Enterprise billing can follow a separate commercial structure depending on deployment model, compliance requirements, support expectations, or procurement boundaries.
What this page covers
Use this page to understand:- how Revolte pricing is structured
- what credits mean in practice
- what infrastructure billing includes
- how to estimate likely monthly cost
- what enterprise controls change commercially and operationally
Pricing Structure
Revolte pricing is best understood through three independent usage surfaces.AI Execution
Reasoning and code generation. This includes model-driven work such as code generation, PR review, planning assistance, and deeper reasoning workflows.Delivery Operations
Builds, runs, logs, and previews. This includes CI/CD runs, log ingestion and storage, deployment activity, and preview environment usage patterns.Runtime Infrastructure
Managed cloud resources. This includes web services, databases, storage, cache, secrets, metrics, network egress, and other underlying cloud primitives.Understanding Credits
Credits exist to make AI usage easier to package at the plan level. Free and Pro plans communicate bundled AI capacity through included Revolte credits, while deeper technical AI pricing can still be reasoned about at the model level using token-based reference tables. In practice, this means the platform gives teams a simpler top-level allowance while still allowing deeper technical accounting underneath. On the public pricing page, Free includes 5 Revolte credits and Pro includes 20 Revolte credits. The plan-facing mental model also notes that 1 credit is approximately 1K prompt tokens. For overages, the public PAYG rate is listed as $0.20 per 1K AI tokens.What a credit helps simplify
Credits abstract raw model usage into a platform-friendly unit so users do not need to think in token accounting first. This is useful for onboarding, plan comparison, and governance discussions.- small prompt or lightweight task = lower usage
- PR review with reasoning = medium usage
- Jira to implementation workflow = higher usage
- large refactor or deep debugging = highest usage
What actually drives AI consumption
Usage grows when prompts get longer, more repository context is loaded, more code is generated, more review turns are requested, or higher-capability model families are selected. Repeated context can become cheaper when caching is used effectively. Good usage is focused execution with clear requirements, tighter repository scope, and the right model for the job, rather than always defaulting to the most expensive reasoning tier.| Consumption area | What usually increases it | How to keep it efficient |
|---|---|---|
| Prompt input | Large task descriptions, excessive pasted context, broad repository scans | Use focused requirements, smaller task scopes, and precise repository context |
| Model output | Large code generation, long explanations, repeated iterations | Constrain the desired output shape and ask for narrower, reviewable increments |
| Premium reasoning | Using high-capability models for every task | Reserve premium models for architecture, deep debugging, and critical review paths |
| Repeated context | Sending the same documents or system context over and over | Use caching where available so repeated context is read more cheaply than rewritten |
Enterprise Controls
Enterprise pricing is not only about discounting. It exists for teams that need deployment flexibility, compliance posture, support guarantees, usage governance, and modular procurement options.Commercial flexibility
AI-only, platform-only, intelligence-only, or any combination of the three modules can be packaged. All three bundled together are positioned for maximum discount.Control and deployment model
Enterprise capabilities include unlimited services and usage with custom caps, BYOC, private cloud, and on-prem deployment options.Assurance and support
Custom SLA and uptime guarantees, dedicated customer success support, white-glove onboarding, migration support, SOC 2 and GDPR support, and a listed 4-hour priority support SLA.| Enterprise capability | Why it matters |
|---|---|
| Unlimited services and custom caps | Lets larger organizations grow service count while still governing spend and consumption thresholds |
| BYOC + private cloud / on-prem | Supports organizations with infrastructure, data residency, or security requirements beyond standard SaaS hosting |
| SSO and RBAC | Improves access governance and aligns the platform with enterprise identity management practices |
| SOC 2, GDPR, custom compliance support | Helps regulated buyers evaluate trust, governance, and procurement readiness |
| Dedicated success manager and white-glove onboarding | Reduces rollout risk for platform adoption across multiple teams and services |
| BYO LLM and custom fine-tuning support | Allows larger customers to control model strategy rather than being locked into a single default approach |
FAQ
Why is Revolte not priced per seat?
Revolte pricing is aligned to managed services and actual execution rather than the number of developers who need access. This avoids automatic cost multiplication when engineering teams grow.Is AI usage the same as infrastructure billing?
No. AI usage, delivery operations, and runtime infrastructure are separate cost surfaces and should be evaluated separately.What usually increases cost fastest?
The main growth levers are repeated AI-heavy workflows, high CI/CD volume, rapid log growth, preview sprawl, and runtime infrastructure expansion across services.When should a team move to Enterprise?
Enterprise becomes relevant when service count, compliance requirements, support expectations, deployment model requirements, or governance controls move beyond standard Pro boundaries.Next steps
- Review plan and pricing assumptions against expected service count and workflow usage.
- Estimate likely AI, CI/CD, and log overages before production rollout.
- Add runtime infrastructure only for the services that will actually be deployed.