Heroku defined a whole generation of application deployment. git push heroku main set the bar for what a good deploy loop feels like, buildpacks made runtimes disappear into the background, and add-ons turned integrating a database into a two-minute chore rather than a two-day one. Even now, teams building brand new applications quietly copy the operational model Heroku popularised.
What changed over the last few years is the price of that convenience and the surface area around it. Dyno costs climb quickly once traffic grows, the free tier that made Heroku famous for side projects is long gone, add-on pricing stacks on top of the dyno bill, and the ecosystem around Postgres, workers, and background jobs no longer feels as central as it once did. A generation of newer platforms has picked up the same developer experience and rebuilt it on top of containers, edge networks, and pricing models that match how modern applications actually consume resources.
This post walks through nine platforms that we see teams choose when they move away from Heroku, why each one exists, what they are best at, and the trade-offs to know before you start migrating. Some are fully managed, some are self-hosted, and one of them is ours.
What to look for when you leave Heroku
Before running through the list, it helps to name the axes that usually decide the choice, because most of the time the "right" answer changes depending on which of these matters most for the workload in question.
The runtime model is the first fork in the road, since some platforms run your code as always-on containers with steady memory footprints and others run it as short-lived function invocations that spin up on demand. Container platforms feel closer to Heroku dynos and suit long-running processes, background workers, and stateful protocols like WebSockets, while function platforms bill by request and shine for spiky, stateless traffic.
Pricing structure matters more than the sticker number. A flat monthly workspace fee plus per-second compute metering looks very different on a bill from a purely usage-based model, especially once you factor in bandwidth, storage, and add-ons like managed Postgres. Look at how the platform charges for the traffic patterns you actually have, not the ones the pricing page is optimised to make look cheap.
Regional coverage decides how close your services live to your users, and how close they live to any managed databases you already run on AWS, GCP, or Azure. If your Postgres is in us-east-1, running your app anywhere else adds latency you cannot make up for with faster code.
The edge tier in front of your app changes what you have to build yourself. Platforms fronted by a real CDN and WAF cover DDoS protection, TLS termination, and bot mitigation without you thinking about it, while platforms that only expose raw TCP leave those layers as your problem or as another vendor line on the bill.
Whose infrastructure your workload runs on is a question that used to have one answer (the vendor's) but increasingly has two. Bring-your-own-cloud and bring-your-own-cluster options let you use a managed control plane while keeping your workloads on infrastructure you already own or are contractually committed to.
With those in mind, here are the nine.
1. Suga
Suga is the platform we build, so consider this section the founder's pitch, but the honest one: we started Suga because we wanted the Heroku-style loop back without the parts that stopped working at scale. You design your application on a visual canvas, connect services to databases and networking, and deploy every change with a commit message and an audit trail. Deployments finish in seconds, environments can be forked whole for testing, and rolling back to any previous state is a single click.
Under the hood, workloads run as containers on managed infrastructure fronted by Cloudflare's global edge, so DDoS protection, WAF, and TLS termination apply to every service by default rather than being an integration you configure later. Pricing is per-seat with hosting credits bundled into every Pro seat, which tends to suit small teams running real production workloads without needing enterprise governance features up front.
The interesting move on the top tier is bring-your-own-cloud. On Enterprise, the same control plane can also drive infrastructure you already own on any cloud or on-premises, so you get a single deployment surface across the managed environment and workloads that need to run inside your existing cloud commitments.
Best for: teams that want the discipline of Infrastructure as Code without the multi-minute deployment cycles, that value environment forking for testing, and that want the edge tier included by default. Enterprises with existing cloud footprints who want a modern deployment experience without giving up their cluster of record.
Consider elsewhere if: you need very large per-instance memory ceilings today, you require published SOC 2 and ISO 27001 certifications (Suga is working toward these but has not published them yet), or you need a mature managed Postgres with point-in-time recovery as a first-party service.
Pricing: Free tier for evaluation, Pro is per-seat with $20 of hosting credits included per seat, Enterprise is custom with BYOC support.
2. Render
Render is the platform that most closely mirrors the Heroku operating model on modern infrastructure. You push a repo, Render detects the runtime or reads a Dockerfile, and the service comes up on managed infrastructure with automatic TLS, health checks, and zero-downtime deploys.
Where Render differentiates is on the range of instance sizes it offers, scaling up to 32 GB of RAM and 8 CPU per service on the top tier, and on its managed data services, which include a full Postgres product with point-in-time recovery, read replicas, and tiered compute. For teams whose main frustration with Heroku is dyno price-to-performance and add-on lock-in, Render often reads as the closest one-to-one replacement without the operational retraining.
Best for: teams who want the Heroku experience (push code, get a URL) with wider instance sizes, first-party managed Postgres, and published compliance certifications.
Consider elsewhere if: you need to run workloads on your own infrastructure, or your traffic pattern is spiky enough that flat workspace fees plus per-second compute metering does not fit as cleanly as pure usage-based billing.
Pricing: Free hobby tier with sleep, Pro from $25/month workspace fee plus compute metered by the second, Scale plan for larger teams.
