# Heroku vs Fly.io: Which Platform Is Better in 2026?

> Compare Heroku and Fly.io for deployment, pricing, scaling, multi-region apps, databases, and developer control. Find the right platform for your team.
- **Author**: parth-kanpariya
- **Published**: 2026-01-05
- **Modified**: 2026-08-24
- **Category**: Alternatives
- **URL**: https://kuberns.com/blogs/heroku-vs-flyio-vs-kuberns/

---

Heroku vs Fly.io is fundamentally a choice between platform abstraction and infrastructure control. Heroku gives teams a mature PaaS built around dynos, buildpacks, managed data services, and release conventions. Fly.io exposes configurable Machines, regional placement, volumes, networking, and a container-oriented deployment model.

That difference affects more than the first deployment. It determines who chooses compute resources, how environments are represented, where state lives, how regions are added, and how much of the production architecture the development team must operate.

Heroku is the more direct fit when a team wants the platform to make more infrastructure decisions. Fly.io is the stronger fit when regional placement and Machine-level control are part of the application design. The sections below evaluate that tradeoff from source code through production operation.

## The Short Answer: Heroku Abstraction or Fly.io Control?

* **Choose Heroku** for established PaaS conventions, managed application processes, Heroku Postgres, Pipelines, Review Apps, and marketplace add-ons.
* **Choose Fly.io** for flexible Machine sizes, container-oriented deployment, regional placement, persistent volumes, Anycast networking, and greater infrastructure control.
* Heroku runs application process types in selected dynos. Fly.io runs Docker images inside lightweight virtual machines called Fly Machines.
* Heroku publishes plan-based dyno prices. Fly.io uses resource-based billing for Machines, storage, networking, and managed services.
* Heroku provides native autoscaling for eligible dyno tiers. Fly.io provides autostop and autostart plus a configurable metrics-based autoscaler.
* Heroku Pipelines and Review Apps provide an integrated delivery model. Fly.io supports GitHub Actions, review-app patterns, and separate configuration for multiple environments.
* **Choose Kuberns** when your team wants an Agentic AI platform for deployment, built for full-stack and complex backend projects. Connect a GitHub repository, and Kuberns prepares the deployment configuration with CI/CD, monitoring, and alerts included.

## Two Platform Models at a Glance

| Category | Heroku | Fly.io |
| --- | --- | --- |
| Platform model | Managed PaaS | Application platform using Fly Machines |
| Compute unit | Dyno | Lightweight virtual machine |
| Source deployment | Git, GitHub, CLI, API, or container registry | Fly CLI, GitHub Actions, or prebuilt image |
| Build options | Supported or custom buildpacks; container images | Dockerfile, Buildpack, Nixpack, or prebuilt image |
| Application configuration | App settings, config vars, process types, and add-ons | `fly.toml`, secrets, services, process groups, and Machines |
| Regional model | Platform regions and eligible Private Spaces | Direct Machine placement across documented regions |
| Persistent storage | Dyno filesystem is ephemeral | Local persistent Fly Volumes |
| Databases | Heroku Postgres, Key-Value Store, and add-ons | Managed Postgres, extensions, or self-managed services |
| Scaling | Change dyno type or quantity; eligible native autoscaling | Machine sizing and count, autostop/autostart, metrics autoscaling |
| Environments | Pipelines, Review Apps, staging, and production apps | Separate apps/configurations, GitHub Actions, and review-app patterns |
| Pricing model | Selected dyno and data-service plans | Usage-based resource billing |
| Best fit | Teams prioritizing PaaS conventions and less infrastructure control | Teams prioritizing regions, resource control, and container workflows |

Heroku and Fly.io both remove the need to manage physical servers. Heroku makes more platform decisions for the team, while Fly.io exposes more infrastructure controls through its Machines and configuration model.

### Does Fly.io Give You More Control Than Heroku?

Yes. Fly.io gives developers more direct control over the application control plane than Heroku. Heroku hides most infrastructure decisions behind dynos, buildpacks, config vars, add-ons, Pipelines, and platform conventions. Fly.io exposes more of the runtime through Fly Machines, `fly.toml`, services, regions, volumes, private networking, and Machine-level scaling.

