Inferless is live · pricing from $0.000555/sec · last verified 26 Oct 2026 · viability healthy
Inferless
Deploy custom ML models on serverless Nvidia GPUs with per-second billing and scale-to-zero.
Inferless is the right tool when your inference traffic is spiky and you refuse to pay for idle GPUs. Per-second billing from $0.000092/sec on a shared T4, scale-to-zero with no charge when minimum replicas is zero, and dynamic batching are the three features that actually move the bill, and Cleanlab's reported ~90% GPU cost reduction backs the claim. It is the wrong tool for latency-critical user-facing paths, because first-time calls cold start in 10-20s, and for anyone who wants a curated model library rather than deploying their own. Compare Baseten, which acquired Inferless and now owns the roadmap, and Replicate if you would rather call hosted models than ship your own container.
Verdict generatedVerified 4d ago · liveness 75/100 · cite: rightaichoice.com/tools/inferless
- Teams with spiky or unpredictable inference traffic
- Startups and indie developers deploying custom open-source models
- Teams comfortable building Docker containers for their models
- Engineering teams that want the GPU bill tied directly to usage
- Latency-critical user-facing apps that cannot absorb 10-20s first-request cold starts
- Teams that want a curated library of ready-hosted models instead of deploying their own
- Organizations that need offline or air-gapped deployment
We scan live Reddit threads, YouTube comments, X posts, G2 reviews and other communities — and hand you an honest verdict in under a minute.
- Honest verdict, not marketing
- Real pros & cons from real users
- Attributed quotes with receipts
3 free scans · no card needed
Skip Inferless if your users hit a latency-sensitive path on the first request — a 10-20s cold start on first-time calls means anything synchronous needs a warm replica you pay for continuously.
Extra NFS volume storage past the free 50GB/month is billed at $0.30/GB/month, so large model artifacts add a recurring line item.
The Starter rate of $0.000555/sec is aimed at small teams and independent developers, and a shared T4 at $0.33/hr or a fractional dedicated T4 at $0.000092/sec sits at the cheap end of serverless GPU. Enterprise is priced as a discounted rate for larger volumes (minimum 100,000 inference requests per month, GPU concurrency of 50) rather than a public per-seat number. Compare Baseten and Modal on the same workload before committing, since all three bill per second and the machine mix usually
In short
Inferless — Deploy custom ML models on serverless Nvidia GPUs with per-second billing and scale-to-zero. Best for Teams with spiky or unpredictable inference traffic, Startups and indie developers deploying custom open-source models, Teams comfortable building Docker containers for their models. Plans from $0.000555/sec.
No head-to-head comparison for Inferless yet — the alternatives below are the closest matches we list.
What is Inferless?
Inferless — Deploy custom ML models on serverless Nvidia GPUs with per-second billing and scale-to-zero. Best for Teams with spiky or unpredictable inference traffic, Startups and indie developers deploying custom open-source models, Teams comfortable building Docker containers for their models. Plans from $0.000555/sec.
Inferless is a serverless GPU inference platform for running your own machine learning models without managing GPU clusters. You deploy from Hugging Face, Git, Docker, or your CLI, optionally turning on Auto-Rebuild so the endpoint picks up new model versions without manual re-imports. The platform scales from zero to hundreds of GPUs, bills per second for the time a model spends loading and answering requests, and charges nothing when minimum replicas is set to zero. Supported hardware is Nvidia A100, A10, and T4 in shared and fractional-dedicated configurations, with shared T4 starting at $0.000092/sec ($0.33/hr) and dedicated A100 on 80GB at $0.001491/sec ($5.36/hr). Other pieces buyers care about: dynamic batching for server-side request combining, NFS-like writable volumes with 50GB free per month, private endpoints with configurable scale-down, timeout, concurrency and webhook settings, and detailed call and build logs. Inferless is SOC-2 Type II certified, keeps execution environments isolated with Docker containerization, encrypts model storage with AES-256, and cites a typical prediction of up to 80% cost savings versus paying for idle GPUs. Cleanlab reported roughly 90% lower GPU bills and going live in under a day. The tradeoffs are real: first-time calls can cold start in 10-20 seconds, model size tops out at 16GB unless you talk to them, and there is no curated model marketplace. Inferless has joined Baseten, so roadmap and support direction now sit with the acquirer.
