h ago · liveness 76/100 · cite: rightaichoice.com/tools/inferless
- Deploying custom LLMs and diffusion models without managing GPU clusters
- Teams with spiky inference workloads wanting to slash GPU costs via per-second billing
- Startups needing to scale from zero to many GPUs on demand with $30 free credit
- Developers who prefer deploying from Hugging Face or Git repositories
- Real-time user-facing apps that can't tolerate 10-20s first-request cold starts
- Teams needing static GPU allocation with predictable monthly costs
- Users wanting a curated model library—there's no built-in marketplace
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Skip Inferless if you need real-time responses under 10 seconds, want a managed model library, or require a predictable monthly flat cost—its per-second billing and cold starts favor spiky, non-latency-critical workloads.
Going past 50GB of NFS volume storage adds $0.3/GB/month, which can accumulate if you host large models.
Inferless fits startups and indie devs with spiky inference workloads who want to avoid idle GPU costs. Per-second billing saves money compared to hourly instances like AWS or GCP, and the $30 free credit lowers the barrier. Compared to Baseten (which acquired Inferless), pricing is comparable; Replicate charges per request, which may be cheaper for low-volume.
In short
Inferless — Deploy ML models on serverless GPUs with per-second billing and auto-scaling. Best for Deploying custom LLMs and diffusion models without managing GPU clusters, Teams with spiky inference workloads wanting to slash GPU costs via per-second billing, Startups needing to scale from zero to many GPUs on demand with $30 free credit. Free to start; paid plans from $0.001.
What's new in Inferless
Checked 16 days agoAcross the latest 3 updates: 3 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.
What people actually say about Inferless — is it worth it?
We ran a structured research pass across product reviews, community discussions, and post-purchase forum threads to surface the patterns vendors won't publish themselves. Below: the recurring strengths, the hidden costs people mention most, and the cohort that consistently regrets adopting this tool.
20 mentions across 3 sources (Hacker News, Product Hunt, Bluesky) · researched Jul 6, 2026.
Average across the 3 sources that answered — each source counts once, not each post.
- +Per-second billing with zero idle costs saves money on spiky workloads.
- +Deploy from Hugging Face, Git, Docker, or CLI in minutes.
- +Auto-scales from zero to hundreds of GPUs based on demand.
- +SOC-2 Type II certified and uses isolated Docker containers.
- +Responsive support that helps small teams deploy models.
- −Cold starts 10-20 seconds for large models can be slow.
- −Recent Baseten acquisition creates uncertainty about future.
- −No uptime guarantees or performance benchmarks shared publicly.
- −Limited integrations compared to competitors like Modal.
- −Scaling to hundreds of GPUs unverified in community feedback.
- • Cold start time may incur costs before actual inference.
- • Writable NFS volumes storage may have separate billing.
Viability Score
How well maintained and how widely used is Inferless? Built from what the vendor actually publishes (docs, changelog, tutorials, integrations, pricing), whether the site is live, and how much real users discuss it. How we calculate this
Last calculated: September 2026
How we score →Key Features
- Serverless GPU inference
- Auto-scaling from zero to hundreds of GPUs
- Per-second billing with zero idle costs
- Deploy from Hugging Face, Git, Docker, or CLI
- Automatic CI/CD with auto-rebuild
- Dynamic batching
- NFS-writable volumes (50GB free)
- Private endpoints with scale-down and timeout settings
- Sub-second cold starts for large models
- Custom runtime with Docker container support
- Support for Nvidia T4, A10, A100 GPUs (shared/dedicated)
- Fractional dedicated GPU instances
- Detailed call and build logs
- New UI for model management and monitoring
- SOC-2 Type II certified
About Inferless
Inferless is a serverless GPU inference platform that lets you deploy custom machine learning models without managing infrastructure. You can launch models from Hugging Face, Git, Docker, or your CLI, with automatic CI/CD rebuilds to keep your endpoints current. It scales from zero to hundreds of GPUs based on demand, charges per second for compute time, and includes features like dynamic batching, writable volumes, and private endpoints. It supports Nvidia T4, A10, and A100 GPUs, with shared and dedicated options, and offers sub-second cold starts for large models. SOC-2 Type II certified with AES-256 encryption, Inferless is pitched at startups and developers who want to keep costs low for spiky inference workloads and avoid paying for idle GPU time. Compared to traditional GPU hosting, it can deliver significant savings—one customer reported a 90% reduction in GPU bills—but you'll need Docker comfort and a tolerance for first-request latency. The platform starts with $30 free credit and pricing from $0.33/hour for a fractional T4, making it accessible for small teams and independent developers. Recently, Inferless joined Baseten (a competing serverless GPU platform), which may affect its roadmap and support.