That control is useful when an application needs explicit regional placement, container behavior, private networking, or persistent volumes. It also means the team owns more production decisions. With Heroku, the platform decides more for you. With Fly.io, the team decides more directly.

> *If your main reason for leaving Heroku is platform direction or pricing, compare the broader [Heroku alternatives in 2026](https://kuberns.com/blogs/heroku-alternatives/) before choosing a more infrastructure-heavy platform.*

## From Source Code to Running Infrastructure

[Heroku](https://kuberns.com/blogs/what-is-heroku/) is a managed Platform as a Service that builds application code, creates releases, and runs web, worker, and other process types in dynos. Teams can attach managed data services and marketplace add-ons and organize delivery through Heroku Pipelines. The [official Heroku platform](https://www.heroku.com/) presents these capabilities through an application-centered workflow.

![Heroku cloud application platform](https://kuberns-blogs-media.s3.ap-south-1.amazonaws.com/heroku-home.png)

[Fly.io](https://kuberns.com/blogs/what-is-flyio/) is an application platform built around Fly Machines, which are lightweight virtual machines that run Docker images. Teams can select regions and Machine resources, attach volumes, configure services through `fly.toml`, and route traffic through the [Fly.io platform](https://fly.io/).

![Fly.io application deployment platform](https://kuberns-blogs-media.s3.ap-south-1.amazonaws.com/flyio-deployment.png)

The central decision is how much infrastructure control the team wants. Heroku offers a more opinionated PaaS experience. Fly.io provides more direct control over compute, storage, regions, and networking.

### Heroku Git Releases vs Fly Launch and Fly Deploy

Heroku supports deployment through Git, GitHub, its CLI and API, and a container registry. Its build system detects supported buildpacks for many applications, installs dependencies, creates a release, and starts the configured process types. A Procfile can define explicit web, worker, and other process commands, but it is not required for every supported project.

A typical Heroku workflow includes:

1. Create or connect an application.
2. Configure variables and required data services.
3. Deploy source code or a container image.
4. Select dyno types and process quantities.
5. Review the release, logs, and metrics.

Fly.io's [Fly Launch workflow](https://fly.io/docs/reference/fly-launch/) starts with `fly launch`, which detects supported projects, creates a Fly App, selects a preferred region, produces a default `fly.toml`, and can deploy the application. `fly deploy` builds or retrieves the Docker image and updates the application's Machines.

Every Fly.io application runs from a Docker image, but that does not mean every developer must write a Dockerfile manually. Fly.io can build from a Dockerfile, Buildpack, Nixpack, or prebuilt image. Fly Launch can generate build configuration for supported projects.

A typical Fly.io workflow includes:

1. Install and authenticate the Fly CLI.
2. Run `fly launch` and review the detected configuration.
3. Adjust `fly.toml`, secrets, region, Machine size, and services when required.
4. Provision volumes, Postgres, or other services required by the application.
5. Run `fly deploy` and inspect deployment status, logs, and metrics.

Heroku provides more dashboard and platform conventions. Fly.io makes application and infrastructure configuration more visible and editable.

## Dynos, Machines, and Regional Architecture

### Managed Dynos or Configurable Machines?

[Heroku dynos](https://devcenter.heroku.com/articles/dynos) are managed containers that execute application process types. Teams select from published dyno tiers and scale each process type by changing its dyno type or quantity. The platform handles the host operating system, dyno lifecycle, routing, and container orchestration.

Fly Machines are lightweight virtual machines created from Docker images. Teams can choose shared or performance CPUs, memory, regions, services, process groups, and storage attachments. A Machine can be managed through Fly Launch or directly through the Machines API and CLI.

Fly Machines provide more granular infrastructure control than Heroku dynos. That flexibility also means teams must understand Machine configuration, regional capacity, process placement, storage, and application networking.

