DEV Community: Pavel Horak

Build vs. Buy Your AI Infrastructure Platform: A Decision Framework

Pavel Horak — Tue, 14 Jul 2026 15:04:34 +0000

Organizations developing AI applications face a critical decision: should they build their AI infrastructure in-house or adopt a commercial platform? This article explores key factors to consider when choosing a strategic approach to AI infrastructure, featuring the benefits of a robust, open-source AI gateway like Bifrost.

The rapid adoption of large language models (LLMs) and generative AI has pushed AI infrastructure to the forefront of enterprise strategy. Teams must decide whether to allocate significant resources to develop and maintain their own AI infrastructure from scratch or integrate existing platforms. This build-or-buy decision carries profound implications for development velocity, cost, scalability, and long-term strategic flexibility.

The Build vs. Buy Dilemma for AI Infrastructure

The choice between building and buying an AI infrastructure platform is rarely straightforward. Each approach presents a unique set of advantages and disadvantages that warrant careful consideration.

What Does "Building" Entail?

Building an AI infrastructure platform involves developing core components such as model serving, prompt management, LLM routing, data pipelines, observability, and governance systems entirely in-house. This path offers maximum control and customization. Teams can tailor every aspect to their precise needs, integrating deeply with existing internal systems and intellectual property. The ability to control the entire stack can be appealing for organizations with highly specialized requirements or unique security postures.

However, the "build" approach comes with substantial overhead. It demands significant upfront investment in specialized engineering talent, including AI/ML engineers, DevOps specialists, and security architects. The ongoing costs associated with maintenance, updates, and feature development can quickly escalate. Many companies underestimate the time and resources required to achieve enterprise-grade reliability, performance, and security. A common challenge is diverting valuable engineering talent from core product development to infrastructure tasks, potentially slowing innovation.

What Does "Buying" Offer?

Opting for a commercial or open-source AI infrastructure platform can accelerate time to market, reduce operational burden, and provide access to specialized expertise. "Buying" can take various forms, from fully managed cloud AI services to integrating open-source components with commercial support.

Platforms like Bifrost, an open-source AI gateway from Maxim AI, exemplify the "buy" approach for a critical piece of AI infrastructure. It handles complexities such as multi-provider LLM routing, automatic failover, semantic caching, and unified API access, allowing teams to focus on building AI applications rather than managing the underlying connectivity. A well-chosen platform provides battle-tested reliability, performance, and security features that would be costly and time-consuming to replicate in-house.

The primary trade-off with buying often relates to customization limitations and potential vendor lock-in, although open-source options like Bifrost mitigate some of these concerns by offering transparency and extensibility.

Key Factors in Your Decision Framework

A structured decision framework can help organizations navigate the build vs. buy choice for their AI infrastructure platform.

Core Capabilities and Scope

Identify the essential capabilities your AI applications require, both now and in the foreseeable future. This includes considerations for model serving, LLM orchestration, data management, evaluation, and observability.

Breadth of models and providers: Does the platform need to support a wide array of LLMs from different providers? A gateway like Bifrost supports over 1000 models from more than 20 providers, ensuring flexibility and avoiding single-vendor dependence.
Agentic workflows: Is Model Context Protocol (MCP) support, including tool execution and agent modes, a necessity? An MCP gateway can significantly simplify integrating AI agents.
Evaluation and observability: For quality assurance, platforms that offer comprehensive AI agent evaluation, simulation, and observability are crucial. Tools like Maxim AI's platform provide these capabilities, enabling teams to measure and improve AI agent quality consistently.

Total Cost of Ownership (TCO)

Evaluate the long-term financial implications beyond initial setup costs. TCO for building includes:

Salaries for specialized engineers (hiring, retention)
Infrastructure costs (compute, storage, networking)
Software licensing and tooling
Maintenance, patching, and security updates
Opportunity cost of diverting engineering talent from core product innovation.

For buying, TCO includes:

Subscription fees or licensing costs
Cloud consumption costs (if applicable)
Costs for integration and customization
Training for platform users.

