7 Private OpenRouter Alternatives for Teams That Need Data Control (2026)
Looking for OpenRouter alternatives with better privacy controls? We compare Portkey, LiteLLM, Helicone, and 4 more gateways for teams needing data sovereignty.
69% of enterprise leaders now cite AI data privacy as a top concern, according to KPMG's Q2 2025 report. Six months earlier, that number was 43%. The jump reflects what security teams have been warning about: 40% of files uploaded to GenAI tools contain PII or payment card data. Roughly 15% of employees have pasted sensitive code, credentials, or financials into public LLMs.
OpenRouter gives you a single API to access hundreds of models. For prototypes, that works. For production systems handling customer data, medical records, or financial information, it creates a compliance question mark. Your prompts pass through OpenRouter's infrastructure before reaching whichever provider they route to. Even with Zero Data Retention enabled, you are trusting multiple third parties with data that regulators want to know about.
Cloudera's 2025 enterprise AI report found that 53% of organizations identify data privacy as their primary obstacle to AI deployment. In healthcare, finance, and legal services, that number runs higher because the consequences of exposure hit harder.
This guide covers seven alternatives with stronger privacy controls. Some run entirely on your infrastructure. Others offer managed services with data residency guarantees. One eliminates third-party API calls altogether.
Quick Comparison
1. Prem AI: Full Sovereign AI Stack
Prem AI is not a gateway. It is a complete platform for building, fine-tuning, and deploying custom AI models on your own infrastructure. Instead of routing requests to external APIs, you run the models yourself.
Privacy approach: This is the only option on this list that eliminates third-party API calls entirely. Your data never leaves your environment. Prem AI operates under Swiss jurisdiction (FADP) and offers cryptographic verification for every interaction. Zero data retention is not a policy you trust. It is architecture you control.
Key features:
- Fine-tune 30+ base models (Mistral, LLaMA, Qwen, Gemma) on your data
- One-click deployment to AWS VPC or on-premises infrastructure
- Autonomous fine-tuning system that handles dataset prep through production
- Sub-100ms inference latency with 99.98% uptime
- SOC 2, GDPR, and HIPAA compliant
Limitations: Requires more upfront investment than a simple API gateway. You are running infrastructure, not just routing requests. Teams that only need basic routing might find it more than they need.
Pricing: Usage-based through AWS Marketplace. Enterprise tier with custom support available. Contact sales for specifics.
Best for: Teams that want to stop relying on external AI providers entirely. If your compliance team will not approve sending data to third parties, this solves that problem at the architecture level.
2. LiteLLM: Open-Source Self-Hosted Proxy
LiteLLM is an open-source Python SDK and proxy server that gives you a unified interface to 100+ LLM providers. You can run it as a library in your app or deploy it as a standalone gateway.
Privacy approach: Fully self-hosted. Your API keys stay on your servers, and requests go directly from your infrastructure to providers. No telemetry unless you configure it. For air-gapped environments, this is often the only viable option.
Key features:
- OpenAI-compatible API for any provider
- Automatic fallbacks when providers fail
- Cost tracking and rate limiting
- Works with Ollama for fully local inference
- Active community with 20K+ GitHub stars
Limitations: Running it in production requires Redis for caching and PostgreSQL for logging. At scale (1M+ logs), the database can slow down API requests. Enterprise features like SSO and RBAC are behind a paywall. Cold start times of 3-4 seconds can hurt serverless deployments.
Pricing: Open source (MIT). Enterprise version with governance features requires contacting sales.
Best for: Platform teams comfortable managing infrastructure who want full control over their LLM routing layer. Pairs well with self-hosted fine-tuned models for complete privacy.
3. Portkey: Managed Gateway with Enterprise Controls
Portkey is a managed AI gateway with built-in guardrails, observability, and prompt management. It sits between your app and LLM providers, handling routing, retries, and monitoring.
Privacy approach: SOC 2, ISO 27001, HIPAA, and GDPR compliant. Enterprise tier offers private cloud deployment options. Requests still go through Portkey's infrastructure in the standard tier, so data residency depends on your plan.
Key features:
- 200+ LLM providers through one API
- Automatic fallbacks and load balancing
- Built-in guardrails for content filtering
- Real-time cost and latency monitoring
- Prompt versioning and management
Limitations: G2 reviewers note bugs and complexity for newcomers. Advanced analytics are limited. Pricing gets steep for smaller teams. Custom security controls and strict data residency require the enterprise tier.
Pricing: Free tier available. Pro and Enterprise tiers with custom pricing.
Best for: Teams that want a managed solution with strong compliance certifications and can budget for enterprise features.
4. Helicone: Observability-First Gateway
Helicone is an open-source LLM observability platform that also functions as an AI gateway. It started as a monitoring tool but now offers routing, caching, and fallbacks.
Privacy approach: Self-hosted option available via Docker or Helm. When self-hosted, your data stays in your infrastructure. The managed version is SOC 2 and GDPR compliant.
Key features:
- One-line integration (just change your base URL)
- Request/response logging with full traces
- Semantic caching to reduce costs
- Automatic provider fallbacks
- 10K free requests/month on managed tier
Limitations: The free tier burns through credits quickly during testing. Primarily an observability tool, so routing features are not as mature as dedicated gateways. Limited customization for some services.
Pricing: Free tier (10K requests/month). Pay-as-you-go after that.
Best for: Teams that prioritize debugging and monitoring. Good complement to other tools if you need deep LLM observability without building it yourself.
5. Unify: Smart Routing by Benchmark
Unify routes requests to the optimal LLM endpoint based on your constraints. It benchmarks providers in real-time and picks the best one for latency, cost, or quality per prompt.
