nexos.ai AI Gateway
nexos.ai is an AI orchestration platform that unifies multiple models for easy company use. AI Gateway enables developers to integrate once, while teams can access Claude, GPT-4, Gemini, and custom models without building separate connections.
The platform combines gateway functionality with enterprise-ready infrastructure:
- Gateway capabilities: AI Gateway routes traffic across providers with automatic failover. Organizations can deploy guardrails that block sensitive data leaks before they happen. Teams can track logs and traces at the request level, while admins monitor costs in real-time across departments, projects, and users.
- Built-in security: The platform ensures end-to-end encryption for data in transit and at rest. Role-based access controls (RBAC) let IT set policies once and enforce them automatically. Security architecture designed for regulated industries handles customer data. The architecture protects the connection from online attacks.
- Integrated features: There are several integrated features within the gateway. AI Gateway provides easy integration through a single API endpoint. Built-in RAG connects models to your internal knowledge base. LLM observability tracks latency, error rates, and token consumption. Built-in fallback logic ensures no service outages. AI guardrails enforce content policies and output validation. For example, human error is minimized when inputting a certain word or phrase that is forbidden.
- Deployment speed: The tech team can configure policies and access controls in hours, not weeks. 500+ employees start using approved models immediately through a unified workspace. Teams need no training and start chatting with AI the same way they use any messaging tool.
Integration works through API or web platforms. Companies can connect internal databases to provide context-aware responses and route requests based on cost, latency, or model capability. Moreover, users can switch providers without changing a single line of application code.
nexos.ai targets enterprises that need compliant, secure, cost-controlled AI deployment across technical and non-technical teams. AI Gateway is a great option for IT leaders who must balance innovation speed with security service requirements or companies processing sensitive data that can't risk exposure through third-party APIs.
unify.ai gateway
unify AI is a fully hackable LLMOps platform designed for developers building custom AI infrastructure. The platform provides modular building blocks for logging, image generation, evals, guardrails, tracing, and optimization that developers assemble into workflows.
The platform's core approach is that every AI product has unique requirements. Your infrastructure should reflect that uniqueness through maximum flexibility.
- Gateway capabilities: Developers can log LLM data with unify.log, build custom interfaces using four components: tables for data structure, views for filtering, plots for visualization, and an editor for manipulation. It’s also possible to create logging systems, human-grade evaluation frameworks, custom connection, and guardrail implementations tailored to a specific use case.
- Developer-first approach: unify.ai emphasizes modularity over pre-built solutions. The environments give you the components, and you resolve how to connect them. You can build custom agent workflows and review them iteratively.
- Customization depth: Every unify.ai interface is hackable. You can modify tables, views, and plots to capture the metrics that matter for your product. Developers can use the platform to integrate with existing MLOps pipelines and extend functionality through open APIs.
- Security and compliance: Documentation doesn't specify certifications or compliance standards. The platform's open architecture lets teams implement security measures that align with their requirements. Developers control data handling, storage, and access patterns to trigger control.
unify targets technical teams comfortable building infrastructure from components and startups with unique AI workflows that don't fit pre-configured platforms. Engineering organizations that prioritize customization over deployment speed will also find this platform useful.
nexos.ai vs. unify.ai
Both platforms approach gateway functionality from opposite directions. nexos.ai ships complete infrastructure on day one, observability tracks everything automatically, and security solutions meet standards out of the box, protecting against online attacks. unify.ai offers building blocks you assemble to build your own monitoring, implement specific guardrails, and design a security layer by site owner.
| Feature | nexos.ai | unify.ai |
|---|---|---|
| Gateway approach | Unified API to 200+ models with automatic failover and smart routing | Modular LLMOps platform for custom gateway implementations |
| Guardrails | Pre-built: PII detection, content filtering, output validation | Developer-built using platform components |
| Observability | Automatic logging, tracing, cost tracking, latency monitoring | Custom logging via unify.log with build-your-own dashboards |
| Security | End-to-end encryption, role-based access controls, self-hosted models, isolated LLM instances, AI guardrails | Developer-implemented security service using open architecture |
| RAG capabilities | Built-in: connect internal databases, context-aware responses | Custom RAG implementations |
| Deployment time | 48 hours organization-wide | Weeks to months: developers build infrastructure |
| Integration | API and web platforms | Modular APIs, custom connection, and integrations |
| Target audience | Enterprises needing compliant, rapid AI deployment | Technical teams building custom AI infrastructure |
| Pricing model | Usage-based | Free for personal use, $40 per seat per month for professional use, custom pricing for enterprises |
This table compares gateway-specific functionality. nexos.ai optimizes for deployment speed and compliance. unify.ai optimizes for architectural flexibility and developer control. Choose based on your constraint: time-to-deployment or customization requirements. nexos.ai accelerates go-live, while unify.ai accommodates unique workflows.