A Gartner report on AI infrastructure often highlights how hidden costs associated with maintenance and custom integrations can inflate the TCO for self-built solutions.

Time to Market and Agility

The urgency of deploying AI applications can heavily influence the decision.

Buy: Accelerates deployment by providing immediate access to a functional, pre-built infrastructure. This is crucial for rapid prototyping and gaining competitive advantage.
Build: Incurs significant delays due to design, development, testing, and hardening phases. This path is generally only viable for organizations with no immediate time pressure and a long-term strategic vision for a truly differentiating custom stack.

Talent and Expertise

Assess your organization's existing talent pool and its capacity to acquire new skills.

Building requires deep expertise across various domains: distributed systems, cloud native architectures, AI/ML engineering, and cybersecurity. A lack of specific skills can lead to project delays, subpar solutions, and increased technical debt.
Buying allows teams to leverage the vendor's expertise. The focus shifts from building foundational infrastructure to understanding and integrating the platform effectively, freeing internal engineers to work on domain-specific AI applications.

Customization and Vendor Lock-in

The degree of customization required is a vital factor.

Building offers complete control, enabling bespoke solutions perfectly aligned with unique business processes or highly specific performance requirements. However, this often comes at the expense of maintainability and upgradability.
Buying might involve some degree of vendor lock-in, although this can be mitigated by choosing platforms with open standards, extensive APIs, and modular architectures. Open-source solutions like Bifrost offer a balance, providing a robust base while allowing for custom plugins and extensions to address specific needs. The plugin architecture in Bifrost allows teams to extend its functionality with custom business logic.

Security and Governance

AI applications, especially those handling sensitive data, necessitate robust security and governance frameworks.

Building a secure and compliant AI infrastructure requires significant investment in security engineering, audit trails, data access controls, and guardrails. This must meet regulatory standards like SOC 2, GDPR, HIPAA, and ISO 27001.
Buying a platform from a reputable vendor means inheriting battle-tested security features, often with certifications and compliance readiness built in. Bifrost, for example, provides comprehensive governance features like virtual keys, budgets, rate limits, and fine-grained access control. Critically, Bifrost extends its governance and security controls (virtual keys, budgets, guardrails, audit logs) centrally, and Bifrost Edge extends that same governance and security to AI traffic on employee machines, with endpoint enforcement on each device.

Bifrost: A Hybrid Approach to AI Infrastructure

For many organizations, a pure build or pure buy strategy may not be optimal. A hybrid approach, integrating robust, open-source components with in-house customization, often strikes the right balance. Bifrost serves as an exemplary component for this strategy.

As an open-source AI gateway, Bifrost offers the stability and performance of a "bought" solution (with enterprise support available) while providing the flexibility and transparency of an "built" component.

Performance and reliability: Bifrost adds only 11 microseconds of overhead per request at 5,000 requests per second, ensuring minimal impact on application latency. It features automatic failover and intelligent load balancing across providers, maintaining high availability for mission-critical applications.
Unified access and control: It provides a unified API for all LLM providers, simplifying integration and allowing developers to switch models or providers without code changes. Its MCP gateway capabilities enable advanced agentic workflows, including code mode for token reduction.
Enterprise readiness: For large organizations, Bifrost Enterprise offers advanced features like clustering for high availability, role-based access control, and advanced guardrails for content safety and data privacy. Its ability to deploy in-VPC or air-gapped meets strict security and compliance requirements.

Navigating the Decision

The decision to build or buy an AI infrastructure platform depends on an organization's specific context, strategic goals, and resource availability. Teams should start by thoroughly assessing their current and future needs, conducting a detailed TCO analysis, and honestly evaluating their internal capabilities. For many, a pragmatic path involves adopting best-in-class, open-source components like Bifrost that deliver core infrastructure capabilities while reserving internal engineering efforts for truly differentiating business logic and proprietary AI applications.