Key features
- Serverless GPU inference on Nvidia A100, A10, and T4
- Auto-scaling from zero to hundreds of GPUs via an in-house load balancer
- Per-second billing with no charge when minimum replicas is set to zero
- Deploy from Hugging Face, Git, Docker, or CLI
- Auto-Rebuild for automatic model re-import and CI/CD
- Dynamic batching via server-side request combining
- NFS-like writable volumes with 50GB free per month
- Private endpoints with configurable scale-down, timeout, concurrency, and webhook settings
- Sub-second cold starts for large models (first-time calls can run 10-20s)
- Custom runtime with Docker container support
- Shared and fractional dedicated GPU instances
- Model sizes up to 16GB on the standard path
- Detailed call and build logs for monitoring and debugging
- SOC-2 Type II certification, penetration testing, and vulnerability scans
- AES-256 encryption for model storage
Behind the verdict
Inferless sits in the narrow band of serverless GPU platforms where the pricing model, not the feature list, is the product. You are charged per second for the time a model spends in a healthy running state, measured from when it starts loading until it finishes a request, and when minimum replicas is set to zero nothing spins up and nothing is billed. On a shared T4 that is $0.000092/sec, or $0.33/hr; a dedicated A100 with 80GB runs $0.001491/sec, or $5.36/hr. Their own billing example puts 25,200 seconds across one and two A100 machines at $35.28 for the month. If your workload looks like that, the arithmetic is hard to argue with, and Cleanlab's public case study of roughly 90% lower GPU bills is the outcome you are buying. The engineering that makes the economics work is mostly invisible. An in-house load balancer handles scale-up and scale-down, dynamic batching combines requests server-side to lift throughput on high-QPS endpoints, and Auto-Rebuild re-imports your model automatically so the endpoint stays current. Volumes behave like NFS and support simultaneous connections across replicas, with 50GB free each month and extra storage billed at $0.30/GB/month. Private endpoints let you tune scale-down, timeout, concurrency, and webhook behavior per deployment. Custom runtimes let you put whatever software and dependencies your model needs into the container. The friction points are worth stating plainly rather than buried. First-time calls can cold start in 10-20 seconds; subsequent calls are inference-bound, but that first hit rules out synchronous user-facing latency paths unless you keep a replica warm and pay for it. Model size is capped at 16GB on the standard path, with larger models handled case by case. There is no curated model marketplace, so you are bringing your own container and your own Docker comfort. Governance is split across tiers: GPU concurrency of 5 and 15-day log retention on the startup tier versus concurrency of 50 and 365-day log retention on Enterprise, which is the usual shape but worth knowing before you plan an audit. Ownership is the open question. Inferless has joined Baseten, announced on the site and framed as "Inferless joins baseten." That usually means continuity for existing workloads and uncertainty about how long the standalone product keeps receiving attention, so anyone signing a multi-year commitment should ask directly. For a spiky workload with tolerant latency and a team comfortable in Docker, the pricing model still earns its place. For a latency-sensitive consumer app or a team that wants to rent someone else's hosted models, look at Baseten or Replicate instead.
Models under the hood
as of 2026-10-23
Limitations
- First-time calls can cold start in 10-20 seconds, so anything synchronous and user-facing needs a warm replica you are paying for.
- Model size tops out at 16GB on the standard path, with larger models handled case by case rather than self-serve.
- There is no curated model marketplace, so you bring your own container and need Docker competence to use the custom runtime well.
- Log retention and GPU concurrency differ sharply by tier: the startup tier gives concurrency of 5 and 15 days of log retention, while Enterprise gives 50 and 365 days.
- Inferless has joined Baseten, so the standalone roadmap is now set by the acquirer.
as of 2026-09-30
Researching Inferless? Get your full AI stack in 60 seconds.
Free, no signup — tell us your goal and get tools matched to your budget & existing stack.
Is Inferless still active?
Inferless is live · pricing from $0.000555/sec · last verified 26 Oct 2026 · viability healthy.
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: October 2026
How we score →What do users say about Inferless?
What people actually say about Inferless — is it worth it?
Our community sentiment research written Jul 6, 2026 is more than 90 days old, so it is being re-reviewed against current data and is not shown.
How we verified this
Every listed fact about Inferless— pricing, features, links, status — is re-read from the vendor's own pages by our verification pipeline and each change is logged with a date; the last pass ran on 26 Oct 2026. Pages mix model-assisted synthesis with human review; see how we verify.
Verification history
We have re-verified Inferless 20 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.
- — changed use cases, description, our verdict, features, hidden costs, integrations, limitations, switching in, switching out, who should skip it, pricing tiers, pricing fit, setup time, summary, who it suits
- — changed limitations
- — changed SSL certificate
- — changed SSL certificate
- — changed limitations
- — changed description, who should skip it, our verdict, features, hidden costs, switching in, setup time, switching out, models, pricing fit, limitations
Showing the 6 most recent of 20 verification passes.
Free to cite with attribution — this page re-verifies continuously.
How much does Inferless cost in 2026?
Inferless: Plans from $0.000555/sec. Pricing last verified 26 Oct 2026.
Plans compared
Every published Inferless tier, its price as the vendor publishes it, who it fits, and what it adds over the tier below. The 12-month cost projection follows.
| Plan | Price | Billing | Who it's for |
|---|---|---|---|
| Starter | $0.000555/sec | usage | Small teams and independent developers deploying a model in minutes who want the bill to track usage rather than a seat count.Adds: Starting tier: $0.000555/sec with 10 hours of free credit and no credit card required, plus a stated $30 free credit. |
| Enterprise | Custom | quote | Fast-growing startups and larger organizations that need higher concurrency and longer log retention at a discounted rate.Adds: Adds a 100,000 inference requests per month minimum, GPU concurrency of 50, 365 days of log retention, unlimited webhook endpoints, and support via private Slack with a support engineer. |
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.
