A unified API for managing multiple AI models gives your application one stable interface for models from OpenAI, Anthropic, Google, open-source runtimes, and private cloud deployments. Instead of embedding provider-specific SDKs throughout your product, you place an AI gateway between application code and inference providers.
This architecture matters for Indian startups because model prices, capabilities, regional availability, and data-handling terms change quickly. A gateway lets a small engineering team switch providers, introduce local or self-hosted models, and control spend without rewriting every feature that calls an LLM.
What a unified AI API should standardise
A useful gateway standardises the parts your product needs while preserving access to provider-specific capabilities when required. At minimum, define a common contract for:
- Authentication: Keep provider keys in the gateway, not in application services or client-side code.
- Messages and inputs: Support text, system instructions, images, documents, and structured content where relevant.
- Generation controls: Normalise temperature, token limits, stop sequences, and response formats, while documenting models that ignore or reinterpret them.
- Outputs: Return text, tool calls, citations, usage, finish reasons, and safety metadata in predictable fields.
- Errors: Map rate limits, timeouts, invalid requests, policy blocks, and provider outages to a consistent error taxonomy.
- Telemetry: Attach tenant, product, region, request type, model, token, latency, and cost metadata to every call.
Use model identifiers that expose both provider and capability, such as anthropic/claude, google/gemini, or self-hosted/qwen-hindi. Avoid hiding model differences behind a generic name like best-model; explicit routing is easier to test and audit.
A production-ready architecture
The gateway normally contains five layers:
1. Request policy: Authenticate the caller, validate input size, apply tenant quotas, and classify the request by risk and workload.
2. Router: Select a model using capability requirements, quality targets, price, latency, geography, and current provider health.
3. Adapter: Translate the common request into each provider’s API format. Keep adapters isolated so provider changes do not spread through the codebase.
4. Resilience layer: Apply timeouts, bounded retries, circuit breakers, fallback rules, and idempotency controls.
5. Observability and accounting: Record traces, token usage, model responses, redactions, and estimated cost for operations and finance teams.
For retrieval-augmented generation, agents, and tool use, treat the gateway as a policy enforcement point rather than only a protocol converter. It should restrict which tools a model can invoke, validate arguments, and prevent an untrusted prompt from changing routing or security policy.
Routing strategies that work
Start with deterministic routing. A practical policy might send:
- Classification, extraction, summarisation, and customer-support drafts to a low-cost model.
- Complex reasoning, long-context analysis, and difficult coding tasks to a stronger model.
- Sensitive workloads to an approved region or a model running in your own cloud account.
- Hindi, Marathi, Tamil, or other Indic-language tasks to models that have passed your own language evaluation.
- Time-critical requests to the provider with the best recent latency, subject to a quality floor.
Do not route solely on advertised benchmark scores. Maintain a task-specific evaluation set containing real examples, including code-mixed Indian language, noisy OCR, long documents, and domain terminology. Teams working with Indic text can compare gateway candidates against resources such as open-source small language models for Hindi before changing production traffic.
Use weighted routing for controlled experiments, but keep a stable control group. Record model version, prompt version, provider, region, and evaluation outcome; otherwise an A/B test will produce data that cannot be explained later.
Fallbacks without silent quality degradation
A fallback is not simply “try another model after any error.” Separate failures into categories:
- Retryable: timeouts, temporary 5xx responses, and provider rate limits.
- Non-retryable: malformed requests, unsupported features, invalid credentials, and policy refusals.
- Quality failures: empty answers, invalid JSON, missing citations, or tool arguments that fail validation.
Set short, explicit deadlines for interactive requests. Retry at most once or twice with jitter, then use a preapproved fallback. If the request requires vision, tool calling, or structured output, the fallback must support that same capability. Never silently downgrade a high-risk workflow to a weaker model; return a clear status or send it to human review.
Streaming complicates failover: once tokens have reached the user, switching providers can produce duplicated or contradictory output. For streaming responses, fail over only before the first token, or design the client to handle a controlled restart.
Cost and latency controls
A gateway makes spend visible, but it does not automatically reduce it. Build controls around actual usage:
- Set per-tenant and per-feature budgets in INR as well as provider billing currency.
- Track input and output tokens separately; long prompts often dominate cost.
- Cache deterministic or low-risk requests, with privacy-aware retention rules.
- Trim conversation history and summarise old turns instead of sending the full transcript.
- Batch offline work such as document indexing and evaluation.
- Cap maximum output length and reject oversized inputs before they reach a paid endpoint.
- Compare hosted APIs with self-hosted inference for steady, high-volume workloads.
For teams considering private inference, how to deploy large language models locally provides a useful starting point. A hybrid gateway can keep burst traffic on hosted providers while directing predictable workloads to GPU infrastructure you control. Measure total cost, including GPUs, engineering time, monitoring, electricity, and idle capacity—not just the per-token rate.
Security, privacy, and Indian compliance
Treat prompts and outputs as potentially sensitive business data. Apply controls before traffic leaves your environment:
- Redact credentials, financial identifiers, health information, and unnecessary personal data.
- Maintain an allowlist of providers, regions, models, and tools for each workload.
- Encrypt traffic and gateway logs; restrict who can view raw prompts and completions.
- Define retention periods and support deletion requests under your data-governance process.
- Keep audit records for model selection, policy decisions, administrative changes, and human overrides.
- Confirm provider terms on training use, retention, subprocessors, and cross-border transfer before onboarding.
The Digital Personal Data Protection framework should be part of your design review, but legal obligations depend on the data, role, and deployment context. For regulated use cases, combine gateway controls with access management, data classification, vendor assessments, and incident procedures. Sensitive workloads may be better served by a private endpoint or a locally deployed model; deploying deep learning models on GKE is relevant when your team needs a managed Kubernetes route for inference services.
Choosing an implementation approach
LiteLLM-style proxy: A practical option when you want an OpenAI-compatible endpoint, provider adapters, routing, fallbacks, and self-hosting. Review operational maturity, security configuration, and feature coverage before making it your critical path.
Managed AI gateway: Faster to launch and often stronger on dashboards, analytics, and enterprise controls. Check data residency, contractual retention, regional availability, and whether gateway fees erase the savings from routing.
Framework-level abstraction: Libraries such as LangChain or LlamaIndex can simplify model selection inside application code. They are useful for orchestration, but they do not replace a central gateway when you need organisation-wide credentials, quotas, audit logs, and traffic policy.
Build your own thin gateway: Appropriate when your requirements are narrow and your team can operate it. Begin with a small adapter interface, not a large platform: request validation, two providers, timeouts, usage logging, and one tested fallback are enough for an initial production version.
A practical rollout plan
1. Inventory every existing model call, prompt, data class, latency target, and failure mode.
2. Define a versioned request and response schema, including streaming and tool-call behaviour.
3. Add one primary and one fallback provider behind the gateway.
4. Create a representative evaluation suite before changing routing.
5. Add redaction, quotas, timeouts, structured logs, and cost dashboards.
6. Shadow traffic to candidate models without exposing their responses to users.
7. Move low-risk workloads first, then expand only after quality and incident tests pass.
8. Review routing, spend, privacy, and provider terms monthly.
A unified API is successful when developers can change inference providers without destabilising product code—and when operators can explain every model decision, failure, and rupee spent. Build the abstraction around measurable capabilities, not marketing labels, and keep an escape hatch for provider-specific features that genuinely improve the product.