Cohere vs Modal Pricing (2026)
How do these two stack up on price? Here's what each one costs, what you get, and where the value sits.
North
- Intuitive interface
- Purpose-built generative models
- Intelligent search
- AI agents for routine tasks and complex workflows
Compass
- Pre-built data connectors
- Intelligent search
- Document parsing
- Managed index
Model Vault
- Fully managed model deployment
- No shared resources or multi-tenancy overhead
- Seamless integration with Cohere North
- Simple startup and self-serve model access
- Fixed or Flex pricing plans available
Starter
- $30 / month free credits
- 3 workspace seats included
- 100 containers + 10 GPU concurrency
- Scheduled and Web Functions (limited)
- Real-time metrics and logs
- Region selection
Team
- $100 / month free credits
- Unlimited seats
- 5000 containers + 50 GPU concurrency
- Unlimited Scheduled Functions
- Custom domains
- Static IP proxy
- Deployment rollbacks
- Environment-level budgets
Enterprise
- Volume-based discounts
- Unlimited seats
- Higher GPU concurrency
- Embedded ML engineering services
- Environment-level budgets
- Support via private Slack
- Audit logs, Okta SSO, and HIPAA
Cohere vs Modal FAQ
- Which one is cheaper?
- One or both use custom pricing, so it depends on your specific needs.
- Can I use either one for free?
- Modal has a free plan. Cohere doesn't — though they do offer a free trial.
- How do they charge?
- Different approach here. Cohere uses custom pricing, while Modal goes with usage-based. That changes the math depending on your team size and usage.
- Which one is a better deal?
- Depends on what you need. Cohere: Cohere is squarely targeting enterprise and developer teams that can't or won't send data to OpenAI or Anthropic — data residency, security, and deployment flexibility are the pitch. They're not the cheapest option, but they're positioning as the serious infrastructure play for regulated industries and large orgs that need control. Modal: Modal isn't competing on sticker price — it's positioned as infrastructure for serious ML/GPU workloads, so the real comparison is against running your own Kubernetes/GPU cluster or paying cloud providers directly, not against cheap PaaS tools. The surcharge structure signals they're chasing teams who value developer experience and fast iteration over teams hunting for the rock-bottom compute rate.
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