Overview
Serverless compute is the layer where you rent execution instead of managing servers. That covers a wide range of tools: function runtimes that run your own code on request, managed backend platforms that add a database, authentication, and storage around function execution, event and workflow services that pass messages between systems and trigger jobs on a schedule, and GPU or notebook compute for training and running AI models without owning hardware. Free serverless functions are the most common starting point, but the other three groups solve real problems of their own, so matching the tool to the job matters more than picking whichever free tier looks biggest.
What to look for in free serverless functions and compute
A useful free tier gives you enough room to build something real rather than a demo you outgrow in a week. For function runtimes, that means a workable request allowance alongside enough compute time and memory to run typical workloads without constant throttling. Concurrency matters as much as the headline allowance, because a burst of traffic can exhaust a low concurrency ceiling long before a monthly cap does.
Managed backend platforms need to be judged as a whole stack rather than by any single meter. Look at how the database, authentication, storage, and function limits work together, because a generous function allowance paired with a thin database tier still leaves you rebuilding the app elsewhere. Event and workflow services are worth checking for retry behavior and delivery guarantees, not just throughput, since a dropped event is often harder to notice than a rejected request.
Gotchas to watch for
The catch with serverless compute free tiers is rarely the headline limit itself, it is what surrounds it. Some platforms pause an idle project after a period of inactivity, which is fine for a side project and disruptive for anything with real users. Others meter usage across several dimensions at once, so a single slow function or a chatty background job can burn through one allowance while the rest sit untouched. GPU and notebook compute tends to bill in credits rather than a flat monthly limit, so a demanding workload can exhaust a free allocation faster than a lighter one running the same length of time.
10 free serverless & compute tiers compared
| Product | Type | Free tier includes | Est. value | Card required |
|---|---|---|---|---|
| AWS LambdaFTV 49 | Free tier | One million free requests per month for Lambda function invocations. | $6.87 / month | Not stated |
| Cloud Functions for FirebaseFTV 58 | Free tier | No-cost up to 2 million Cloud Functions invocations per month. | $7.00 / month | No |
| Azure FunctionsFTV 48 | Free tier | Monthly free grant of 1 million executions for Consumption plan, per subscription. | $6.87 / month | Not stated |
| DigitalOcean FunctionsFTV 49 | Free tier | 90,000 GiB-seconds of compute per month per account. | $7.59 / month | Not stated |
| SupabaseFTV 56 | Free tier | 500,000 Edge Function invocations. | $25 / month | Not stated |
| ConvexFTV 50 | Free tier | 1M function calls per month. | $8.50 / month | Not stated |
| AppwriteFTV 47 | Free tier | 750K executions per month. | $8.00 / month | Not stated |
| InngestFTV 66 | Free tier | 50,000 executions per month included. | $25 / month | No |
| Amazon EventBridgeFTV 52 | Free tier | EventBridge Scheduler with up to 14,000,000 invocations per month free. | $14 / month | Not stated |
| ModalFTV 41 | Free tier | 100 containers and 10 GPU concurrency. | Not stated | Not stated |
Best free serverless & compute picks by use case
Best for: You want a function runtime with the broadest documentation and the widest choice of supported languages
AWS Lambda works with nearly every language and framework in common use, and it plugs directly into the rest of AWS, so triggers, storage, and queues are already available without extra wiring. If your app already lives on AWS, keeping the function code there too saves you a separate account and a separate set of credentials to manage.
Best for: You need a managed backend that bundles authentication and storage around your functions
Supabase pairs a Postgres database with authentication, storage, and edge functions in one project, so a small app can ship without stitching together separate vendors. That single-project setup is the whole appeal: one dashboard instead of four.
Best for: You need to route events between many AWS services without writing custom glue code
EventBridge understands the event formats that AWS services already emit, so wiring a trigger from one service to another is mostly configuration rather than code. That matters once you have more than a couple of services talking to each other and the glue code starts to sprawl.
Best for: You are training or serving AI models and need GPU access without owning hardware
Modal lets you run Python functions on GPU hardware on demand, so a training job or an inference endpoint starts when called and stops when idle. That is useful while you are still figuring out what a workload actually needs, before you commit to renting a GPU box around the clock.
Best for: You want your database and your serverless functions to live in one deployment
Convex ties its database, functions, and realtime updates together in a single deployment, so a change to one is reflected everywhere without separate syncing logic. Skipping that sync layer is the main reason to reach for it over wiring a database and a function runtime together yourself.
Frequently asked questions
What counts as serverless compute for a free tier comparison?
It covers function runtimes that run your own code on request, managed backend platforms that add a database and authentication around functions, event and workflow services that route messages and triggers between systems, and GPU or notebook compute for AI workloads. These groups solve different problems even though they sit under one label.
Are free serverless functions enough to run a real app?
Often yes, if the app is mostly request driven and stays within the platform's request and compute limits. Background jobs, retries, and steady traffic are the usual reasons a free tier gets exhausted faster than expected, so check those specifically before committing.
Should I pick a managed backend or plain function hosting?
Pick a managed backend when you want database, authentication, and storage to move together with your functions. Pick plain function hosting when you already have those pieces elsewhere and only need code that runs in response to a request or an event.
What is the usual catch with a serverless compute free tier?
The common catch is that usage is metered across several dimensions at once, such as requests, compute time, storage, and egress, so a tier that looks generous on one meter can still force an upgrade because of another. Idle projects on some platforms also pause after a period of inactivity.
Do I need a credit card for GPU or notebook compute free tiers?
It varies by vendor, and some platforms that give free compute credits still ask for a card up front to prevent abuse. Check the specific listing before you sign up, since this detail changes more often than the underlying compute allowance.
Can event and workflow services replace a message queue I already run?
Sometimes, if your workload is mostly about routing events between managed services rather than processing a high volume queue yourself. A dedicated queue still makes sense when you need strict ordering or very fine control over consumption, which most event routing services do not prioritize.
