Together AI vs Modal
A detailed comparison to help you choose between Together AI and Modal.
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Verdict: Modal wins
Modal scores 4.4/5, edging out Together AI at 4.1/5. Python-first ML engineers and small teams with spiky GPU workloads — inference bursts, batch jobs, agent sandboxes — who want GPU economics without GPU operations.
Together AI Run open-source AI models in the cloud | Modal Run AI workloads serverlessly in Python | |
|---|---|---|
| Rating | 4.1 (1 review) | 4.4 (1 review) |
| Pricing Model | usage-based | freemium |
| Starting Price | Pricing varies | Free tier available |
| Best For | Developers and researchers who want to run, compare and fine-tune open-weight models across a wide catalogue with OpenAI-compatible calls and no minimum spend. | 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 accessfree tieropen source | free tierapi access |
| Visit Together AI → | Visit Modal → |
Together AI
Pros
- + Switched one pipeline across four serverless models in an afternoon — only the base URL and model string changed
- + Model table publishes context length, cached pricing and quantisation format (NVFP4/FP8/MXFP4) per model
- + Flash-tier pricing ($0.15-$0.30 input) made a 10M-token/day extraction job far cheaper than frontier equivalents
- + Breadth: chat, image, vision, video, audio, embedding, rerank and moderation under one API
- + $800M raise at $8.3B valuation (July 2026) is a strong continuity signal for an infrastructure vendor
Cons
- - No proprietary frontier models; my messy scanned-forms task needed a frontier fallback
- - No published per-model latency figures — you must measure for yourself
- - Fine-tuning pricing (tokens x epochs, per-model rates) takes real digging to budget
- - Experimental and free-tier models sit in the same list as production ones with no quality signal
- - Dedicated GPU cluster options exist but are not as configurable as pure compute providers
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, Together AI or Modal?▼
Modal scores higher at 4.4/5 vs 4.1/5 for Together AI, making it the better-rated option in our comparison.
What is the difference between Together AI and Modal?▼
Together AI: Together AI is a cloud platform for running, fine-tuning and serving open-source AI models, including dedicated GPU clusters. Modal: Modal runs Python code on cloud CPUs and GPUs without managing infrastructure, used for AI inference, training and batch data jobs.
Is Together AI or Modal more affordable?▼
Together AI starts at €0/mo, Modal starts at €0/mo. Check the comparison table above for current pricing.
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