Behind the Verdict
Inferless shines for teams deploying custom LLMs like Llama 2 13B or Stable Diffusion control-net without managing GPU clusters. The per-second billing means you only pay for compute time, so zero idle requests cost nothing—this is a huge win for spiky workloads. The platform's auto-scaling from zero to hundreds of GPUs is handled by their in-house load balancer, which keeps overhead minimal. You can deploy from Hugging Face, Git, Docker, or CLI, and enable auto-rebuild for continuous delivery. Features like dynamic batching, NFS volumes (50GB free), private endpoints, and detailed logs are practical for production. The new UI introduced in August 2024 improves model management and monitoring. Security is solid with SOC-2 Type II, AES-256 encryption, and container isolation. However, the 10-20 second cold start on first requests is a dealbreaker for real-time user-facing apps. Also, you need Docker comfort and tolerance for some infrastructure learning. The platform has no curated model library—you bring your own models. Pricing varies by GPU type: T4 shared starts at $0.000092/sec ($0.33/hr), A10 at $0.000170/sec ($0.61/hr), A100 at $0.000745/sec ($2.68/hr). Dedicated options cost more but give consistent performance. There's a Starter tier at $0.000555/sec (that's $2/hr) though the detailed pricing shows T4 shared at $0.33/hr—this discrepancy may be outdated. Enterprise tier requires min 100k inference requests/month and offers GPU concurrency of 50. Given the recent acquisition by Baseten, evaluate their roadmap and support continuity.
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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.
Deploy a Hugging Face model with auto-scale
Outcome: Engineer connects Hugging Face repo, selects GPU, enables auto-rebuild, and gets a public endpoint in minutes. They set scale-down to zero to avoid idle costs, and the model scales to handle load.
Scale a custom model for a new feature
Outcome: Founder uses Docker CLI to push a custom runtime, sets up dynamic batching, and leverages per-second billing to keep costs low during launch spike. They monitor logs via the UI.
Prototype an embedding model for document processing
Outcome: Data scientist deploys a custom embedding model via CLI, uses NFS volume to store vectors, and processes hundreds of books daily, paying only for compute time, thanks to auto-scaling.
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 2026-08-31
Limitations
- Inferless is a serverless GPU inference platform with per-second billing and auto-scaling from zero to hundreds of GPUs.
- Pricing varies by GPU type (Nvidia T4, A10, A100) and shared vs. dedicated options, starting at $0.000092/sec for a shared T4.
- Deployments are supported from Hugging Face, Git, Docker, or CLI, with features like dynamic batching, private endpoints, and NFS volumes with 50GB free monthly.
- The platform is SOC-2 Type II certified.
as of 2026-08-29
Verification history
We have re-verified Inferless 18 times since . Each pass re-reads the vendor's own pages and re-checks every listed field against that evidence; passes where nothing had changed are marked as such.
- — re-checked, vendor evidence unchanged
- — re-checked, vendor evidence unchanged
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-checked, vendor evidence unchanged
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
Showing the 6 most recent of 18 verification passes.
Free to cite with attribution — this page re-verifies continuously.
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
Ideal for
Solo developers and small teams exploring serverless GPU inference with a $30 free credit and per-second billing.
What this tier adds
Entry point with per-second billing and no upfront cost; includes 10 hours free credit.
Enterprise
Custom
Ideal for
Growing startups and enterprises needing high concurrency, custom credits, and dedicated support.
What this tier adds
Adds GPU concurrency of 50, 365-day log retention, and 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 fits startups and indie devs with spiky inference workloads who want to avoid idle GPU costs. Per-second billing saves money compared to hourly instances like AWS or GCP, and the $30 free credit lowers the barrier. Compared to Baseten (which acquired Inferless), pricing is comparable; Replicate charges per request, which may be cheaper for low-volume.
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.
Deploying from Hugging Face or Git takes under 30 minutes if you're comfortable with Docker. CLI deployment is similar. If you need a custom runtime, budget an hour to set up the Dockerfile. You can test with 10 hours of free credit without a credit card.
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: Deploy your model container to Inferless via Docker and use the CLI to create an endpoint; you avoid managing instances.
- →From Replicate: If you have a custom model, you can deploy it on Inferless with your own Docker image and get per-second billing instead of per-request.
- →From a traditional GPU server: Convert your setup to a Docker container and use the Inferless CLI to deploy, eliminating fixed hardware costs.
- ↗To Baseten: Since Inferless joined Baseten, you can migrate your endpoints to Baseten's platform, which offers similar serverless GPU features.
- ↗To Replicate: Use Cog to containerize your model and deploy to Replicate if you prefer per-request pricing.
- ↗To Modal: If you need more complex workflows, Modal offers serverless containers with a similar model.
Resources & Guides
Tutorials & Learning
YouTube returned 6 videos for “Inferless”, and we withheld 6: 6 could not be judged, because “Inferless” is a single word that other videos use for other things. We are showing none, because we could not prove any of them are about Inferless.
Official links
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Frequently Asked Questions
Best-of guides
Topics
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