Modal vs Replicate (2026): Pricing, Features & Verdict

Why choose Modal

• Has a free plan (Replicate does not)
• Higher Scalability score (9/10 vs 7/10)
• Higher Security score (7/10 vs 5/10)
• Better fit for enterprise (7/10 vs 5/10)

Why choose Replicate

• Higher Integration score (7/10 vs 6/10)

Modal

Pros

• GPUs from the T4 up to the B300 are available without quota requests or reservations, and workloads can burst to thousands of containers.
• Per-second billing and scale-to-zero mean idle bursty inference workloads cost nothing.
• Python developers define infrastructure in code and deploy with a single CLI command, with no Kubernetes or Dockerfiles needed.
• The free Starter plan includes $30 a month of compute, and startup and academic credit programs are available.
• Sandboxes support large numbers of concurrent isolated environments for AI agents and untrusted code.

Cons

• Always-on or steady workloads can cost more than reserved GPU rentals from providers such as RunPod or Lambda.
• Containers stay billable for 60 seconds after the last request by default, and keep-warm settings are a common source of bill surprises.
• Region pinning raises prices by 1.15 to 1.75 times, and non-preemptible execution by 3 times.
• SSO, audit logs, and HIPAA support are limited to the custom-priced Enterprise plan.
• Credits from AWS, GCP, or Azure cannot be applied to Modal usage.

Replicate

Pros

• Thousands of ready-to-run community and official models can be called through one API, and Cloudflare cites more than 50,000 production-ready models.
• Public models charge only for active processing time, so prototypes cost nothing while idle.
• The open-source Cog tool packages custom models for upload, and the docs cover webhooks, streaming output and fine-tuning.
• Deployments give private endpoints that autoscale from zero, with minimum and maximum instance caps to keep warm capacity or limit spend.
• Per-second hardware pricing is published openly for every GPU type, from T4 to 8x H100.

Cons

• Cold starts on shared public and community models are the most cited complaint, with reviews reporting delays of tens of seconds or more for custom models.
• Private models and deployments bill for setup and idle time, so latency-sensitive production use costs much more than the list per-second rates suggest.
• Dedicated GPU rates, such as 5.49 USD per hour for an H100, are higher than many raw GPU clouds, according to competitor comparisons.
• No SOC 2 report or public trust center was found, and enterprise SLA terms are not published.
• Bills are hard to forecast because costs vary by model, hardware and billing method, and some canceled runs are still charged.