Last updated Apr 9th 2026
OpenAI-compatible API gateway for 200+ models from 30+ providers. One integration gives you smart routing, persistent memory, web search, context compression, and input security. All features activate per request by adding a single parameter. No new SDKs, no infrastructure changes.
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Sansa Benchmark data released to the public in December 2025.
Sansa Censorship Benchmark covered by online media world wide in December 2025. (gazeta.ru, substack.com, etc.)
Sansa Benchmarks go viral on X and Reddit in December 2025.
Sansa reaches first 100 waitlist signups in December 2025.
Sansa surpasses 12 Billion tokens processed in January 2026.
Sansa launched to the public on February 17, 2026.
Sansa releases OpenClaw installer for the open source AI agent framework: https://github.com/sansa-ai/openclaw-installer
Sansa adds Text and Audio processing to their OpenAI compatible API in March 2026.
Sansa is launching Memory, Input Guard (input security), Context Compression, AI Web Search, and Smart Routing as gateway features in 2026.
Sansa is a unified LLM gateway. One API, one key, one base URL. OpenAI-compatible. 200+ models from 30+ providers (OpenAI, Anthropic, Google, Meta, Mistral, and more). The gateway automatically routes between providers for uptime. What you get depends on what you pass in the request:
- Any model by name: pass claude-3.5-sonnet, gpt-4o, gemini-2.5-pro, or any of the 200+ supported models. The gateway handles provider-level failover automatically. - Smart Model Routing: pass sansa-auto as the model and a routing model trained on 20+ Billion tokens selects the best model for the prompt. Better than frontier quality at roughly half the cost. - Memory: add a memory parameter and the model remembers users across sessions. No vector database or retrieval pipeline required. - AI Web Search: append :search to the model name and the model gets grounded with live web results and inline citations. - Context Compression: add a compression parameter to strip low-value tokens from long inputs. ~40% faster latency, ~30% fewer input tokens. - Input Guard: add input_guard to detect PII and prompt injections before they reach the model.
The Sansa gateway is OpenAI-compatible. Change the base URL and API key and existing OpenAI SDK code works immediately. Pass the model you want, or pass sansa-auto to let the routing model choose. Add parameters to the request body to turn on memory, search, compression, or input guard. Everything is one API call.
- 200+ models supported - 30+ inference providers with automatic failover - 99.9% uptime - 20+ Billion training tokens for the smart routing model - 10-15ms latency added by smart routing (sansa-auto) - ~40% token savings with context compression - 98% accuracy for input guard detection - Sub-50ms latency for memory and search - $5 per million tokens with smart routing vs $10-25 for frontier models
Compatible with OpenAI SDK (three line code change) and the Sansa SDK. No new SDKs, middleware, or infrastructure changes required. Every feature is opt-in per request.
Free community benchmarking tool. Tests individual models from various providers on real-world tasks. Does not test the sansa-auto endpoint. Results published at trysansa.com/benchmark
SOC 2 Type II ready. Data never sold or shared with third parties. All requests encrypted in transit and at rest. Opt out of training data. US-hosted infrastructure. Full user control over data at all times.
Last updated Apr 9th 2026
Smart Model Routing is a feature of the Sansa gateway. Pass sansa-auto as the model and a proprietary routing model selects the best model for each prompt from a pool of 200+ models across 30+ providers.
Instead of choosing a model yourself, pass sansa-auto as the model name. The routing model, trained on 20+ Billion tokens of real-world requests, classifies the prompt and picks the optimal model. The routing decision happens in under 20ms.
Not every prompt needs the most expensive model. Many smaller models excel in areas that frontier models handle no better. Smart routing identifies those cases automatically, reducing cost without sacrificing quality.
The routing model outperforms individual frontier models by evaluating the capability profile required for each request and matching it to the strongest model for that profile. Average of 10% higher performance than single frontier models across MMLU, HLE, BBEH, and BigCodeBench.
Roughly half the cost of using a single frontier model. $5 per million tokens versus $10-25 for frontier models. Savings come from using less expensive models when they are the best fit for a given prompt.
Pass sansa-auto as the model in your request to the Sansa gateway. The gateway is OpenAI-compatible. Change the base URL and API key and existing OpenAI SDK code works immediately. All other gateway features (memory, search, compression, input guard) work alongside smart routing.
- 20+ Billion training tokens for the routing model - Sub-20ms routing latency - 1000+ requests per second - 200+ models in the routing pool - 30+ inference providers - ~50% cost reduction versus frontier models
Your data is never shared with model providers or third parties. All infrastructure runs in the US. Full user control over data at all times.
Smart Model Routing is one feature of the Sansa gateway. Other features include persistent memory, AI web search, context compression, input guard, and automatic provider failover. All activate through the same API by adding parameters to the request.
Learn more at trysansa.com/product/smart-routing
Smart routing
Sansa matches each request to the model best suited for it based on task type, tooling, and context.
TRAINING TOKENS
LATENCY
RPS
Sansa outperforms individual frontier models by evaluating the capability profile required for each request and matching it to the best model for the task. Resulting in higher quality answers across benchmarks and real-world tasks.
Not every prompt needs the most expensive model. Many inexpensive models excel in areas you would not expect, by selecting these models under the right conditions you can reduce your spend by 50% or more.
Your data is never shared with model providers or third parties. You have full control over how it is used. All infrastructure runs in the USA.
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