AI decisions Architecture and technology
AI gateway vs direct API integrations: how should applications connect to models?
Use a centralised AI gateway when you need to track costs across departments, switch models without rewriting code, and enforce data redaction or rate limits at scale. Use direct API integrations only for isolated pilots or tightly coupled applications where sub-millisecond latency is critical and the team can manage its own model governance. For most enterprises scaling AI, a gateway is the standard architecture to prevent fragmented and unmanaged AI consumption.
The options
AI gateway
A centralised proxy layer that intercepts all requests from your applications to AI models to handle routing, authentication, logging and cost tracking.
Direct API integrations
Applications connect straight to the provider's API endpoints using native SDKs, handling their own authentication, retries and usage logs.
Side by side
| Criterion | AI gateway | Direct API integrations |
|---|---|---|
| Model lock-in | Low; the gateway abstracts the API, letting you swap a model in the background without changing application code. | High; each application hardcodes the specific model's SDK and format, requiring developer work to switch providers. |
| Cost tracking and limits | Centralised dashboard showing spend per team, application or user, with hard budgets and rate limits. | Fragmented; finance only sees the total bill from the provider, making chargebacks difficult. |
| Security and compliance | Can inspect prompts globally to redact PII, block malicious injections and log interactions for audit. | Each team must build and maintain its own guardrails and logging, risking inconsistent security standards. |
| Latency and performance | Adds a slight network hop (milliseconds), but enables semantic caching and fallback routing if a model fails. | The most direct path to the model, eliminating middleman latency, but lacks global fallback logic. |
| Credential management | Developers use internal single sign-on or corporate keys; the gateway holds the actual vendor API keys. | API keys are distributed across multiple repositories and environments, increasing the risk of leaks. |
| Maintenance overhead | Requires setting up and maintaining the gateway infrastructure or subscribing to a platform. | Zero central infrastructure to run, but pushes maintenance burden to individual development teams. |
| Standardisation | Forces all traffic into a standard format, making it easy to enforce company-wide policies. | Teams choose their own patterns, leading to duplicate work and varying levels of reliability. |
Choose AI gateway when…
- Multiple departments or applications are building AI features and you need unified oversight.
- You want the freedom to route requests dynamically between different vendors or open-weight models based on cost or speed.
- Compliance requires a central audit trail of all prompts and responses.
- You need to enforce rate limits and budgets across different internal teams to prevent bill shock.
Choose Direct API integrations when…
- You are running a standalone, isolated pilot and want to avoid setting up new infrastructure.
- The application relies on highly specific features of one vendor's SDK that are not easily abstracted.
- You operate in an environment where even milliseconds of added latency are unacceptable.
When to combine them
You can mix both approaches during a transition. A standard practice is to allow direct API calls in an isolated sandbox for rapid prototyping and discovery, but require all production traffic to go through the enterprise AI gateway. This gives developers freedom to experiment while ensuring that deployed applications meet security, cost and compliance standards.
Common mistakes
- Waiting until the enterprise has dozens of direct integrations in production before trying to implement a gateway.
- Building a custom gateway from scratch instead of using mature open-source or commercial platforms.
- Treating the gateway only as a load balancer, missing the opportunity to add semantic caching and PII redaction.
- Using direct APIs but sharing the same root API key across all departments, making cost attribution impossible.
How Thinkia approaches it
We view the abstraction layer as the foundation of an enterprise AI architecture. Without it, organisations often lose track of what models are running, what data they see, and how much they cost. Placing a gateway between applications and models decouples business logic from AI providers, enabling teams to adapt to new models as they are released.
Thinkia evaluates AI deployment maturity by checking if applications connect to models through a governed layer. Synapse acts as a model-agnostic gateway, providing corporate single sign-on, dynamic routing between models, rate limiting, and ROI dashboards. This setup allows applications to switch models without rewriting code, giving security and finance teams visibility over usage and costs.
Thinkia products involved
- SynapseGoverned agentic platform: agents, models, costs and data in one place.
- EU AI Act governance guideRisk tiers, timeline, roles and a 20-point checklist. Not legal advice.
Related AI solutions
Frequently asked questions
Does an AI gateway increase latency?
It adds a minor network hop, typically in the low milliseconds. However, it can actually reduce overall latency if you implement semantic caching, which serves repeated questions instantly without calling the model at all.
Can we build our own gateway?
You can, but it is rarely a good use of engineering time. Routing, load balancing, streaming support and caching are complex to build and maintain. Using an existing platform or open-source solution is almost always more efficient.
How does an AI gateway help with vendor lock-in?
It standardises the API interface. Your applications talk to the gateway using one format, and the gateway translates that to OpenAI, Anthropic, or an open-weight model. To switch models, you update the gateway configuration, not the application code.
Do we still need an AI gateway if we only use one provider?
Yes. Even with a single provider, a gateway provides essential capabilities that direct APIs lack: team-based cost attribution, global prompt logging for compliance, centralised API key management and internal rate limits to prevent budget overruns.
How does this affect the EU AI Act?
A gateway simplifies compliance. If you act as a deployer, you must monitor usage, keep logs and ensure human oversight. A centralised gateway collects these logs automatically across all applications, making it much easier to prove compliance to auditors.
Keep exploring
Related decisions
- Single AI vendor vs multi-model strategy: should you bet on one provider?
- MCP vs custom API integrations: how should your AI agents connect to your systems?
- In-house team, AI consultancy or platform: who should build your AI?
Key terms
Thinkia articles
- Vendor-Neutral AI: What Is an AI Application Server and Why It Matters
- Dynamic Model Routing: The Key to Cost-Effective AI Agents
- AI Governance: The Strategic Engine for Competitive Advantage
Whitepapers
- Shadow AI. Govern it, don't ban it.Three in four employees already use AI where IT cannot see it. Banning removes the witnesses, not the risk: make visible, govern, and enable.
- AI–SDLC. From code to intent.Thinkia's playbook for governed AI software delivery—shared specs, gates, risk tiers, and one model gateway so teams ship with audit-ready evidence.