Where the pricing makes sense
The company stage and team size where Inferless's pricing actually pencils out — and where peers do it cheaper.
The Starter rate of $0.000555/sec is aimed at small teams and independent developers, and a shared T4 at $0.33/hr or a fractional dedicated T4 at $0.000092/sec sits at the cheap end of serverless GPU. Enterprise is priced as a discounted rate for larger volumes (minimum 100,000 inference requests per month, GPU concurrency of 50) rather than a public per-seat number. Compare Baseten and Modal on the same workload before committing, since all three bill per second and the machine mix usually
Has Inferless' pricing changed?
No plan price for Inferless has moved since we began logging in Aug 2026; the pricing page is re-read on every verification pass.
Who should pick Inferless?
Pick Inferless if you are Teams with spiky or unpredictable inference traffic, Startups and indie developers deploying custom open-source models, Teams comfortable building Docker containers for their models, Engineering teams that want the GPU bill tied directly to usage.
Look elsewhere if you are Latency-critical user-facing apps that cannot absorb 10-20s first-request cold starts, Teams that want a curated library of ready-hosted models instead of deploying their own, Organizations that need offline or air-gapped deployment.
Skip it if Skip Inferless if your users hit a latency-sensitive path on the first request — a 10-20s cold start on first-time calls means anything synchronous needs a warm replica you pay for continuously.
GeneratedReal-world workflow fit
Concrete scenarios for the personas Inferless actually fits — and what changes day-one when you adopt it.
You push a Llama 2 13B container to Git, connect it to Inferless, and deploy against the $30 free credit without entering a card for the initial 10 hours.
Outcome: An endpoint that idles at zero replicas overnight and costs nothing until someone calls it, so hobby traffic does not turn into a monthly GPU bill.
You deploy a Stable Diffusion ControlNet model, enable dynamic batching, and let the in-house load balancer scale from zero to several machines during peak.
Outcome: Throughput rises under load and the bill tracks actual usage — the pattern Cleanlab reported as roughly 90% lower GPU costs with a live time under one day.
You move a private embedding endpoint onto Inferless with a custom runtime, private endpoint settings, and NFS volumes for shared artifacts.
Outcome: The model stays behind configurable concurrency and timeout controls, with call and build logs for debugging and Auto-Rebuild keeping the endpoint on the latest import.
Use cases
- Deploy Llama 2 13B on serverless GPUs and let concurrency scale to zero overnight
- Serve a Stable Diffusion ControlNet endpoint that only wakes when someone calls it
- Run your own embedding model for document processing and pay per second instead of per month
- Scale a computer vision API from zero to hundreds of concurrent requests during traffic spikes
- Turn on dynamic batching to lift throughput on a high-QPS inference endpoint
- Expose a model behind a private endpoint with tuned concurrency and timeout settings
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 developer already comfortable with Docker: minutes to a live endpoint if the container is ready, and under a day end-to-end for a first production model — Cleanlab reported going live in less than a day. Teams that need to build the custom runtime and container from scratch should budget longer for that work before the first deploy.
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 a hand-rolled GPU cluster: package the model in a Docker container, deploy from Git or CLI, and let scale-to-zero replace the idle instances you were paying for.
- →From Hugging Face Inference Endpoints: re-import the same model repository through Inferless and turn on Auto-Rebuild instead of manually re-deploying.
- →From a per-month GPU rental: redeploy the container and compare the per-second bill against the flat monthly rate over a full month of real traffic.
- ↗To Baseten: the two products now share ownership, so the port is mostly container and endpoint configuration rather than a rewrite.
- ↗To Replicate: switch to calling hosted model endpoints instead of deploying your own container if you would rather not maintain a runtime.
- ↗To a managed GPU cluster: move the same Docker image onto reserved instances if your traffic becomes steady enough that per-second billing stops winning.
Integrations
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
What are the best Inferless alternatives?
The closest alternatives to Inferless we list are Baseten, Replicate and Modal. Ranked by direct product-type match, not category overlap.
| Alternative | Pricing | Starts at | Last verified |
|---|---|---|---|
| Baseten | Freemium | Pricing not published | — |
| Replicate | Freemium | Pricing not published | — |
| Modal | Freemium | Pricing not published | — |
Alternatives to Inferless
View allTools that pair well with Inferless
Common stack mates teams adopt alongside Inferless, with the specific reason each pairing earns its keep.
Baseten
Deploy AI models in production with dedicated GPU inference and pre-optimized Model APIs.
Replicate
Run image, video, speech, music and LLM models through one Replicate API call and one bill
Modal
Serverless GPU cloud for production AI — declare Python functions and hardware in one file, scale to thousands of GPUs, pay per second.
Frequently asked questions
Used Inferless? Help shape our editorial sentiment research.