Teams evaluating AI gateways can request a Bifrost demo or review the open-source repository.

Sources

The New Stack. "Build vs. Buy for Cloud Native Infrastructure". https://thenewstack.io/build-vs-buy-for-cloud-native-infrastructure/
Maxim AI. "Bifrost Supported Providers". https://docs.getbifrost.ai/providers/supported-providers/overview
IBM. "Build vs. Buy: The Decision Point". https://www.ibm.com/downloads/cas/M3Y94GZL
Gartner. "Predicts 2024: AI Infrastructure and Software Engineering". https://www.gartner.com/en/articles/predicts-2024-ai-infrastructure-and-software-engineering
Bifrost Docs. "Security at Bifrost". https://docs.getbifrost.ai/security

Best AI Gateways for Groq, Together, and Fireworks

Pavel Horak — Thu, 09 Jul 2026 09:54:26 +0000

The increasing demand for low-latency AI inference from providers like Groq, Together, and Fireworks highlights the need for robust AI gateways. This post compares leading options, evaluating their performance, reliability, and governance capabilities to help teams choose the optimal solution for high-speed LLM deployments. Bifrost stands out as the top pick for enterprises requiring best-in-class performance, scalability, and robust governance for mission-critical AI workloads.

The rapid evolution of large language models (LLMs) has led to a proliferation of specialized inference providers, each offering unique advantages in terms of speed, cost, and model access. Services like Groq, Together AI, and Fireworks AI have emerged as leaders in delivering high-performance, low-latency LLM inference, making them attractive choices for applications where real-time responsiveness is paramount. However, integrating multiple such providers, managing their APIs, ensuring reliability, and maintaining consistent governance across an AI application's lifecycle presents significant challenges. This is where a dedicated AI gateway becomes indispensable.

The Rise of Specialized LLM Providers

Providers like Groq, Together AI, and Fireworks AI are redefining what's possible in LLM inference by optimizing for speed and efficiency. Groq, for instance, leverages its Language Processing Unit (LPU) inference engine to deliver exceptionally fast inference, making it ideal for latency-sensitive applications. Together AI offers a platform for fine-tuning and running open-source models at scale, emphasizing performance and cost-effectiveness across a diverse model catalog. Fireworks AI focuses on ultra-low-latency inference for a range of models, including specialized small language models (SLMs), catering to developers building high-speed generative AI applications.

These providers excel by offering access to cutting-edge models with optimized hardware or software stacks, enabling developers to build more responsive and powerful AI products. Their benefits include:

Reduced Latency: Crucial for interactive AI experiences like chatbots, real-time content generation, and coding assistants.
Cost-Effectiveness: Often providing better performance-to-cost ratios for specific models or workloads.
Model Diversity: Access to a wide array of specialized open-source and proprietary models.

Why an AI Gateway is Crucial for High-Performance LLMs

While specialized LLM providers offer clear advantages, relying solely on their direct APIs can introduce complexities:

API Inconsistencies: Each provider may have slight variations in their API, necessitating custom code for each integration.
Reliability: Single points of failure, rate limits, and service outages from any one provider can disrupt applications.
Cost Management: Tracking and optimizing spend across multiple providers can be challenging without centralized control.
Governance and Security: Enforcing access controls, budgets, data policies, and guardrails becomes complex at scale.

An AI gateway centralizes the management of LLM traffic, abstracting away provider-specific complexities and introducing critical infrastructure capabilities. It acts as a single, unified entry point for all LLM requests, providing features like intelligent routing, automatic failover, load balancing, and comprehensive governance, all while maintaining the low latency required for high-performance models.