3. Railway
Railway leans into the visual side of application composition. You drop services on a canvas, wire databases, workers, and crons together, and Railway takes care of stitching networking and secrets between them. Every project can spin up databases as containerised services, and per-service resources are set by picking a compute plan rather than an instance size.
For teams that liked Heroku's add-on marketplace but want the flexibility to run everything as containers, Railway hits a familiar note, and the developer loop of connecting to running containers over SSH is one of the more distinctive features on this list.
Best for: small teams and startups that want a single project canvas showing everything running in the application, plus straightforward per-minute billing on CPU and RAM.
Consider elsewhere if: you need L7 DDoS protection and a WAF at the edge without adding a separate provider, or you need to run on your own infrastructure.
Pricing: Hobby plan from $5/month with compute metered per minute, team plans add collaboration and higher limits.
4. Fly.io
Fly.io approaches the deployment problem from a very different angle. Fly runs your app on its own micro-VMs distributed across dozens of regions worldwide, so "run this container close to the user" is baked into how you design the deployment from the start, and the concepts you'd normally reach for on other platforms (a CDN in front of a single-region origin) are replaced by the workload actually living where the users are.
That model shines for latency-sensitive applications, multi-region databases, and anything with a genuinely global user base. It also introduces its own set of concerns around data locality, region failover, and how you design your topology, so Fly rewards teams who are willing to think about placement explicitly.
Best for: applications where round-trip latency to the user matters, multi-region topologies, and workloads that benefit from geographical distribution as a first-class primitive.
Consider elsewhere if: you have a single-region application backed by a single-region database, since much of Fly's distinctive value comes from the multi-region model.
Pricing: Usage-based across CPU, RAM, and bandwidth, with a small always-on hobby footprint free.
5. Vercel
Vercel is not a container platform, but it belongs on any honest list of Heroku alternatives because Heroku's Node.js and frontend workloads are exactly the traffic Vercel is optimised for. If your application is a Next.js frontend calling out to an API, Vercel gives you a build-and-deploy experience tuned for that pattern, with edge-cached static assets, serverless functions for the API layer, and preview environments on every pull request.
Where Vercel diverges from Heroku is in the runtime model. Instead of always-on dynos, Vercel runs your code as serverless functions that scale to zero between requests, which suits spiky HTTP workloads and hurts long-running background jobs, WebSocket connections, and anything that assumes a warm process.
Best for: Next.js frontends, marketing sites, JAMstack applications, and teams whose "backend" is mostly a thin API layer in front of managed services.
Consider elsewhere if: your workload is a long-running process, holds open WebSocket connections, runs background workers, or does heavy in-process caching that scale-to-zero would evict.
Pricing: Hobby free with usage limits, Pro from $20/user/month plus usage-based function invocations and bandwidth.
6. Porter
Porter takes the Heroku developer experience and puts it on top of Kubernetes. You get a push-to-deploy flow, sensible defaults for TLS and autoscaling, and the ability to graduate into raw Kubernetes primitives when you need them, all against a cluster Porter manages for you or one you connect from your own AWS, GCP, or Azure account.
The pitch is aimed at teams who see Kubernetes in their future but do not want to learn it as a prerequisite to shipping today. Porter is meaningfully more opinionated than raw Kubernetes and meaningfully less opinionated than a fully managed PaaS, so it lives in an interesting middle ground.
Best for: teams who expect to end up on Kubernetes eventually and want a gradient into it, or teams who already have cluster infrastructure they want to keep using with a nicer deploy loop on top.
Consider elsewhere if: you have no interest in Kubernetes concepts ever leaking into your day-to-day work, or you want a fully managed platform with no cluster-level decisions to make.
Pricing: Metered by compute usage on Porter Cloud, with self-hosted options for teams running on their own accounts.
7. Dokploy
Dokploy is a self-hosted PaaS that sits on top of Docker and Docker Swarm, giving you an application dashboard, database provisioning, and deploy flows on infrastructure you rent yourself. Because the platform itself is open source, you can run it on a single VPS and treat that VPS as your entire production footprint, which is a very different economic profile than any managed service.
The obvious appeal is cost control: for small side projects and internal tools, running everything on a single $5-$20 VPS with a self-managed platform on top can be dramatically cheaper than the equivalent workload on a managed PaaS, especially once bandwidth and add-ons stack up.
Best for: developers who are comfortable managing a VPS, want maximum control over the underlying host, and have workloads whose economics benefit from a fixed monthly VM bill rather than usage-based metering.
Consider elsewhere if: you would rather not manage host-level concerns like OS updates, backups, and VPS-level networking, or you want a platform team's support contract behind your production infrastructure.
Pricing: Open source and free to self-host, with a paid cloud option for teams that want the same platform without running the host.
8. Coolify
Coolify occupies the same self-hosted PaaS space as Dokploy and has grown a substantial community over the last few years. The user experience is broadly similar: connect a Git repository or a Docker image, provision databases as containers on the same host, and get automatic TLS through Let's Encrypt on top of your own server.
Coolify's differentiators are its plugin ecosystem, breadth of one-click databases and services, and a mature web UI that a lot of solo developers and small teams find pleasant to live in day to day.