Heroku is the simpler abstraction for a team that wants to choose a runtime tier and process count. Fly.io is the stronger fit when the team wants control over individual machine resources and placement.

### When Multi-Region Placement Changes the Decision

Heroku applications run in available platform regions. More advanced isolation and regional options are associated with eligible Private Spaces and enterprise configurations. Following Heroku's February 2026 platform update, new Enterprise Account contracts are no longer offered, while existing Enterprise subscriptions and support contracts remain supported and may renew.

Fly.io lets teams place Machines across its documented regions. Traffic can enter through Fly.io's Anycast network and reach an appropriate application instance. Teams can add or remove Machines in specific regions and assign process groups to regional deployments.

Multi-region compute does not automatically make an entire application globally distributed. Teams must still plan:

* Database location and replication
* Stateful volumes and data consistency
* Regional capacity and failover
* Background jobs and singleton processes
* Inter-region network usage and cost
* Observability across regions

Fly.io is the better platform when regional placement is a core architectural requirement and the team is prepared to operate the application accordingly. Heroku is more appropriate when a simpler managed application-region model is sufficient.

## Migrating from Heroku to Fly.io: What Changes?

Migration from Heroku to Fly.io is not only a change in hosting provider. It changes how the application is packaged, configured, deployed, connected to data services, and monitored. Fly.io's [official Heroku migration guide](https://fly.io/docs/getting-started/migrate-from-heroku/) shows that a typical migration involves exporting Heroku configuration, launching the app on Fly.io, moving data services, importing secrets, configuring process groups, deploying, verifying, and cutting over DNS.

For teams planning the move, these are the practical changes:

* **Procfile to process groups:** Heroku process types such as web, worker, and release commands need to be represented in Fly.io configuration.
* **Config vars to secrets:** Heroku config vars need to be exported carefully and imported as Fly.io secrets.
* **Dynos to Machines:** Heroku dyno sizing becomes Fly.io Machine CPU, memory, process count, and region placement.
* **Heroku Postgres to Fly data services:** Database migration may require `pg_dump`, `psql`, connection changes, extensions review, and validation.
* **Add-ons to replacements:** Redis, scheduler jobs, logging, monitoring, email, object storage, and marketplace add-ons need direct replacements.
* **Pipelines to separate apps or automation:** Fly.io can support staging and production, but the team usually models this through separate apps, configuration, and GitHub Actions.

The migration can be straightforward for a simple app, but teams should not treat it as a one-command replacement. Heroku gives a more standardized application lifecycle. Fly.io gives more control, but the team must design more of the runtime and operating model.

## Data Services and the Real Monthly Cost

### Managed Add-ons or Composable Regional Services?

Heroku provides Heroku Postgres and Heroku Key-Value Store as managed data services. Its marketplace adds databases, caching, search, logging, monitoring, email, and other application capabilities. Data written to a dyno's local filesystem is ephemeral.

Fly.io provides [Managed Postgres](https://fly.io/docs/mpg/), local persistent Fly Volumes, and extensions operated by external providers. Fly Volumes are attached to Machines in a region and require teams to consider replication, snapshots, recovery, and regional placement.

Heroku provides a more established add-on and managed data-service workflow. Fly.io gives teams more direct storage and service choices but exposes more of the architecture.

Applications migrating from Heroku must map every dependency, including Postgres, Redis or Key-Value Store, object storage, scheduled work, background workers, log drains, and marketplace add-ons. Moving application code is only one part of the migration.

### Fixed Plan Maximums or Resource-Based Billing?

Heroku publishes monthly maximums for selected dyno and data-service plans. Its [official pricing page](https://www.heroku.com/pricing/) currently lists:

| Heroku resource | Published price |
| --- | ---: |
| Eco | $5/month for a shared pool of dyno hours |
| Basic | Up to $7/month per dyno |
| Standard-1X | Up to $25/month per dyno |
| Essential-0 Postgres | $5/month |

Fly.io charges according to provisioned resources. Its [official resource pricing](https://fly.io/docs/about/pricing/) lists Machine CPU and memory configurations, volumes, snapshots, networking, managed services, IP addresses, and support separately. Example compute prices vary by region and configuration. The [Fly.io pricing guide](https://kuberns.com/blogs/flyio-pricing/) explains how these individual resources combine in application estimates.

