Unified model routing and spend control workspace

For

Engineering teams and platform owners routing production AI traffic across several model providers

Solves

Teams integrate each model provider separately, cannot see or cap combined spend, and have no tested fallback when a provider degrades.

Delivers

Reviewed gateway configuration with measured routing and spend reports

Built in

about 6 weeks of creation time, MVP in 7 days

Investment

$13,500 for the MVP, $46,000 for the full product

Run it

Inside your business, or as part of your offer to clients

Reduce integration and provider-management effort while keeping model traffic, keys and spend under the buyer's control.

1. Register provider accounts and bring-your-own keys.
2. Expose one OpenAI-compatible endpoint.
3. Route requests by cost, speed or task rules.
4. Retry and fall back to alternative models on failure or timeout.
5. Cache responses to cut repeat cost and latency.
6. Multiplex across models to improve utilization.
7. Handle text, vision, video and speech workloads.
8. Benchmark providers on price, latency and load.
9. Apply rate limits across combined provider usage.
10. Track token usage, cost and spend by team.
11. Set budget caps and cost alerts.
12. Run built-in evaluations and regression tests.
13. Orchestrate multi-agent and multi-model chains.
14. Scope provider and model access per application key.
15. Compare the reviewed result with the recorded baseline and value assumptions.
16. Capture corrections and named-owner approval before consequential use.
17. Export a versioned reviewed gateway configuration with source references and unresolved questions.

Everything these tools do, in one app

• Unified API endpoint Access multiple AI model providers through a single API interface.Found in LLM Gateway, RouKey, Respan Gateway and 7 more
• OpenAI-compatible interface Use an API that follows OpenAI's format for easy integration with existing code.Found in Respan Gateway, IonRouter, MakeHub.ai and 3 more
• Bring your own keys Use your own API keys or credits from providers instead of the platform's.Found in LLM Gateway, RouKey, ngrok AI Gateway
• Automatic routing Automatically select the best model or provider for each request based on criteria like cost, speed, or task.Found in RouKey, MakeHub.ai, Router by Ramp and 1 more
• Fallback and retries Automatically retry requests with alternative models or providers when one fails or times out.Found in Respan Gateway, Free LLM API, ngrok AI Gateway
• Usage analytics dashboard Monitor token usage, costs, and other metrics through a dashboard.Found in LLM Gateway, RouKey, Respan Gateway and 2 more
• Cost tracking and controls Track spending and set limits or caps to prevent cost overruns.Found in LLM Gateway, Respan Gateway, Router by Ramp
• Caching Cache responses to improve performance and reduce costs.Found in LLM Gateway, Respan Gateway
• Self-hostable Deploy the gateway on your own infrastructure for control and privacy.Found in LLM Gateway, Free LLM API
• Multi-agent workflows Orchestrate complex AI chains involving multiple models or agents.Found in RouKey
• Built-in evaluations Run regression tests and quality checks on model outputs before deployment or on live traffic.Found in Respan Gateway
• Model multiplexing Switch between models quickly to improve utilization and reduce latency.Found in IonRouter
• Multi-modal support Handle not just text but also vision, video, and text-to-speech workloads.Found in IonRouter
• Real-time provider benchmarking Continuously compare providers on price, latency, and load to inform routing decisions.Found in MakeHub.ai
• Rate limiting Manage combined usage across providers by limiting request rates.Found in Free LLM API
• Spend visibility by team Map token usage and costs back to specific teams or budgets for internal accounting.Found in Router by Ramp
• Model insurance Receive compensation for slow responses or inaccurate outputs.Found in ZenMux
• Access control Control which providers and models each application or developer can use with separate keys.Found in ngrok AI Gateway

What goes in, what comes out

The workflow

1. In
   Start with

   Provider accounts, application keys, routing rules and budget limits

2. 1

   Confirm the buyer's problem and scope

3. 2

   Collect provider accounts

4. 3

   Application keys

5. 4

   Routing rules and budget limits

6. 5

   Then follow this sequence: 1

7. Out
   Finish with

   Reviewed gateway configuration with measured routing and spend reports

AI does the heavy lifting, people stay in charge

Use AI to interpret permitted inputs, suggest structured mappings and generate candidate outputs for the three stated task modules. Use deterministic code for arithmetic, schema validation, hard constraints and reproducible tests. Review source-linked explanations and uncertainty before accepting results. One self-hosted deployment and one approved provider set; final routing policy and spend decisions remain engineering. A model suggestion is never a verified fact, professional decision or authorization to act.