Best for: solo developers and small teams who want a self-hosted PaaS with an active community, first-class support for a wide range of databases as containers, and no per-service pricing to worry about.
Consider elsewhere if: the workload really needs multi-region deployment, managed Postgres with point-in-time recovery, or a support contract with an SLA behind the platform itself.
Pricing: Open source and free to self-host, with paid cloud tiers available.
9. Dokku
Dokku is the original self-hosted "mini-Heroku" and still one of the leanest options on this list. Built around Docker and Heroku buildpacks, it gives you git push deploys onto a single server with almost no ceremony, and its plugin ecosystem covers Postgres, Redis, and the rest of the classic Heroku add-on lineup.
Where Dokku differs from the newer self-hosted PaaS options is in its philosophy: it is command-line first, opinionated in the Heroku direction (buildpacks, procfiles, twelve-factor conventions), and comfortable running on a single small server for years without much attention.
Best for: developers who want the original Heroku-style deploy loop reproduced on a single VPS, and teams who appreciate a CLI-first, minimal-web-UI approach to platform administration.
Consider elsewhere if: you prefer a web UI as the primary interface, or you need built-in multi-host orchestration without stepping up to Kubernetes.
Pricing: Open source and free to self-host.
How to choose
The question that decides most of these choices is not "which platform is best" but "which platform matches the character of the workload and the operational appetite of the team".
If the workload is a straightforward web application with a Postgres database, a managed platform like Suga, Render, or Railway will remove the largest amount of undifferentiated infrastructure work with the smallest amount of ceremony. Among those, Suga leads with an included edge tier and hosting credits per seat, Render leads on managed Postgres depth and published compliance, and Railway leads on the canvas-first project experience with SSH into running containers.
If latency to end users is central to the product, Fly.io's multi-region model is genuinely differentiated and worth investing in.
If the frontend is Next.js and the backend is mostly a thin API in front of managed services, Vercel is the natural fit even though the runtime model is different from Heroku's dynos.
If you are already on the road to Kubernetes or need to run on your own cluster, Porter is the smoother on-ramp than raw kubectl, and Suga's Enterprise plan can also run against clusters you already own.
If cost control is the primary motivation and you are comfortable managing a host, Dokploy, Coolify, and Dokku each solve the same problem with different flavours: Dokploy leans into a modern web UI on Swarm, Coolify offers the largest community and plugin ecosystem, and Dokku offers the closest reproduction of the original Heroku loop.
Migrating away from Heroku
Whichever destination you pick, the migration itself tends to follow the same rough pattern. Start by containerising the application, since every platform on this list can accept a Dockerfile and this is the deploy artefact most likely to survive any future platform change too. Move the database next by taking a fresh dump from Heroku Postgres and restoring it into the new managed Postgres or container-based database in your target platform, ideally cutting over during a low-traffic window with DNS pointed at both endpoints during the switch. Environment variables are usually the fiddliest part, since Heroku's config vars often accumulate over years, so treat this as an opportunity to audit and remove the ones nobody remembers adding.
Two decisions make the difference between a clean migration and a painful one. First, decide the runtime model deliberately: if you are moving to a serverless platform like Vercel, budget time to rework anything that assumed a warm long-running process, and if you are staying on containers, take the opportunity to consolidate any background workers and web dynos into a single application topology. Second, mirror your Heroku environment layout into whatever the target platform calls its environments (workspaces, projects, environments, spaces), and use the migration as a chance to formalise dev, staging, and production with clean isolation between them.
Frequently asked questions
Is Heroku itself still viable? Yes, Heroku is still a working product and still fits some teams well, particularly those that have been on it for years and have deep integration with its add-on ecosystem. The reason teams look elsewhere is usually a combination of cost curves that no longer match the workload, a preference for containers over buildpacks, or a desire to run on infrastructure the team already owns.
Can I combine platforms? Absolutely, and this is more common than the vendor pages suggest. A typical modern stack might run the frontend on Vercel, the backend on Suga or Render, and background jobs on Fly.io close to the database, all glued together with the same managed Postgres. Pick per workload rather than trying to fit everything onto one platform.
What about AWS, GCP, and Azure directly? They are always an option, and for large enterprises with dedicated platform teams they are often the right answer. For everyone else, the PaaS platforms on this list exist because the raw cloud consoles carry too much undifferentiated infrastructure work for a small team to absorb, so the sensible move is usually a managed PaaS with a bring-your-own-cloud escape hatch for the day the workload outgrows the vendor's own infrastructure.
How should I evaluate free tiers? As evaluation environments, not as long-term hosting. Every free tier on this list has constraints (sleep after inactivity, low resource ceilings, or usage caps) that make sense for kicking the tyres and stop making sense once you have real traffic. Budget for the paid tier of whichever platform you pick, and use the free tier to make sure the deploy loop and operational model actually suit your team before you commit.
What if I want to change platforms again later? Container-based platforms make this dramatically easier than the previous generation of buildpack-based PaaS, since the deploy artefact is a Docker image that any container platform can accept. If portability is a first-order concern, favour platforms that use Dockerfiles as the primary input and treat any platform-specific configuration as a thin wrapper around the container.