For the pricing region displayed in Fly.io's documentation, published examples include:

| Fly.io resource | Published example |
| --- | ---: |
| Shared CPU 1x, 256 MB | Approximately $2.02/month |
| Shared CPU 1x, 512 MB | Approximately $3.32/month |
| Shared CPU 1x, 1 GB | Approximately $5.92/month |
| Persistent volume | $0.15/GB per month |

These Machine examples are not complete application prices. A production estimate must include redundant Machines, storage, snapshots, data transfer, managed Postgres, extensions, IP addresses, support, and additional regions.

Heroku pricing is easier to estimate from selected plan maximums. Fly.io can align compute cost more closely with provisioned resources, but teams must model the entire architecture. See the [complete Heroku pricing guide](https://kuberns.com/blogs/heroku-pricing-explained/) for detailed Heroku examples.

## Delivery Environments, Scaling, and Production Ownership

### Integrated Pipelines or Environment-as-Configuration?

Heroku Pipelines group applications into review, staging, and production stages. Review Apps can create temporary applications for pull requests, and pipeline promotion moves an existing build between stages.

Fly.io supports continuous deployment and environment automation through GitHub Actions. Teams can represent development, staging, review, and production as separate Fly Apps and configuration files. Fly.io also documents monorepo, multi-environment, and review-app patterns.

Heroku provides the more integrated multi-environment user experience. Fly.io provides flexible building blocks, but teams own more of the workflow design, configuration, secrets, naming, and automation.

For teams focused on faster time to production, the deciding factor is not the number of deployment commands. It is how much environment configuration, data provisioning, validation, and operational automation the team must maintain after the first release.

### Managed Autoscaling or Application-Controlled Scaling?

Heroku lets teams change dyno types and quantities for each process type. Native autoscaling is available for eligible Performance, Private, and Shield dynos and scales web dynos according to supported application performance signals.

Fly.io supports two main automatic scaling approaches:

* Fly Proxy autostop and autostart starts or stops existing Machines according to request load.
* The [metrics-based autoscaler](https://fly.io/docs/reference/autoscaling/) can create, delete, start, or stop Machines according to configured metrics.

Fly.io's metrics autoscaler runs within the customer's organization and must be configured for the application. This provides flexibility for custom signals such as queue depth, but it also creates more operational responsibility.

Heroku offers a more managed autoscaling experience for eligible dynos. Fly.io offers more configurable Machine behavior for teams willing to manage the scaling setup.

### What Each Platform Leaves for the Team to Operate

Heroku aggregates application logs and provides platform metrics and alerts for eligible dynos. Teams can extend observability through marketplace add-ons. The platform's conventions reduce infrastructure-level decisions, but teams still need application monitoring, database monitoring, error tracking, and log retention.

Fly.io provides application and Machine logs, metrics, health checks, and a hosted Prometheus metrics system. Multi-region applications require teams to monitor Machine health, regional placement, routing, volumes, databases, and regional dependencies.

Heroku moved to a sustaining engineering model in February 2026, prioritizing stability, security, reliability, and support rather than new feature development. [Heroku's official announcement](https://www.heroku.com/blog/an-update-on-heroku/) states that existing applications and core platform functionality remain supported.

Fly.io continues to provide a more infrastructure-aware platform model. Teams evaluating either platform should validate service availability, support terms, compliance, data residency, backup requirements, and recovery procedures for their specific workload. The [Fly.io production-readiness guide](https://kuberns.com/blogs/is-fly-io-good-for-production/) provides a separate checklist for that evaluation.

## A Workload-Based Decision Framework

### Heroku fits teams optimizing for platform simplicity

Heroku is the better choice when:

* Your team wants established PaaS conventions with fewer infrastructure decisions.
* Buildpacks, dynos, add-ons, Pipelines, and Review Apps fit the workflow.
* Heroku Postgres or another managed Heroku service is central to the application.
* The application does not require direct Machine-level or multi-region placement control.
* Your existing Heroku deployment is stable and migration would add more complexity than value.

