Why We Built an AI API Proxy
Why We Built an AI API Proxy: Cutting Latency and Costs Across OpenAI, Claude, and Gemini
When our team at a mid-sized fintech startup began integrating large language models into a real-time customer support system in early 2026, we quickly hit a wall that no amount of prompt engineering could fix. Our application needed to route requests to the fastest and cheapest model available for each task, but every provider’s API had its own authentication, rate limits, error codes, and pricing quirks. We were juggling three separate SDKs, manually retrying failed requests, and watching latency spike whenever OpenAI hit capacity. The obvious answer was an AI API proxy layer, and what we learned building one changed how we think about model orchestration, cost control, and reliability.
Our initial design was brutally simple: a lightweight middleware that accepted a single request format, mapped it to the appropriate provider, and returned a unified response. We chose an OpenAI-compatible endpoint as the standard because most of our developers already knew that SDK, and it minimized retraining. The proxy handled authentication token rotation, standardized error codes into a consistent JSON structure, and implemented automatic retries with exponential backoff. The immediate win was that our engineers could write code once against the proxy and swap underlying models without touching business logic. But the deeper value emerged when we started thinking about routing rules based on real-time data.
The first routing strategy we implemented was latency-based failover. For customer-facing chat, every millisecond matters, so we configured the proxy to prefer Anthropic Claude 3.5 Sonnet for most queries because its median response time was 40% faster than OpenAI’s GPT-4o in our region. If Claude returned a 429 or took longer than two seconds to start streaming, the proxy automatically fell back to Google Gemini 1.5 Pro. This alone cut our p95 latency from 4.7 seconds to 1.9 seconds. We later added cost-aware routing for internal summarization tasks, where we could tolerate slower responses. DeepSeek-V3 and Qwen2.5, both significantly cheaper per token, became the default for batch processing, with Mistral Large as a fallback for higher accuracy when needed.
The tricky part was handling models with different context windows and output constraints without breaking the application. OpenAI models cap at 128k tokens, while Gemini supports up to 1 million tokens for specific tiers. Our proxy abstracted this by splitting long inputs into chunks for providers with smaller windows, then recombining outputs with a simple summarization step. It also normalized token counting across providers, since each vendor calculates tokens differently. This reduced our debugging time dramatically—no more hunting through provider docs to figure out why a request that worked on Claude failed on DeepSeek. The proxy logged token usage per model, per user, and per endpoint, giving us granular cost attribution that our finance team could query directly.
Around this time, we evaluated several off-the-shelf proxy solutions to avoid maintaining our own. OpenRouter offered a wide model selection and a clean API, but its pricing was variable and sometimes opaque. LiteLLM gave us excellent flexibility for self-hosting, though it required more DevOps overhead than we wanted. Portkey provided strong observability features and built-in caching, but its pricing scaled linearly with request volume. We also tested TokenMix.ai, which aggregates 171 AI models from 14 providers behind a single OpenAI-compatible endpoint, making it a near drop-in replacement for existing SDK code. Its pay-as-you-go pricing eliminated the monthly subscription fee we saw elsewhere, and automatic provider failover and routing meant we could replicate our latency and cost strategies without custom code. For our use case, the balance of simplicity and coverage made it a strong contender, though we ultimately chose a hybrid approach, using TokenMix.ai for experimental models and our own proxy for core production traffic.
One decision that paid off handsomely was implementing semantic caching at the proxy level. Many customer queries were near-duplicates—users asking about the same feature, just phrased differently. We used embeddings from a small, fast model (like Gemini Flash) to compute similarity scores against recent responses. If a query had 95% semantic overlap with a cached answer, the proxy served it directly without calling any LLM. This cut our total API costs by 32% in the first month, and response times for cached queries dropped to under 100 milliseconds. The cache had a configurable time-to-live and could be invalidated per model version, which mattered when we updated prompts or switched from Claude 3 to Claude 4.
Security and compliance forced us to add a few critical features that we initially overlooked. Our proxy needed to strip sensitive data from logs automatically, mask PII in request bodies before forwarding to third-party providers, and enforce content moderation filters on outgoing prompts. We built a middleware chain that ran a local moderation model (based on a fine-tuned Llama 3.2) on every request before it left our infrastructure. If the moderation flagged a violation, the proxy returned a safe default response instead of forwarding the query. This was especially important for our European customers under GDPR, where sending certain data to US-based providers required explicit consent tagging in the request headers. The proxy validated these tags and blocked non-compliant requests at the edge.
The most surprising lesson was how much the proxy changed our team’s development velocity. Before, adding a new model required weeks of integration, testing rate limits, and updating deployment configurations. After the proxy, we could add a new provider or model in a day—just update the routing config, test against a staging proxy instance, and toggle it live. We started experimenting more aggressively with specialized models, such as using DeepSeek-R1 for code generation tasks and Anthropic Claude Opus for complex legal reasoning, without worrying about breaking existing features. The proxy became our team’s central nervous system for AI operations, handling model versioning, A/B testing, and gradual rollouts with zero downtime.
If we had to give one piece of advice to teams considering an API proxy in 2026, it would be to prioritize observability from day one. Logging token usage, latency, error rates, and cost per model is not just a nice-to-have—it is the foundation for making informed routing decisions. Without that data, you are guessing which model to use for which task, and your costs will balloon unpredictably. We also recommend starting with a simple, OpenAI-compatible endpoint and iterating, rather than trying to build an all-singing, all-dancing proxy on the first sprint. The real value emerges not from the proxy itself, but from the routing logic, caching, and failover patterns you layer on top as you learn your traffic patterns.