Privacy approach: Cloud-only service. No self-hosted option. Your requests go through Unify's infrastructure to reach providers. They do not publish detailed data retention policies like some competitors.
Key features:
- Dynamic routing based on cost, latency, or quality
- Real-time benchmarks across providers
- Single API key for all models
- Automatic failover when providers go down
Limitations: No self-hosted option means no data sovereignty. Features are still in development. Learning curve for new users. Reviews mention occasional integration hiccups and slow customer support response times.
Pricing: Usage-based. Free tier available for testing.
Best for: Teams optimizing for cost and performance who do not have strict data residency requirements.
6. Kong AI Gateway: Enterprise API Management
Kong AI Gateway is an extension of Kong's popular API gateway, adding LLM-specific features like semantic caching, prompt guards, and PII sanitization.
Privacy approach: Fully self-hosted on your Kubernetes cluster. Your data never touches Kong's infrastructure. Integrates with existing enterprise security tools.
Key features:
- Semantic caching for LLM responses
- PII sanitization plugins
- Integration with all major LLM providers
- Built on battle-tested Kong Gateway (used by Netflix, Zillow)
- Prometheus and OpenTelemetry support
Limitations: Requires Kubernetes, Helm, and often Istio. That is heavy infrastructure for teams just wanting an AI gateway. Documentation is fragmented. Advanced plugins are enterprise-only. Pricing is complex and expensive at scale (over $30 per million requests vs. $1 on AWS).
Pricing: Open-source core. Enterprise features require Kong Konnect subscription.
Best for: Teams already running Kong Gateway who want to add AI routing to their existing API infrastructure.
7. Eden AI: Multi-Provider Aggregator
Eden AI aggregates 50+ AI providers through a single API. It covers more than just LLMs: OCR, speech-to-text, image recognition, and translation are all available.
Privacy approach: Cloud service with standard encryption. No self-hosted option. Your data goes through Eden AI's infrastructure to reach providers. They follow industry-standard security practices but do not offer the compliance certifications some enterprises need.
Key features:
- 50+ AI services through one API
- Real-time cost and latency comparison
- Pay-as-you-go pricing
- No-code workflow builder
Limitations: Varying response times depending on underlying provider. Free credits disappear quickly. Some providers only available on Enterprise tier. Reviews mention pricing transparency issues. No self-hosting means no data sovereignty.
Pricing: Free tier available. Pay-as-you-go starting at $29/month.
Best for: Teams that need multiple AI capabilities (not just LLMs) and are not blocked by data residency requirements.
How to Choose the Right Alternative
Start with one question: can your data leave your infrastructure?
If the answer is no, your options narrow fast. Prem AI is the only platform here that removes external API calls from the equation entirely. You fine-tune models on your data, deploy them to your AWS VPC or on-prem servers, and run inference without anything leaving your environment. For teams in regulated industries or those handling genuinely sensitive data, this is often the only path that clears legal review.
If you can use external APIs but need to control the routing layer, LiteLLM and Kong both let you self-host the gateway. LiteLLM is lighter and faster to set up. Kong makes sense if you already run Kubernetes and want AI routing alongside your existing API infrastructure.
If you want someone else to manage the infrastructure but still need compliance certifications, Portkey offers SOC 2 and HIPAA with enterprise deployment options. Helicone works well as a complement if your primary need is visibility into what your LLMs are actually doing.
If privacy is not the main constraint and you care more about reducing LLM API costs, Unify's benchmark-based routing picks cheaper providers when quality is equivalent. Eden AI works for teams that need OCR, speech, and vision alongside text generation.
The tools are not mutually exclusive. Some teams run LiteLLM as their routing layer, Helicone for observability, and Prem AI for fine-tuned models that handle their most sensitive workloads.
FAQ
Can I use multiple alternatives together?
Yes. Many teams layer these tools. A common setup: LiteLLM or Portkey handles routing to external providers for general workloads, while Prem AI runs fine-tuned models for anything involving customer PII, financial data, or proprietary information. Helicone sits on top for monitoring regardless of which backend serves the request.
What is the difference between a gateway and running my own models?
Gateways route requests to external providers. You still depend on OpenAI, Anthropic, or others. Running your own models means the computation happens on your hardware. No external calls, no data leaving your environment. The tradeoff is more infrastructure to manage, but for some use cases that tradeoff is mandatory.
Is self-hosting worth the complexity?
Depends on your constraints. If you are a startup without compliance requirements, managed services get you to production faster. If you are in a regulated industry or handle data that cannot touch third-party infrastructure, self-hosting might be the only option that passes security review.
How do I evaluate LLM reliability across these platforms?
Start with systematic evaluation practices before committing to any platform. Test with your actual prompts, measure latency under load, and check how each handles provider failures. The right choice depends on your specific workload patterns, not just feature lists.
Moving Forward
The gap between AI adoption and AI governance keeps widening. 96% of organizations plan to expand AI agent usage this year, but most have not solved the data privacy question. Waiting for regulators to clarify the rules is not a strategy.
If your current setup routes sensitive data through infrastructure you do not control, you have a decision to make. Gateways like LiteLLM and Portkey give you more visibility and control over routing. Observability tools like Helicone help you understand what is actually happening with your prompts.
But if the goal is to eliminate third-party data exposure entirely, the architecture has to change. Running your own models on your own infrastructure is the only approach that removes external dependencies from the equation.
Prem AI was built for exactly this use case. Fine-tune models on your proprietary data. Deploy to your AWS VPC or on-premises servers. Run inference with sub-100ms latency and zero data leaving your environment. SOC 2, GDPR, and HIPAA compliant out of the box.
Explore Prem AI or talk to the team about your specific requirements.