If Heroku's pricing, platform direction, or abstraction no longer fits, compare the [top Heroku alternatives in 2026](https://kuberns.com/blogs/heroku-alternatives/).

### Fly.io fits teams optimizing for infrastructure control

Fly.io is the better choice when:

* The team wants to select Machine CPU, memory, region, and storage.
* Multi-region application placement is an intentional architecture requirement.
* The application already uses containers or can be packaged as a Docker image.
* Persistent volumes and region-aware networking fit the workload.
* The team is comfortable managing `fly.toml`, Machine configuration, scaling, and regional data decisions.

If Fly.io exposes more infrastructure than your team wants to operate, compare the [leading Fly.io alternatives](https://kuberns.com/blogs/fly-io-alternatives/) before committing to the Machine model.

## Kuberns: A Simpler Way to Deploy Than Heroku or Fly.io

[Kuberns](https://kuberns.com/) is an Agentic AI platform for deployment, built for full-stack and complex backend applications. It is designed for teams that want to reduce deployment configuration without selecting and coordinating every dyno, Machine, region, or supporting workflow manually.

After you connect a GitHub repository, Kuberns analyzes the application and prepares its deployment configuration. CI/CD, monitoring, and alerts are included. Plans start at $7, a Trial Option is available, and bundle packs provide additional savings.

![Kuberns Agentic AI platform for deployment](https://kuberns-blogs-media.s3.ap-south-1.amazonaws.com/kuberns-home-page-new.png)

### What changes when the repository becomes the starting point

Kuberns is most relevant when the primary requirement is deploying and operating a full-stack or complex backend application through a more unified workflow.

| Consideration | Heroku | Fly.io | Kuberns |
| --- | --- | --- | --- |
| Platform model | Managed PaaS | Machine-based application platform | Agentic AI platform for deployment |
| Primary workflow | Configure an app, dynos, and services | Configure an app, Machines, regions, and services | Connect a GitHub repository for application analysis |
| Compute abstraction | Dynos | Fly Machines | Deployment configuration prepared for the analyzed application |
| Regional control | Platform and eligible Private Space options | Direct Machine placement | Configuration depends on the analyzed application |
| CI/CD | Pipelines and related platform features | GitHub Actions and custom workflows | Included |
| Monitoring and alerts | Platform capabilities and add-ons | Logs, metrics, and health checks | Included |
| Pricing model | Selected plans | Resource usage | Plans start at $7 |
| Trial or free option | No permanent free runtime tier | Trial and billing terms apply | Trial Option available |
| Savings option | Right-size dynos and add-ons | Control provisioned resources | Bundle packs provide additional savings |

Kuberns does not need to reproduce every Heroku add-on or Fly.io infrastructure control to be useful. Its strongest fit is a team that values reduced deployment configuration over choosing every infrastructure component directly.

## Conclusion: Reduce Deployment Decisions with Kuberns

Heroku is the stronger choice for teams that want mature PaaS conventions, managed dynos, data services, Pipelines, and an add-on ecosystem. Fly.io is the stronger choice when teams need Machine-level resource control, persistent volumes, and deliberate multi-region placement.

For full-stack and complex backend teams that want to reduce deployment configuration, [Kuberns](https://kuberns.com/pricing) provides a more unified path. Kuberns is an Agentic AI platform for deployment that prepares deployment configuration after a GitHub repository is connected. CI/CD, monitoring, and alerts are included, plans start at $7, a Trial Option is available, and bundle packs provide additional savings.