nexos.ai vs. unify.ai: Key functions and features
nexos.ai delivers production-ready gateway infrastructure that connects to 200+ models via a single API call. Automatic failover switches providers when primary models hit rate limits. Smart routing selects the optimal model based on cost, speed, or capability. Guardrails enforce data policies in real-time. Observability tracks every request with no configuration.
unify.ai delivers LLMOps building blocks to log data with a single function call. It allows developers to build custom tables to structure logged information, create views that filter data by project, user, or model. Users have to assemble components into evaluation frameworks, completing guardrail systems, or optimization pipelines on their own.
The functional difference: pre-built vs. build-your-own. nexoa.ai offers the complete stack, while unify.ai offers the building pieces. Both platforms handle logging, guardrails, and optimization.
nexos.ai vs. unify.ai: Security standards
nexos.ai key AI security standards include end-to-end data encryption, ensuring safe connection. Role-based access controls let IT administrators define who can access which models, set spending limits per team, and automatically enforce compliance policies. The architecture isolates customer data, and your prompts never train external models.
The documentation available on unify.ai’s website doesn't specify security certifications or pre-built compliance features. The platform's open architecture gives developers full control over security implementation. You decide on encryption standards, build access control systems, and design audit mechanisms.
In short, nexos.ai handles security for you, including online attacks. unify.ai lets you handle security solutions by the site owner or yourself.
nexos.ai vs. unify.ai: Gateway capabilities
Gateway capabilities determine how effectively you route traffic, enforce policies, and manage multiple providers.
nexos.ai operates as a unified proxy layer. Developers call one endpoint, and the gateway handles provider selection, request formatting, response parsing, and error handling. Automatic failover activates backup providers when primary models fail. Load balancing distributes traffic across instances, while caching reduces redundant API calls.
AI guardrails operate at the gateway level. They block prompts containing PII, filter outputs for profanity or bias, and validate responses against business rules.
Built-in RAG connects models to internal data sources. The gateway chunks content, generates embeddings, and automatically retrieves relevant context.
unify.ai provides the components to build custom gateway functionality. The unify.log function captures request data. Developers build tables to structure logged information, create views to analyze traffic patterns, and design plots to visualize performance metrics. The gateway helps implement routing logic, build caching layers, and design guardrail systems.
The platform doesn't provide pre-built gateway features. It provides the infrastructure to construct them. For teams with standard needs, this means building what others ship. For teams with unique requirements, this means building exactly what they need.
nexoa.ai vs. unify.ai: Monitoring and observability
LLM observability determines whether you understand what's happening inside your AI operations or operate blind.
nexos.ai tracks everything automatically. Every request generates a log entry containing prompt, response, model used, tokens consumed, latency, cost, and user identity. Dashboard views aggregate this data: total spending by department, error rates by model, average latency by endpoint, and token consumption trends over time.
unify.ai requires developers to build an observability infrastructure. The unify.log function captures data. Developers design tables to store it, create views to query it, and build plots to visualize it. Companies can implement the metrics that matter for their product and track what standard platforms ignore.
For organizations where standard metrics suffice, nexos.ai is a ready solution. For organizations where standard metrics miss crucial insights, unify.ai enables custom options.
nexos.ai vs. unify.ai: Integration and customization
Integration determines how easily AI capabilities can be added to existing systems. Customization determines how precisely those capabilities match requirements.
nexos.ai integrates through APIs and web platforms in minutes. Switching from GPT-4 to Claude requires changing one parameter, not rewriting integration code. Pre-built connectors link internal databases for RAG. You select which models teams can access, configure guardrail rules using the dashboard, and set routing preferences by cost or speed. No need to touch the application code.
unify.ai delivers maximum customization through modular components. Developers can build interfaces that match your exact workflow, and design optimization routines that target your performance metrics. The platform provides hooks into every system layer.
Integration requires more development work. The platform doesn't ship pre-built connectors or abstraction layers. Developers must implement the integration logic themselves. This enables perfect alignment with existing infrastructure. It also requires engineering resources to build and maintain.