What your team sees

Primary screens: Provider and key registry, Routing and policy editor, Live traffic and spend dashboard. Use a provider list with health and latency, a central rule editor for routing, fallback and caps, and a right-hand panel for request logs, evaluations and comments. Let users compare routing policies side by side. Display draft, changes requested and approved states. Provide a client preview link with comments anchored to the relevant policy. Make the task-specific outcome reviewed gateway configuration with measured routing and spend reports visible beside its evidence, review state and value baseline.

Accounts and administration

Project ownership, key versions, client comments, approval states, usage allowances, revision limits, download history and a rights record for supplied material. Add organization access boundaries, named reviewers, usage caps, data retention controls, export logs and explicit approval for external actions.

Integrations and data access

Provider APIs, application code repositories, observability tools and billing systems. Cloud or on-premise deployment, log export and alerting destinations. Start with file exchange and validate destination specifications before promising direct publishing. Start with authorized file exchange. Validate current provider access, usage rights and schema behavior before promising a connector.

We build with our own AI software development factory, so most implementations take days to a few weeks of creation time, not months. You see working software at every step, and exact timing depends on availability.

1. 1

   Scoping call

   Day 1

   Thirty minutes on your process, your data and how you want to run it: for your own team, or for your clients. You get a fixed scope and price for the MVP.

2. 2

   MVP

   7 days

   One buyer segment, one recurring use case; first modules: register provider accounts and bring-your-own keys; expose one OpenAI-compatible endpoint. Manual review in the loop. Built by our AI software factory.

3. 3

   Paid pilot

   8 days

   Accounts, roles, review states, audit trail and the first integration, hardened for two to three paying pilot customers.

4. 4

   Full product

   3 weeks

   Self-serve onboarding, billing, monitoring and the wider integration set.

5. 5

   Run and improve

   Monthly

   We host, monitor and improve it for a fixed monthly fee, or hand it over to your team. How the retainer works.

Why we start with an MVP

An MVP, or minimum viable product, is the smallest version that your users can actually work with. It is not a cheap version of the full solution. It is a test, built to answer the questions that decide whether the rest is worth building.

1. Pick the riskiest assumption. Here: will engineering teams and platform owners routing production AI traffic across several model providers use it to solve "teams integrate each model provider separately, cannot see or cap combined spend, and have no tested fallback when a provider degrades"?
2. Build only what tests it. One team, one use case, a few core modules. People do the rest by hand for now.
3. Run a paid pilot. Agree quality and outcome thresholds before the pilot using this measure: Requests served per provider incident and cost per accepted response.
4. Measure, then decide. Track requests served per provider incident and cost per accepted response; accepted-output rate; material error rate; reviewer correction time; actual repeat purchase. Then expand, change course or stop, with evidence instead of opinions.

MVP scope for this solution. Pilot scope: One self-hosted deployment and one approved provider set; final routing policy and spend decisions remain engineering. Implement one approved input format, a bounded representative case set and the first two task modules: register provider accounts and bring-your-own keys; expose one OpenAI-compatible endpoint. Support the third module with operator review: route requests by cost, speed or task rules. Include source references, corrections, basic organization access, approval states, export and value measurement. Use managed operator assistance for unresolved exceptions. The cost estimate covers this narrow prototype, not unrestricted multi-tenant scale, complex production integrations, specialist certification or physical operations.

After the MVP. Once paid pilots prove usefulness, automate repeatable reviewed steps and add one verified source integration. Expand supported inputs and case volume only after new evaluation cases pass. Build reusable customer configurations and recurring value reports around reviewed gateway configuration with measured routing and spend reports. Retain the explicit scope boundary: One self-hosted deployment and one approved provider set; final routing policy and spend decisions remain engineering.

What the build depends on. Key upload and preview, asynchronous routing jobs, editable version history, reviewer access and tested export formats. High-fidelity production requires specialist engineering QA. Obtain representative authorized cases, baseline measurements, qualified reviewers and a buyer-side decision owner. Specific limitation: One self-hosted deployment and one approved provider set; final routing policy and spend decisions remain engineering.

A planning range to start the conversation, not a quote. You pay per phase, so you can stop after the MVP.