[Deploy your application with Kuberns](https://dashboard.kuberns.com)

<a href="https://dashboard.kuberns.com" target="_blank" rel="noopener noreferrer">
  <img src="https://kuberns-blogs-media.s3.ap-south-1.amazonaws.com/CTA_banner.png" alt="Deploy your application with Kuberns" />
</a>

## Frequently Asked Questions

### Why would a team move from Heroku to Fly.io?

A team may move from Heroku to Fly.io when it needs direct control over Machine CPU and memory, regional placement, persistent volumes, container images, or resource-based billing. The tradeoff is greater responsibility for `fly.toml` configuration, data placement, scaling behavior, and regional operations.

### How are Heroku web and worker dynos mapped to Fly.io?

Heroku web and worker process types can be represented as Fly.io process groups, with each group running on one or more Machines. The team must define the commands, services, Machine resources, scaling counts, regions, secrets, and any attached volumes in the Fly.io application configuration.

### Can Fly Launch deploy an application without Docker expertise?

Fly Launch can detect supported projects, generate a Dockerfile or build configuration, create `fly.toml`, select defaults, and perform the first deployment. Docker knowledge is not required for every first deploy, but it becomes useful when teams customize images, debug builds, or operate applications with specialized runtime requirements.

### What happens to application data when Fly Machines run in multiple regions?

Adding Machines in multiple regions does not automatically replicate application data. Fly Volumes are local to a region, and databases require an intentional replication and failover design. Teams must decide where writes occur, how replicas stay consistent, and what happens when a region or database node is unavailable.

### Is Fly.io's lowest Machine price comparable to a Heroku dyno?

Not by itself. A Fly Machine price covers a specific CPU and memory configuration, while a complete Fly.io application can also require redundant Machines, volumes, snapshots, data transfer, IP addresses, Managed Postgres, extensions, and support. Compare the full architecture with Heroku's dynos, databases, and add-ons.

### How does Fly.io scale background workers?

Background workers can run as a separate Fly.io process group with their own Machine size and count. For dynamic scaling, a team can configure the metrics-based autoscaler around signals such as queue depth. Unlike request-based autostop and autostart, worker scaling requires an appropriate metric and operating configuration.

### Which platform is faster to production for staging and review environments?

Heroku usually provides the faster standardized path through Pipelines, staging apps, Review Apps, and build promotion. Fly.io can support staging, review, and production environments through separate apps, configuration, and GitHub Actions, but the team owns more of the environment design and automation.

### What should teams monitor after migrating from Heroku to Fly.io?

Teams should monitor Machine health, application latency, regional capacity, restarts, memory and CPU use, volume capacity, database replication, data transfer, health checks, deployment failures, and total resource cost. A multi-region design also needs region-specific alerts and a tested failover procedure.

### How can Kuberns reduce configuration for teams comparing Heroku and Fly.io?

[Kuberns](https://kuberns.com/) is an Agentic AI platform for deployment, designed for teams that want fewer platform and infrastructure decisions. [Connect a GitHub repository](https://dashboard.kuberns.com), and Kuberns analyzes the application and prepares its deployment configuration. CI/CD, monitoring, and alerts are included. Plans start at $7, a Trial Option is available, and bundle packs provide additional savings.

### Is Fly.io a good Heroku alternative?

Fly.io is a good Heroku alternative for teams that want more infrastructure control, regional placement, Docker-based deployment, and resource-based billing. It is less suitable for teams that want Heroku's mature PaaS conventions without managing more configuration.

### Should I migrate from Heroku to Fly.io?

You should consider migrating from Heroku to Fly.io if your team needs more control over regions, Machines, containers, volumes, and scaling behavior. If your goal is to reduce operational work instead of gaining more infrastructure control, Kuberns may be the better path.

### Does Fly.io handle more infrastructure than Heroku?

Fly.io exposes more infrastructure control than Heroku through Machines, regions, volumes, services, private networking, and `fly.toml` configuration. Heroku hides more of those decisions behind dynos, buildpacks, add-ons, Pipelines, and platform conventions.

### What is the difference between Heroku dynos and Fly.io Machines?

Heroku dynos are managed application containers selected by dyno tier and process type. Fly.io Machines are lightweight virtual machines created from Docker images, with more direct control over CPU, memory, regions, services, and attached volumes.

---
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