Teams with standard integration needs deploy faster with nexos.ai, while teams with non-standard requirements achieve a better fit with unify.ai.
nexos.ai vs. unify.ai: Intended users
User profiles determine which platform delivers AI services faster.
nexos.ai targets:
- Enterprise IT leaders deploying AI to 100+ employees across technical and non-technical teams
- Companies in regulated industries requiring security compliance and audit trails
- Organizations that need production-ready infrastructure in days, not months
- Teams without dedicated MLOps engineers to build custom platforms
- CIOs balancing innovation speed with security service and cost control
unify.ai targets:
- Technical teams comfortable building infrastructure from components
- Developers with unique AI workflows that don't fit pre-configured platforms
- Startups where engineering resources optimize for perfect customization over rapid deployment
- Organizations with existing MLOps infrastructure that need flexible integration points
- Teams prioritizing architectural control over deployment speed
The platforms serve different stages and scales. nexos.ai accelerates enterprise adoption. unify.ai empowers custom implementations.
nexos.ai vs. unify.ai: Pricing
nexos.ai uses usage-based pricing. You pay for tokens consumed, models accessed, and users active. Enterprise plans include volume discounts, dedicated support, and custom SLAs. Transparent cost tracking shows exactly where money goes, and which teams, projects, and models drive spending.
unify.ai documentation doesn't publish standard pricing. Pricing likely reflects platform complexity and customization requirements. Teams building extensive custom infrastructure may face higher costs than those using standard features.
Key similarities between nexos.ai and unify.ai
Both platforms solve the same core problem: managing access to multiple LLM providers through centralized infrastructure. Shared capabilities include:
- Multi-model access: Connecting to models from OpenAI, Anthropic, Google, Meta, and other providers through a single integration point
- Logging and tracing: Capturing request and response data for analysis, debugging, and optimization
- Guardrail support: Implementing policies that filter inputs and outputs based on content, safety, or business rules
- AI-focused: Both platforms target teams needing AI-powered products
- API-based architecture: Accessing functionality programmatically through code rather than solely through graphical interfaces
- Workspace flexibility: Enabling teams to work with multiple models without managing separate accounts
The platforms differ in implementation, not capability categories. Both can route traffic, enforce policies, and track usage. The question isn't what they do, but how readily they do it and how much customization they allow.
Key differences between nexos.ai and unify.ai
The fundamental split: pre-built infrastructure vs. modular components.
- Deployment timeline: nexos.ai deploys in 48 hours with pre-configured guardrails, observability, and security. unify.ai requires weeks to months for developers to build equivalent infrastructure from components.
- Security: nexos.ai includes all the security and compliance a company needs. unify.ai requires teams to implement and certify their own security solution.
- Gateway architecture: nexos.ai operates as a unified proxy that automatically handles routing, failover, and caching. unify.ai provides building blocks to construct custom gateway logic.
- Guardrails: nexos.ai includes PII detection, content filtering, and output validation out of the box. unify.ai requires developers to build guardrail systems using platform components.
- Observability: nexos.ai tracks all metrics automatically. unify.ai requires developers to implement logging, aggregation, and visualization.
- Built-in features: nexos.ai includes RAG, file storage, and workspace access. unify.ai provides primitives to build these capabilities.
- Customization depth: nexos.ai customizes through configuration. unify.ai customizes through code and architecture.
- Target users: nexos.ai serves enterprises needing rapid, compliant deployment. unify.ai serves technical teams building their own AI infrastructure.
These differences reflect two approaches. nexos.ai optimizes for deployment speed and enterprise readiness. unify.ai optimizes for developer flexibility and architectural control.
nexos.ai vs. unify.ai: The conclusion
Gateway functionality determines how securely, efficiently, and reliably your organization accesses AI. nexos.ai ships production-ready infrastructure. unify.ai serves the tools to build it yourself. Both solve the multi-model management problem. The question is whether your company’s vision needs the solution tomorrow or can spend months crafting the perfect custom implementation.
Pick nexos.ai when deployment speed matters. If non-technical employees need secure AI tools without training, finance demands predictable cost controls, and IT must enforce data policies automatically, nexos.ai is a good choice. Your employees can access AI through one interface with role-based permissions.
Select unify.ai when your AI workflows don't fit standard platforms and your product requires custom evaluation metrics that competitors don't track. If your team has engineering resources to build infrastructure, and architectural flexibility matters more than deployment timeline. unify.ai is a great option when you need perfect alignment between platform capabilities and product requirements.
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