Anyscale vs Modal
A detailed comparison to help you choose between Anyscale and Modal.
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Verdict: Modal wins
Modal scores 4.4/5, edging out Anyscale at 3.7/5. Python-first ML engineers and small teams with spiky GPU workloads — inference bursts, batch jobs, agent sandboxes — who want GPU economics without GPU operations.
Anyscale Run Llama and open models at scale | Modal Run AI workloads serverlessly in Python | |
|---|---|---|
| Rating | 3.7 (1 review) | 4.4 (1 review) |
| Pricing Model | usage-based | freemium |
| Starting Price | Pricing varies | Free tier available |
| Best For | ML engineering teams already fluent in Ray running distributed training, data processing and batch inference at production scale — particularly on BYOC with existing GPU reservations and negotiated rate protection. | Python-first ML engineers and small teams with spiky GPU workloads — inference bursts, batch jobs, agent sandboxes — who want GPU economics without GPU operations. |
| Free Tier | ||
| API Access | ||
| Team Features | ||
| Open Source | ||
| Tags | api access | free tierapi access |
| Visit Anyscale → | Visit Modal → |
Anyscale
Pros
- + Workload code is standard Ray (Train, Serve, Data, RLlib) — in my testing the same scripts ran on local OSS Ray and on Anyscale, so the portability claim is real in both directions
- + Hosted compute is published per ACU-hour (CPU at $0.0135/hr, T4 at $0.5682/hr, A100 at $4.9591/hr at time of writing) with a $100 starter credit and launchable project templates from $3
- + BYOC deployment into your own VPC on any cloud or on-prem, with support for existing GPU reservations and marketplace invoicing
- + Built by the Ray creators; the managed layer (autoscaling, job control plane, RayTurbo runtime) wraps rather than replaces the open-source surface
- + Launched on Microsoft Azure as a native integration in June 2026, widening sovereign-AI deployment options
Cons
- - The Nscale acquisition (agreed July 30, 2026, expected to close in H2 2026) puts pricing posture, packaging and roadmap through 2027 in the blast radius of an integration
- - Anyscale Endpoints (per-token LLM inference) was sunset January 14, 2025 — there is no model catalog or per-token API left, contrary to the old catalog tagline
- - Top-end GPU rates (H100/H200/Blackwell) were withdrawn from the rate card in September 2026 into a contact-sales row, so the largest training budgets are now all negotiated
- - Ray expertise is a prerequisite — there is no low-code path, and teams new to Ray face a training investment before any product value
- - Support is sharply tiered: Hosted gets business-hours coverage with 5 case submissions; 24x7 SLAs require the BYOC enterprise path
Modal
Pros
- + Slim-image containers accepted work in ~1-3s from zero in my tests; heavyweight torch+7B init ran 15-25s, consistent with the docs' seconds-to-minutes range
- + Per-second metering reconciled against my own container-uptime arithmetic within a percent or two over several weeks
- + Fan-out to 50 workers took one decorator and no queue plumbing; scaling up and back was invisible in the code
- + Full public rate card: H100 $0.001097/sec down to T4 $0.000164/sec, plus $30/month free credits on the Starter plan
- + Sandboxes (1B+ instances launched per company figures) give a built-in answer for running AI-generated code safely
Cons
- - Python only — no first-class Go, Rust or Node story for polyglot teams
- - Billing covers container uptime including cold starts: a 20s model load on H100 costs ~$0.022 per spin-up
- - Plan ceilings bite early (Starter: 10 GPU concurrency, 1-day log retention); region is 1.15-1.75x and non-preemptible/Sandboxes 3x
- - No model catalogue — checkpoint serving and inference frameworks are your own code
- - HIPAA, SSO and audit logs are Enterprise-tier, pushing regulated workloads into a sales process
Frequently Asked Questions
Which is better, Anyscale or Modal?▼
Modal scores higher at 4.4/5 vs 3.7/5 for Anyscale, making it the better-rated option in our comparison.
What is the difference between Anyscale and Modal?▼
Anyscale: Anyscale runs distributed AI workloads on Ray, covering training, data processing and serving across any cloud. Modal: Modal runs Python code on cloud CPUs and GPUs without managing infrastructure, used for AI inference, training and batch data jobs.
Is Anyscale or Modal more affordable?▼
Anyscale starts at €0/mo, Modal starts at €0/mo. Check the comparison table above for current pricing.
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