AI API integration
Secure AI API integration,with costs under control.
AI API integration is the technical layer between your applications and providers such as OpenAI, Anthropic (Claude) or Google (Gemini): keys stay on the server, usage has limits, every request is logged, and the provider can be changed without rewriting the application.
The prototype works. Production is another story.
A direct call to an AI model is quick to write. The problems come later: the API key ends up in browser code, a faulty loop burns through the budget overnight, the provider has an outage or a rate limit, and nobody knows what was sent and when.
- API keys exposed in client applications or in shared configuration files
- Costs that grow without limit and with no breakdown by application or team
- Rate-limit or availability errors handled badly, which block the application
- Code tied to a single provider, hard to change when a better option appears
Who it is for
- Development teams adding calls to OpenAI, Anthropic or other models in existing applications
- Companies with several applications that use AI and want a single point of control
- IT managers concerned about exposed keys, unpredictable costs or dependence on a single provider
- Companies that have a working AI prototype and want to get it ready for production
What we build into the integration layer
- 01
Keys kept on the server
Applications call your internal service, and the providers' keys live only on the server, in a secrets manager.
- 02
Limits and quotas
Request and budget limits per application, user or team, with an alert before they are exceeded.
- 03
Retries and fallback
Rate-limit and availability errors are handled with controlled retries and, where appropriate, a fallback provider.
- 04
Logging
Every request is recorded with model, duration, cost and outcome, with sensitive data masked.
- 05
Cost control
Usage reports by application and model, and caching for repeated requests.
- 06
A single interface for providers
A shared layer over several providers, so changing the model is a setting, not a rewrite.
How it works
A request's journey through the integration layer
The steps marked in yellow are approved by a person.
- Input
- Action
- Human in the loop
- AI
- Result
- 01
Input
Request from the application
The application sends the task to the internal service, without any provider key.
- 02
Action
Checks and quotas
The service authenticates the application, applies the limits and masks sensitive data.
- 03
Human in the loop
Approval at the threshold
If the approved budget would be exceeded, requests stop until the owner decides.
- 04
AI
Call to the model
The request reaches the configured provider; on error, it is retried or switched to the fallback.
- 05
Action
Log and cost
Duration, cost and outcome are recorded for reporting.
- 06
Result
Consistent response
The application receives the result in the same format, whatever the provider.
What needs to be prepared for API integration
- The list of applications that use or will use AI and the estimated request volume
- Accounts with the model providers, owned and managed by the company
- Approved budgets per application or team
- The rules on which data may be sent externally and which must be masked
- Existing infrastructure: cloud, secrets manager, monitoring system
Possible integrations
- OpenAI API
- Anthropic API
- Google Gemini API
- Azure OpenAI
- Amazon Bedrock
- Mistral AI
- Privately hosted open-source models
- AWS Secrets Manager
- Azure Key Vault
- Grafana
- OpenAI API
- Anthropic API
- Google Gemini API
- Azure OpenAI
- Amazon Bedrock
- Mistral AI
- Privately hosted open-source models
- AWS Secrets Manager
- Azure Key Vault
- Grafana
Each integration's compatibility is checked during the analysis phase, for the systems and versions you use.
Control and transparency
What stays a human decision
- Approving model providers and their data-processing terms
- Setting and changing budgets and usage limits
- Access to logs, which may contain excerpts of company data
- Changing the main model, only after testing
Limitations and how we handle them
- The integration layer does not improve answer quality; it controls access, cost and reliability
- A fallback provider may give different results from the main one, which is why it must be tested separately
- Providers' limits and prices change, and the configuration has to be updated
- Detailed logs have a storage cost and should be kept only as long as necessary
What we monitor in the integration layer
We agree the indicators together before launch and track them afterwards. The values depend on the process, the data and the volume.
- Errors and fallback switches
- How often errors occur per provider and how many times the fallback was used — monitored continuously.
- Latency
- Response time per model and application, with alerts when the agreed thresholds are exceeded.
- Cost per application
- Usage broken down by application, team and model, compared monthly with the budget.
- Key security
- Regular checks that no key appears in client code, code repositories or logs.
How we implement AI API integration
- 01
Inventory and requirements
We list the applications, providers, volumes and data rules.
- 02
Layer architecture
We decide where the service runs, how keys are stored and how errors are handled.
- 03
Moving the calls
We gradually move the applications' direct calls to the shared service, without interruptions.
- 04
Monitoring and handover
We set up the reports and alerts and hand the documentation over to your team.
Use cases
These scenarios are conceptual examples showing what can be built. They are not delivered implementations or results achieved for clients.
- Marketing agency
A shared access point to models
Internal tools that generate copy go through the same service, with a separate budget per client and a monthly usage report.
Conceptual example - SaaS start-up
From prototype to production
Direct calls from the web application are moved to the server, with per-user limits and a fallback provider during outages.
Conceptual example - Retailer with a mobile app
Changing provider without a new release
The model used for product descriptions can be changed in the configuration, after testing, without an app update.
Conceptual example
Frequently asked questions
How is it different from LLM integration?
LLM integration decides what the AI does in your process and what the result looks like. AI API integration is the secure technical connection to the providers: keys, limits, logs, costs, fallback. The first answers the question “what does the AI do”, the second “how does it run safely”.
Why shouldn't the API key be in the application?
Any key included in browser code or in a mobile app can be extracted and used by someone else, at your expense. Keys should live only on the server.
What happens when the provider does not respond?
The service retries a limited number of times, then it can switch to a fallback provider or return a clear error, which the application handles without blocking.
What data appears in the logs?
We agree on it together: usually metadata — model, duration, cost — and, where needed, the content of requests with personal data masked. Access to logs is restricted, and the retention period is set in line with the GDPR and internal policies.
Can we use privately hosted models?
Yes. The integration layer can send requests to open-source models hosted in your infrastructure, alongside commercial providers or instead of them.
Do you work with OpenAI, Claude and Gemini?
The integration layer can work with several providers, for example the OpenAI API, the Anthropic API (Claude models), the Google Gemini API, Azure OpenAI, Amazon Bedrock or Mistral AI, as well as privately hosted models. Each provider's compatibility and terms are checked for your project.
Can’t find the answer? Write to us and we’ll reply directly.
Want secure AI calls with a clear budget?
Tell us which applications already use AI and how the keys are stored. We will suggest the right integration layer.
Related solutions
- LLM integrationLanguage-model features added to the applications and processes you already use, with the model chosen for cost and confidentiality.
- AI evaluation and monitoringTest sets, checks at every change, human spot-check reviews and cost tracking, so you know how your AI performs over time.
- Multi-agent systemsSeveral specialised agents, each with its own role, tools and permissions, coordinated in a single process and supervised by a person. Only where a single agent is no longer enough.