Skip to main content
OpenSourceAlternatives

Modal alternatives

3 projects to consider for your Modal workflows.

3 open source

Explore dstack, E2B and OpenComputer. Each takes a different approach to your Modal workflows. Read its capabilities and limitations to find the right fit.

Catalogue facts checked October 3, 2026

New

dstack

Open-source orchestration for AI workloads across any GPU cloud

Open sourceMPL-2.0Self-hostedManaged hosting

AI teams that juggle GPU capacity across clouds gain the most from dstack. Its fleet, service, and gateway model maps multi-cloud GPUs into one deployment target with autoscaling and cost matching. Teams without infrastructure experience should look elsewhere, because every backend needs its own credentials and YAML configuration. If you want GPUs without touching infrastructure at all, choose a fully managed serverless platform instead.[1][2][3]

Good fit. Multi-cloud training teams: one set of definitions spans reserved and marketplace GPUs, which matters when a single provider cannot cover every workload.[1][2]

Keep in mind. Teams without infrastructure help: per-backend credentials, fleet YAML, and gateway setup assume somebody on the team already thinks in infrastructure terms.[1]

Repository review signals: unknown status, very stale · Observed October 3, 2026.

Read project review Official website
Sources and editorial review

Source research. Reviewed on October 9, 2026. Reviewed by Sanjib Sah. Written by Sanjib Sah. AI assisted; reviewed by a human editor.

We have not run or tested this product.

One inspected vendor-published deployment account: Toffee's infrastructure architect on replacing Terraform scripts with dstack services and gateways across GPU neoclouds (2026); no Reddit or G2 user report located. One vendor-published customer story (2026), not a user sample; no sentiment or prevalence inference possible. No Reddit threads surfaced and no G2 dstack page was located, so user sentiment is uninspected rather than negative.

  1. dstack documentation

    official · Observed October 9, 2026

    Docs intro: workload kinds, server/CLI/Python/REST/dashboard/gateways/volumes/metrics, accelerators, cost optimization, install, agent skills; What's new 0.21.0 and 0.20.x notes.

  2. dstack backends

    official · Observed October 9, 2026

    Backend coverage: VM-based GPU clouds, Kubernetes clusters, SSH fleets for on-prem and marketplaces; SSH fleets need no backend configuration.

  3. dstack Sky

    official · Observed October 3, 2026

    Managed GPU capacity marketplace usable without owning a cloud account (intake ledger, Oct 3; Nebius addition confirmed via search).

  4. How Toffee streamlines inference and cut GPU costs with dstack

    community · Observed October 9, 2026

    Vendor-published customer story quoting Toffee's infrastructure architect on replacing Terraform scripts with services/gateways; links onward to the original Toffee engineering post. Analyzed, not quoted (not Reddit/G2); vendor-hosted, so not an independent sample.

E2B

Isolated Linux sandboxes for running AI agent code

Open sourceApache-2.0Self-hostedManaged hosting

E2B fits agent teams that run untrusted model-generated code and want per-session Firecracker isolation without building sandbox infrastructure. Its SDKs, snapshot and fork lifecycle, egress filtering, and storage mounts cover the full agent workspace, billed per second for CPU and RAM with a free Hobby tier. Sandboxes are CPU-only, SSO is still planned, and Hobby caps sessions at one hour. Code teams should start on Hobby; teams needing GPUs or SSO should look elsewhere.[1][2][3][4][5]

Good fit. We would evaluate E2B for agent teams that execute untrusted model-generated code, since per-session microVM isolation is the stage riskiest to build in-house.[1][2]

Keep in mind. Sandboxes are CPU-only with no GPU option, so GPU-bound training or inference workloads need a different platform.[1]

Repository review signals: unknown status, very stale · Observed October 3, 2026.

Read project review Official website
Sources and editorial review

Source research. Reviewed on October 9, 2026. Written by Sanjib Sah. AI assisted; reviewed by a human editor.

We have not run or tested this product.

One independent practitioner guide (Lizard-affiliated vendor comparison by CEO Yurii Oliinyk) with E2B pricing and lifecycle figures checked against E2B pricing docs in September 2026. No Reddit or G2 firsthand user reports were located; E2B-name searches collide with unrelated Easy2Boot content. Vendor-affiliated single source: the guide's figures are attributed to E2B vendor docs, not independent measurement. No firsthand user quotations were selected, so broader sentiment is unknown and no prevalence claim is made.

  1. E2B - secure sandboxes for AI agents

    official · Observed October 9, 2026

    Official homepage: isolated Linux sandboxes for AI agent code; per-session microVM isolation; managed cloud posture with SOC 2 Type II compliance.

  2. E2B pricing

    official · Observed October 9, 2026

    Official pricing: Hobby free tier with one-time credit; Pro plan with monthly base plus per-second CPU and RAM usage billing.

  3. E2B documentation

    official · Observed October 9, 2026

    Official docs: Python and JavaScript SDKs plus CLI; shell, filesystem, network, and optional desktop; pause, snapshot, resume, and fork; HTTPS port exposure; per-sandbox outbound filtering; egress-proxy secret injection; Git, volume, and S3/GCS/R2 mounts; live metrics and signed lifecycle webhooks; CPU-only sandboxes; SSO planned.

