An LLM gateway is the control plane between your application and one or more model providers. It standardises API calls, routes requests, applies budgets and policies, records usage, and provides fallbacks when a provider is slow or unavailable. For Indian developers, that layer matters because production systems must often balance rupee-denominated budgets, overseas provider latency, sensitive personal data, Indic-language quality, and uneven rate limits.
The best LLM gateway for Indian developers is not automatically the tool with the largest provider catalogue. It is the one that fits your deployment model and gives you control over routing, privacy, reliability, and cost without becoming another platform your team has to operate.
What an LLM gateway should do
A gateway can expose one OpenAI-compatible endpoint while routing traffic to providers such as OpenAI, Anthropic, Google, open-weight models, or your own vLLM deployment. Core capabilities include:
- Provider abstraction: Change models without rewriting application logic.
- Routing and fallbacks: Send requests to an alternate model when latency, availability, or rate limits cross a threshold.
- Authentication and virtual keys: Separate credentials and permissions by product, team, environment, or customer.
- Observability: Track tokens, latency, errors, model quality signals, and cost by user or feature.
- Policy enforcement: Redact sensitive fields, restrict models, and apply retention rules before requests leave your environment.
- Caching and batching: Reduce repeated inference costs and improve response times where the workload permits it.
This is especially useful for products such as voice agents, tutoring platforms, and multilingual customer support. Teams building those systems should also assess the practical benefits of using a voice agent for Indian businesses, where streaming latency and failure recovery are more important than a simple text-generation demo.
The strongest gateway options
Portkey
Portkey is a managed gateway and observability platform suited to teams that want a polished control plane without building every operational feature themselves. It supports routing, retries, fallbacks, prompt management, logs, and usage controls across multiple providers.
Choose it when: your team values rapid deployment, detailed request tracing, and centralised governance. Verify current data-processing, retention, regional hosting, and enterprise terms before sending regulated or sensitive information through the managed service.
LiteLLM
LiteLLM is a popular open-source proxy that presents many providers through a common interface. It can be deployed in your own cloud account, Kubernetes cluster, or private network, making it attractive to startups that need control over data flows and infrastructure costs.
Choose it when: you have engineering ownership for upgrades, scaling, secrets management, monitoring, and incident response. Self-hosting does not remove compliance responsibilities; it moves more of them to your team.
Helicone
Helicone combines gateway functionality with usage analytics and cost visibility. It is useful when product and finance teams need to understand which customers, features, prompts, or models are driving spend.
Choose it when: usage attribution and debugging are more important than deep platform customisation. Review its deployment and data-handling options if requests contain personal or confidential information.
Kong AI Gateway
Kong is a natural fit for organisations already using Kong for APIs and microservices. It brings familiar authentication, quotas, plugins, and policy controls to LLM traffic, making it better suited to larger engineering and platform teams than to a small prototype.
Choose it when: the gateway must fit an existing enterprise API-management architecture and handle multiple internal consumers.
How to evaluate a gateway in India
1. Measure real latency, not marketing claims
Test from the regions where your users and workloads run, including AWS Mumbai or Hyderabad where relevant. Record time to first token, total completion time, gateway overhead, timeout behaviour, and error rates at different concurrency levels. A gateway with an Indian point of presence can help, but it cannot compensate for a slow provider endpoint or inefficient prompt.
For streaming applications, measure p50, p95, and p99 time to first token separately. Test morning and evening traffic, sudden bursts, and provider throttling. Keep a direct-provider path in your benchmark so you can distinguish gateway overhead from model latency.
2. Design routing around workload classes
Do not route every request to the most capable model. Define classes such as:
- High-stakes reasoning: stronger commercial or self-hosted models, stricter review and fallback policies.
- Routine generation: smaller, cheaper models for classification, extraction, and summaries.
- Indic-language interaction: models benchmarked on the languages, scripts, accents, and domains your users actually use.
- Privacy-sensitive workloads: models deployed in a controlled VPC or private cluster.
Support for custom endpoints is important as Indian teams adopt local and open models. If your product depends on Indic-language quality, compare gateways alongside open-source vision-language models for Indian languages and test the model layer independently from the gateway.
3. Make cost controls operational
Model prices are generally quoted in US dollars, while your revenue, budgets, and investor reporting may be in rupees. The gateway should provide:
- Per-project, per-user, and per-feature token budgets.
- Alerts before a budget is exhausted.
- Separate accounting for input, output, cached, and reasoning tokens where available.
- Model routing based on cost, latency, quality, or availability.
- Exportable usage data for finance and internal chargeback.
Set hard limits for development environments. Add approval gates for new models and log the effective model, prompt version, token count, and retry path for each production request.
Privacy, DPDP, and deployment choices
A gateway is not automatically DPDP-compliant. Compliance depends on your purpose, notices, consent or other lawful basis, contracts, security safeguards, retention, access controls, and incident processes. Map the full data path: application, gateway, logging system, provider, cache, traces, and support tools.
Use PII detection and redaction for fields such as phone numbers, email addresses, government identifiers, financial information, and health data. Avoid logging raw prompts by default. Encrypt secrets, restrict operator access, define deletion procedures, and confirm whether provider logs are retained or used for training.
A practical deployment decision looks like this:
- Managed gateway: Fastest for pilots and small teams; validate contractual and regional data terms.
- Self-hosted gateway: More control in an Indian cloud region or private network; requires platform expertise.
- Hybrid gateway: Keep sensitive workloads private while routing low-risk traffic to managed providers.
Do not claim that a Mumbai deployment alone satisfies every sector-specific requirement. RBI, SEBI, health, education, and government workloads can involve additional contractual and security expectations.
A production rollout plan
Start with one service and two providers. Put the gateway behind a feature flag, preserve the original provider path, and capture baseline latency, error, cost, and quality metrics. Then add routing rules gradually:
1. Standardise request and response schemas.
2. Configure timeouts, retries, and exponential backoff without duplicating non-idempotent actions.
3. Add a fallback only after checking output compatibility and safety behaviour.
4. Introduce budgets and alerts before onboarding more users.
5. Redact and minimise logs before production traffic increases.
6. Run load tests and failure drills, including provider outages and expired credentials.
7. Review model quality in English and the relevant Indian languages, not just API success rates.
For teams building student-facing products, gateway decisions should sit alongside the broader best AI frameworks for Indian student entrepreneurs, since framework portability and deployment skills affect how easily you can change providers later.
Recommendation by team profile
- Prototype or small startup: Start with Portkey or a comparable managed gateway if speed matters; keep prompts and provider configuration portable.
- Privacy-focused engineering team: Evaluate LiteLLM self-hosted in your chosen Indian region, with proper monitoring and secrets management.
- Usage-heavy product team: Prioritise Helicone-style cost attribution, budgets, and customer-level analytics.
- Enterprise platform team: Consider Kong when API governance, identity, quotas, and existing infrastructure are decisive.
The right choice is the gateway that makes failures visible, provider changes safe, and spending predictable. Run a representative benchmark before committing, and reassess as your traffic, data sensitivity, and model mix change.