Running costs per month

A rough indication of monthly hosting and AI model costs once it is live, not tested. Real costs depend on usage, file sizes and the models chosen.

Your brand, or this one

Run it under your own brand, or start from this concept style.

Selling it to your own clients: the go-to-market playbook

Pricing to test

Test a USD 300-1,500 fixed pilot for one defined gateway package. Offer a monthly production allowance after repeat demand. Quote complex multi-agent or self-hosted deployments separately. These are test prices, not market benchmarks. Package the initial sale as one bounded reviewed gateway configuration with measured routing and spend reports. Recurring fees must specify volume, review depth and integration support. For exchanges, test a disclosed coordination or successful-service fee rather than holding customer funds. Reprice only after measuring real delivery labor; platform-build cost is separate from a commercial pilot fee.

Message to test

Reduce integration and provider-management effort while keeping model traffic, keys and spend under the buyer's control. Demonstrate a concrete reviewed gateway configuration with measured routing and spend reports using the buyer's approved example and show the baseline, corrections and actual delivery effort.

Where to find buyers

Engineering teams and platform owners routing production AI traffic across several model providers professional communities; specialist consultants serving this buyer; permissioned partner introductions; practical demonstrations at relevant trade or practitioner events.

Lead magnet

A reviewed sample reviewed gateway configuration with measured routing and spend reports from a small authorized input set, with a transparent calculation of requests served per provider incident and cost per accepted response and no promised savings.

The first 30 days

1. Week 1: interview five engineering teams and platform owners routing production AI traffic across several model providers and inspect a recent example of teams integrate each model provider separately, cannot see or cap combined spend, and have no tested fallback when a provider degrades.
2. Week 2: prepare a consented or synthetic demonstration of the three task modules.
3. Week 3: seek one bounded paid pilot with agreed baseline and acceptance criteria.
4. Week 4: measure requests served per provider incident and cost per accepted response, reviewer effort and repeat-purchase interest. This is a demand-validation plan, not a thirty-day full-product delivery promise.

Paid pilot

Agree quality and outcome thresholds before the pilot using this measure: Requests served per provider incident and cost per accepted response. Continue only if the buyer accepts the actual output, the intended job outcome improves without unacceptable errors, and measured delivery cost fits willingness to pay. Revise or stop if access is unavailable, qualified review cannot be provided, or apparent savings disappear after corrections and support. Use held-out cases when comparing model quality; use a properly reviewed comparison design before making causal claims. Record missing cases and negative results alongside successful outputs.

Success metrics

Requests served per provider incident and cost per accepted response; accepted-output rate; material error rate; reviewer correction time; actual repeat purchase.

Retention and expansion

Repeat the workflow when the buyer again needs reviewed gateway configuration with measured routing and spend reports. Retain permissioned settings and reviewed examples, report realized value honestly, and sell increased volume or adjacent approved workflows only after contribution margin and quality remain acceptable.

Why clients would pick it

A reusable library of approved routing policies, provider benchmarks and review examples, together with reliable delivery for a narrow engineering niche. Build a permissioned library of representative task cases, reviewer corrections and verified operating constraints for engineering teams and platform owners routing production AI traffic across several model providers. Repeatable delivery and useful integrations matter more than access to a base model.

Alternatives and positioning

LLM Gateway, RouKey, Respan Gateway, IonRouter, MakeHub.ai, Free LLM API, Router by Ramp, Merlin Unified API, ZenMux and ngrok AI Gateway, plus direct provider SDKs. Compare this product with the buyer's present method on requests served per provider incident and cost per accepted response. Offer a bounded paid workflow instead of claiming broad autonomous expertise. Market uniqueness and competitor coverage are not verified.

Main delivery costs

Provider calls, evaluation runs, storage, reviewer hours, client revision rounds and licensed source assets. Additional initial validation requires representative authorized sample preparation, buyer interviews, buyer-side evaluation and bounded validation of reviewed gateway configuration with measured routing and spend reports. Track cost per accepted output, including correction work, unsuccessful cases and support.

Preserve key security, source attribution, routing accuracy and usage permissions. Engineering owners approve substantive changes and deployment scope. One self-hosted deployment and one approved provider set; final routing policy and spend decisions remain engineering. Keep all consequential actions under authorized human control and do not fabricate missing inputs, permissions, professional judgments or market evidence.