  4. E2B repository README

    official · Observed October 9, 2026

    Repository README at about 14,140 stars: Apache-2.0 licensed runtime in Python and TypeScript; self-hosted runtime on a single Linux x86-64 machine with KVM; managed E2B Cloud across US, EU, and APAC regions.

  5. E2B sandboxes: pricing, lifecycle and alternatives

    community · Observed October 9, 2026

    Independent practitioner guide by Lizard CEO Yurii Oliinyk (Lizard sells sandbox infrastructure; vendor affiliation disclosed): E2B lifecycle and per-second billing walkthrough with figures checked against E2B pricing docs September 2026; Hobby caps of one-hour sessions and 20 concurrent sandboxes, Pro 24-hour sessions; CPU at $0.000014/s and memory at $0.0000045/GB/s; illustrative $20/month small-project math; Modal as the sandbox comparison.

OpenComputer

Always-on cloud computers for long-running AI agents

Open sourceApache-2.0Managed hosting

Teams running agents that keep files, packages, and progress between sessions benefit most from OpenComputer. Its durable sessions with hibernate, resume, and per-second billing directly address the reinstall-and-restart loop of disposable sandboxes. Teams that need short-lived code execution, GPUs, or documented regional controls should choose something else, since those are not evidenced here. If persistent agent computers match the workload, start with OpenComputer; if throwaway execution is enough, a lighter sandbox such as E2B fits better.[1][2]

Good fit. A workbench-style agent edits code, runs checks, and opens draft pull requests from a machine that keeps its checkout and dependencies between sessions.[1][2]

Keep in mind. Short-lived stateless runs do not use persistence, hibernation, or checkpoints, so a lighter disposable sandbox is simpler and cheaper.[1]

Repository review signals: unknown status, stale · Observed October 12, 2026.

Read project review Official website
Sources and editorial review

Source research. Reviewed on October 12, 2026. Written by Sanjib Sah. AI assisted; reviewed by a human editor.

We have not run or tested this product.

  1. OpenComputer: the platform for long-running agents

    official · Observed October 12, 2026

    Official homepage evidencing positioning, session machine, durable sessions, agent functions, MCP, secrets, schedules, usage billing, and support tier.

  2. OpenComputer docs: Serverless Agents overview

    official · Observed October 12, 2026

    Managed runtime, TypeScript agents, one-command deploy, immutable versions, no local server.

  3. OpenComputer documentation index

    official · Observed October 12, 2026

    Management API, SDK, project database, schedules, secrets, service connections, BYOK.

Compare Modal alternatives

Known catalogue facts at a glance. Columns with no reviewed values are omitted. “Not reviewed” means unknown, not that a capability is absent.

Known facts for Modal alternatives
ProjectLicense policyHostingDeploymentOfflineEnterprise supportBest fit
dstackMPL-2.0 · OSI-approvedSelf-hosted + ManagedmoderateNoNot reviewedAI training and fine-tuning on rented GPUs; Self-hosted LLM inference with autoscaling
E2BApache-2.0 · OSI-approvedSelf-hosted + ManagedmoderateNot reviewedYesCoding agents; Code interpretation and data analysis
OpenComputerApache-2.0 · OSI-approvedManagedNot reviewedNot reviewedYesTeams running long-lived coding agents that keep files and packages between sessions; Agent builders who want deploy-as-a-function with managed sessions and schedules

Before you switch

  • Check that the project covers the workflows and data you need to move.
  • Confirm that the license terms fit how you plan to run, modify, and distribute it.
  • Choose between self-hosting responsibility and a reviewed managed option.
  • Open the project page for repository freshness, review dates, and any unresolved requirements.
About Modal

Modal is a serverless cloud for running Python AI code on GPUs without servers. Functions scale from zero to thousands of GPUs, billed only for seconds used.

The platform covers low-latency model inference behind OpenAI-compatible endpoints, batch jobs that fan out across parallel containers, fine-tuning and multi-node training runs, and isolated sandboxes for coding agents and reinforcement-learning rollouts. Functions can be exposed as autoscaling HTTPS endpoints, scheduled on a cron, and wired to volumes, queues, and secrets for stateful workloads.

Pricing is usage-based down to the second across GPU, CPU, memory, volume, and egress meters. The Starter plan costs nothing plus compute with $30 in monthly credits, Team is $250 per month plus compute with $100 in credits, and Enterprise pricing is custom with SSO, audit logs, and HIPAA readiness. Capacity spans regions over a multi-cloud pool, with SOC 2 Type II compliance documented for production teams.

Modal Labs, Inc. · Usage-based per-second billing; Starter $0 plus compute with $30/month free credits; Team $250/month plus compute with $100/month free credits; Enterprise custom pricing. Observed 2026-10-03. · Checked October 3, 2026

Official website
How this list is ordered

Recommended · Organic catalogue order.

A deterministic editorial score using relationship confidence, category fit, logarithmic popularity, as-of maintenance and freshness, with bounded anomaly quarantine and known-data penalties. Payment is fixed at zero.

Payment does not affect organic ranking. Order is a starting point, not a universal recommendation.

OpenAlternative organic alternatives v4 · version 4

Alternatives to Modal: Open Source and More | OpenSourceAlternatives