AI API Development
Your own endpoint in front of the model, with a cap on what it can spend.
What is AI APIs?
An AI API is your own endpoint sitting in front of a model, so your applications call a stable interface you control, with limits on cost and usage, caching for repeated requests, and defined behaviour when the underlying provider is unavailable.
Calling a model provider directly from an application looks simplest and creates three problems. Your code is tied to one provider, every call costs money with nothing capping it, and when that provider has an outage your product goes down with it.
Putting your own API in between fixes all three. Applications call your endpoint, and behind it you can cache, cap, meter, and switch providers without touching the applications.
The cost control is the part that gets skipped and the part that hurts. An unmetered AI endpoint is a bill waiting to happen, and the bill usually arrives before anybody has looked at the usage.
Why nothing ships without a spend cap
Duara builds AI APIs from Nairobi for software teams and product companies who want AI capability in their applications without the provider becoming a single point of failure.
We will not ship an AI endpoint without a spend cap and rate limiting. Every team believes their usage will be modest, and the ones who find out otherwise find out from an invoice. It takes an afternoon to add and it has saved clients more than everything else on this page.
Who this is for
- Your app calls a model provider directly
- Nothing caps what AI usage can cost
- A provider outage takes your product down
- You want to change models without changing code
Industries we serve
- Software companies
- Fintech
- E-commerce platforms
- Media and publishing
- Healthcare technology
- Logistics technology
Problems this solves
These are architectural exposures that only become visible under load or under an invoice.
- Direct provider calls in application code. Changing model or provider means changing and redeploying every application that calls it.
- No cap on spend. Usage scales with traffic and nothing stops it, so the first indication of a problem is the invoice.
- Provider outages become your outages. When the model is unavailable your product fails, with no fallback defined.
- Repeated requests paid for repeatedly. The same question is answered and charged for many times because nothing caches the result.
What we deliver
A stable endpoint you own, with spend caps and rate limits, caching for repeated requests, provider fallback, and usage metered by client or feature.
A stable API your applications call
Spend caps and rate limiting enforced
Caching for repeated requests
Fallback behaviour when a provider is down
Usage metered by client, feature or customer
Documentation your developers can work from
Features
- Spend caps as standard. Never optional in anything we ship, because an unmetered AI endpoint is a bill waiting to happen.
- Provider independence. Applications call your interface, so switching model or provider is a configuration change rather than a release.
- Caching where answers repeat. Identical requests are served from cache, which on some workloads removes most of the cost outright.
- Defined failure behaviour. What happens during a provider outage is decided in advance, whether that is a fallback model, a cached answer or an honest error.
- Usage attributed. Cost per customer or feature is visible, which is what lets you price the feature or switch it off.
Benefits
- The bill stops being a surprise. Caps and metering turn AI spend into a number you set rather than one you discover.
- Provider changes get cheap. Moving to a different model becomes a configuration decision instead of a development project.
- Outages degrade instead of failing. Defined fallback keeps a product usable when the provider is not.
- You can price the feature. Knowing cost per customer is what makes an AI feature a commercial decision rather than a hope.
Technologies we use
- Gateway design. A stable interface your applications call, insulating them from provider specifics.
- Rate limiting and quotas. Per client and per key limits so one caller cannot consume everything.
- Response caching. Serving identical or near identical requests without paying for them again.
- Provider routing and fallback. Switching between providers or models by configuration, including on failure.
- Usage metering. Recording consumption by customer, feature and model so cost is attributable.
Caching is where the real savings are on most workloads, and it is unglamorous enough that it is usually the last thing anybody implements.
How the process works, step by step
An AI API usually takes three to six weeks, and the limits are built before the endpoint is exposed to anything.
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Step 1
Free technical review
We look at how your applications call AI today and what your current usage actually costs.
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Step 2
Limits first
Spend caps and rate limits are built before the endpoint is exposed, not added after a surprise.
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Step 3
Endpoint and caching
The interface goes live with caching, which is usually where the cost reduction comes from.
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Step 4
Fallback tested
We simulate a provider outage rather than assuming the fallback works.
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Step 5
Metering and handover
Usage attribution is switched on and the API is documented for your developers.
Quality assurance and how we work
The commitments attached to putting AI in front of your applications.
- Nothing ships without a spend cap. Every team believes their usage will be modest, and the ones who are wrong find out from an invoice.
- Fallback is tested by breaking things. We simulate a provider outage rather than assuming the fallback path works, because untested failure handling usually does not.
- You own the interface. The API is yours and documented, so changing provider later does not require us.
- We report what it will actually cost. Estimated cost per request is given before build, based on your real volumes rather than a favourable example.
- We will say when you do not need a model. Where a rule or a lookup would answer the request, we say so rather than putting a paid model behind it.
Frequently asked questions about AI APIs
Why not call the provider directly?
It ties your code to one provider, nothing caps the spend, and their outage becomes your outage. A gateway fixes all three.
Will you ship without a spend cap?
No. Every team believes their usage will be modest, and the ones who are wrong find out from an invoice.
How much can caching save?
On workloads with repeated questions, most of the cost. It is unglamorous and usually the last thing anybody implements.
What happens when the provider goes down?
Whatever you defined, whether that is a fallback model, a cached response or an honest error. We test it by simulating the outage.
Can we switch models later?
Yes, as a configuration change rather than a release, which is the main structural reason to build the gateway.
Do you support multiple providers?
Yes, including routing simpler requests to cheaper models, which is frequently the largest saving after caching.
How do we know what it will cost?
We estimate cost per request before building, from your real volumes rather than a favourable example.
Can we see cost per customer?
Yes, metered by customer, feature and model, which is what lets you price the feature or switch it off.
What if a rule would do the job?
We say so. Putting a paid model behind a request a lookup could answer is a cost you keep paying forever.
How soon can our apps call it?
Three to six weeks, with limits built before the endpoint is exposed to anything.
Does it work with our existing stack?
Yes. It is an ordinary service in your infrastructure and does not require a particular language or platform.
Can it handle streaming responses?
Yes, where your application needs them, though caching behaves differently and we would discuss the trade off.
What about rate limiting per customer?
Standard, so one caller cannot consume the quota everybody else depends on.
Is our data sent to the provider?
Whatever the request contains is. We scope what gets sent deliberately and can strip fields that do not need to go.
Can we self host the model instead?
Sometimes worth it at volume. We would compare honestly rather than assuming hosted is always right.
Does it log requests?
To the extent you choose. Logging prompts is useful for debugging and a data protection decision, so it is yours to make.
Can your team keep this running without us?
Yes. It is documented and built as an ordinary service rather than as something only we understand.
Can it enforce content restrictions?
Yes, input and output filtering can be applied, though we are clear that no filter is complete.
Where does the API run?
In your own cloud account or hosting, so the endpoint and its logs stay under your control.
What does an AI API cost to build?
Provider count and caching complexity determine it, quoted after the technical review.
Glossary of terms
API and model terms in plain language.
- Gateway
- Your own endpoint sitting between applications and a model provider.
- Rate limit
- A cap on how many requests a caller may make in a period.
- Spend cap
- A hard limit on cost, after which requests are refused rather than billed.
- Caching
- Storing a previous response so an identical request does not need paying for again.
- Fallback
- Defined behaviour when the primary provider is unavailable.
- Token
- The unit models are billed in, roughly a word fragment, and the basis of every cost estimate.
Related services in AI Development
Talk to us about AI APIs
Check what your AI provider bill was last month and whether anything currently prevents it doubling. That is the first thing this fixes.
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