Enterprise generative AI spend
Menlo Ventures' estimate of enterprise spending in 2025, across models, infrastructure and applications.
AI COST CONTROL / COMPUT
The cost & security
control layer
for your AI.
Reduce avoidable AI spend, govern access and control how your data reaches model providers. One gateway between your applications and your AI providers.
Create a COMPUT account, verify your email, then connect your own Groq key. Already have an account? Sign in.
THE SPENDING CONTEXT / 2025 RESEARCH
AI investment is growing.
Cost discipline needs to follow.
As AI usage spreads across teams and providers, it gets harder to know what each request costs or where the data goes. Start with visibility, then set boundaries.
Year-over-year growth
Up from a restated $11.5B in 2024, using the report's comparable spending definition.
Spent on AI applications
The report's estimate for the application layer in 2025, rather than model API charges alone.
Source: Menlo Ventures, 2025 State of Generative AI in the Enterprise. Market estimates, not average spend per company or COMPUT customer results. Rising total spend does not imply rising per-token prices. Savings vary by workload and configuration.
BETWEEN YOUR APP AND YOUR MODELS
One place to route.
One place to stay in control.
Your application sends a request to COMPUT. The gateway applies configured controls, routes it to a provider and records usage for review. The animated path below is an illustration, not live traffic.
Illustrative architecture, not live traffic. Provider access and controls depend on your configuration.
SPEND LESS ON AVOIDABLE WORK
A smaller bill starts
with a clearer picture.
See where tokens and money go. Then decide which requests, models and limits are worth changing.
Find the expensive requests
Look at model usage, recorded tokens and estimated spend before deciding where to optimize.
Choose models deliberately
Test models in the playground. Compare their responses and latency against what your workload actually needs.
Keep spending accountable
Manage client access and configure spending controls in one place, rather than leaving usage scattered across applications.
A FAMILIAR API. A CENTRAL CONTROL POINT.
Connect your app.
Keep your workflow.
Use a COMPUT client key and an OpenAI-compatible base URL, then choose a configured model. Validate the features your application needs against that model and provider.
Use server-side environment variables. Never embed API keys in browser code.
Read the quick start →Server-side JavaScript
import OpenAI from "openai";
const client = new OpenAI({
baseURL: "https://api.comput.cloud/v1",
apiKey: process.env.COMPUT_API_KEY,
});
const result = await client.chat.completions.create({
model: "openai/gpt-oss-20b",
messages: [{ role: "user", content: "Hello COMPUT" }],
});Example requires a configured Groq connection and a valid COMPUT client key. Provider charges and limits may apply.
GOVERNANCE WITHOUT THE GUESSWORK
Your providers.
Your rules.
Your visibility.
Control access
Create and manage client API keys. Keep provider credentials separate from application credentials.
Set boundaries
Review security, budgets and routing configuration from the gateway console.
Understand what happened
Use request history and available audit records to investigate errors, usage and configuration activity.
SECURITY / INTELLECTUAL PROPERTY / DATA EXPOSURE
Control more than cost.
Know what leaves your workspace.
A prompt can contain customer records, confidential documents, proprietary code or credentials. Before sending it to a commercial AI service, decide what may be shared, which providers are approved and what must stay private.
Keep secrets out of prompts
Minimize personal and confidential information before a request leaves your application. Remove credentials and use approved redaction or anonymization where appropriate. Treat logs and cached responses as potentially sensitive too.
Protect the work that is yours
Source code, product plans and internal research deserve explicit sharing rules. Confirm permission to send the material, review provider terms and choose approved destinations for proprietary workloads.
Review where data goes
Check the specific service's retention, training-use and processing-location settings. Review fallback routes as well as the primary provider; a routing decision is also a data-sharing decision.
A gateway is a control point, not a privacy guarantee.
COMPUT brings client-key management, routing configuration, security settings and available audit records into one console. Requests routed to external providers still leave your infrastructure. Do not assume automatic sensitive-data redaction, zero retention, data residency or compliance guarantees: verify the protections configured for your deployment and each provider before sending sensitive workloads.
Review governance controls →FOLLOW THE DATA
Understand every boundary.
Know what is recorded
Usage metadata and request content are different. Review logging settings and storage permissions; do not assume prompt and response bodies are excluded everywhere.
Retention is a provider decision too
Check retention and training-use terms for the exact provider service and account. A gateway alone does not establish zero-data-retention guarantees.
Review the whole route
A fallback or retry may reach another configured provider. Include every potential destination when approving sensitive workloads.
Make caching an explicit choice
Review gateway and provider caching separately. Confirm eligibility, retention and access boundaries before using caches for confidential content.
MEASUREMENT BEFORE MARKETING
Savings should be measured.
Not assumed.
A lower token count is not the whole story. Compare equivalent workloads, check response quality and account for retries. COMPUT displays recorded usage and rate-based cost estimates; those estimates are not provider invoices or verified savings.
Read about cost tracking →ONE WORKSPACE. A CLEARER VIEW.
Put your AI spend
under control.
Get API key →Create your account, verify your email, and connect your provider key to get started.