5 AI Access and Gateway Approaches for Growing Product Teams
A product team can encounter two very different problems as its AI features grow. The first is access: too many provider accounts, model identifiers, and integrations to maintain comfortably. The second is control: a need to govern routing, observe failures, allocate spending, or operate infrastructure within a particular environment.
A single platform may address parts of both problems, but they are not the same requirement. Treating every multi-model service as a full gateway replacement can lead to unnecessary complexity or missing capabilities after migration.
These five approaches provide a practical way to separate the choices. They draw on different deployment and responsibility models, so the list is not a direct price or speed ranking. Start with the requirement that is blocking the next product release.
1. LiteLLM When the Team Wants an Application-Controlled Gateway
LiteLLM provides an open-source route to building a shared interface across model providers. Its documentation covers gateway behavior, provider integrations, and controls around AI traffic. It is relevant when an engineering team wants the access layer to be an explicit part of its own architecture.
That can be useful for a product with several services calling different models. A common gateway boundary can reduce repeated provider-specific code and provide a consistent place to apply supported policies.
The operational responsibility still matters. Running a gateway means managing its configuration, credentials, updates, availability, and integrations. A self-hosted component is not maintenance-free because its source code is available.
The evaluation should include failure scenarios, not just a successful routed request. Verify how the chosen configuration handles unsupported parameters, provider errors, and any fallback behavior required by the product. Use this approach when the team wants that degree of ownership and can support it.
2. GPTProto When the Immediate Need Is Simpler Hosted Access
GPTProto fits a different part of the decision: consolidated hosted access to multiple model categories. It is a candidate when the team primarily wants to reduce repetitive provider integration and account setup rather than operate a custom serving stack.
When researching Portkey alternatives, keep this access-layer use case distinct from full governance, private deployment, or automatic routing requirements. A service can be useful without being a feature-for-feature replacement for every enterprise gateway capability.
Start with the product’s required models and operations. Validate the relevant endpoints, request fields, media job behavior, and usage reporting. Keep any provider-specific feature behind a deliberate adapter rather than assuming a common account eliminates all compatibility work.
This approach is worth testing when simpler access removes the main obstacle to shipping. If the product needs gateway controls beyond documented access capabilities, assess those separately instead of filling the gap with an assumption.
3. Merge Gateway When Managed Routing Is the Main Requirement
Merge presents Gateway as a product for routing LLM access and controlling spend and performance. A managed control-plane approach is relevant when the team wants gateway functionality without making operation of the gateway service its own project.
The first assessment should be functional rather than promotional. Identify the providers, controls, and reporting needs that the product actually requires. Verify them against the selected service and plan instead of assuming that a general gateway description covers every feature.
A product with several internal teams may need to connect requests to projects or budgets. An application handling customer-specific traffic may need a different allocation model. Clarifying that distinction makes the evaluation more useful than checking whether an analytics screen exists.
Consider this approach when managed routing and oversight address the immediate problem. Keep current commercial terms, credential arrangements, and supported integrations separate from assumptions carried over from another provider.
4. Eden AI When the Application Spans More Than LLM Requests
Eden AI presents access to multiple AI categories, including language-model and broader AI-service workflows. It is relevant when an application combines generative features with tasks such as document processing or speech-related services.
This is a different requirement from standardizing only a chat-completion request. A product may need extracted fields from a document, a transcription, and a generated summary. Those outputs have different schemas and acceptance criteria, even when they are accessed through a shared platform.
During evaluation, map the actual services being replaced. Confirm the required task, supported input, output structure, and selected provider rather than treating the platform’s full feature list as a single contract.
Choose this approach when broader service aggregation reduces meaningful integration work. A language-model-only application may prefer a narrower gateway, while a mixed AI workflow may benefit from the additional categories.
5. TrueFoundry When the Deployment Environment Is a Deciding Factor
TrueFoundry offers an enterprise AI gateway and related infrastructure capabilities. It is a candidate when the gateway decision is tied to the organization’s existing platform environment and governance requirements rather than only to a developer’s first integration.
A useful assessment starts with the actual constraints. Determine where the relevant services must run, how identity and credentials are managed, and what the organization needs to observe or approve. Then verify which capabilities and deployment arrangements are available for the selected product.
Avoid evaluating enterprise architecture with a generic “supports multiple models” checkbox. Deployment ownership, policy enforcement, and integration with existing operations may be the decisive factors.
This route is most relevant when those requirements are real and documented. A small product team should not assume that a more extensive infrastructure platform is automatically a better fit for a simple model-access problem.
Make the Migration a Product Decision
A gateway change affects more than backend configuration. It can alter how the application reports errors, handles waiting states, enforces spending limits, and recovers when a provider is unavailable. Define those behaviors before moving production traffic.
Portkey’s own site now identifies the offering as PRISMA AIRS AI Gateway. That makes it particularly important to compare current product documentation and commercial arrangements rather than relying on an older category label or assuming an acquisition changes every feature immediately.
Run a limited pilot with representative requests, clear acceptance criteria, and a rollback path. Compare the work required to operate each option, not just the apparent simplicity of its first API call. The right choice gives the team a clearer responsibility boundary as the product grows.