Key Criteria for Evaluating AI Gateways for Specialized LLM Providers

When selecting an AI gateway, especially for high-speed providers like Groq, Together, and Fireworks, several criteria are important:

Performance Overhead: The gateway itself must introduce minimal latency to preserve the speed benefits of specialized LLMs.
Provider Compatibility: Broad support for current and future LLM providers, including a unified API across them.
Reliability Features: Automatic failover, intelligent load balancing, and rate limiting to ensure continuous operation.
Cost Optimization: Semantic caching, dynamic routing, and virtual keys for budget management.
Governance and Security: Robust access control (RBAC), data access control (DAC), guardrails, and audit logging.
Deployment Flexibility: Options for cloud, on-premise, or VPC deployment, with enterprise-grade features for clustering and high availability.
Observability: Integrated monitoring, logging, and tracing capabilities for debugging and performance analysis.

1. Bifrost: The High-Performance AI Gateway for Specialized LLMs

Bifrost, an open-source AI gateway from Maxim AI, is engineered for mission-critical AI workloads, offering minimal overhead and extensive capabilities for managing diverse LLM ecosystems. It provides a single OpenAI-compatible API that simplifies integration with over 1000 models, including direct support for Groq, Together AI, and Fireworks AI. This allows teams to leverage the speed of these providers without being locked into a single vendor's API.

Key advantages of Bifrost for specialized LLMs include:

Ultra-Low Latency: Bifrost is benchmarked at just 11 microseconds of overhead per request at 5,000 requests per second, preserving the speed benefits of providers like Groq.
Unified API: A drop-in replacement for existing OpenAI SDKs, it enables seamless switching or routing between Groq, Together, Fireworks, and other providers by simply changing the base URL.
Intelligent Routing and Failover: Teams can configure automatic failover to route requests around provider outages or rate limits, ensuring uninterrupted service. Advanced routing rules can direct traffic based on model performance, cost, or specific virtual keys.
Comprehensive Governance: Virtual keys enable fine-grained access control, budget allocation, and rate limits across models, providers, and users. This is critical for managing costs and preventing abuse when using diverse providers.
Semantic Caching: Bifrost's semantic caching reduces costs and latency by reusing responses for semantically similar queries, significantly offloading traffic from expensive high-performance models.
Endpoint AI Governance with Bifrost Edge: Beyond gateway-level controls, Bifrost applies governance and security controls (virtual keys, budgets, guardrails, audit logs) centrally. Bifrost Edge extends that same governance and security to AI traffic on employee machines, with endpoint enforcement on each device. This ensures shadow AI and ungoverned usage of desktop apps or coding agents are brought under corporate policy. Edge is currently in alpha and available to early access partners.
Enterprise-Grade Capabilities: For large organizations, Bifrost Enterprise offers clustering for high availability, adaptive load balancing, and integrations with identity providers for role-based access control (RBAC).

2. LiteLLM: Unified API for Many Models

LiteLLM is an open-source library that provides a unified API for interacting with over 100 LLMs from various providers. It simplifies the process of making requests to different models by offering a consistent interface, often seen as a lightweight proxy.

LiteLLM's strengths lie in its ease of use for developers looking to quickly switch between models or add new providers without modifying extensive code. It supports features like fallbacks and retries, which contribute to application reliability. However, as a library, its scope for advanced enterprise-grade governance, observability, or sophisticated deployment architectures often requires additional tooling and custom development compared to a dedicated gateway solution like Bifrost. It integrates with frameworks like LangChain and LlamaIndex to extend its capabilities.

3. OpenRouter: Aggregating Diverse LLMs

OpenRouter acts as a universal API for a wide range of LLMs, providing access to many models through a single endpoint. It is designed to make experimenting with and deploying different models simpler, often at competitive prices.

OpenRouter aggregates models from various providers, including popular options and many specialized open-source models. It focuses on providing a convenient way to access models, sometimes offering cost savings through aggregated pricing or novel routing mechanisms. While it offers a unified interface, its primary focus is on model access and aggregation rather than deep enterprise governance, fine-grained control over deployment, or extensibility through custom plugins, which are key features of self-hosted gateway solutions.

How the Options Compare on Key Features for Groq, Together, and Fireworks

Feature	Bifrost	LiteLLM	OpenRouter
Provider Support	1000+ models, including native Groq, Together, Fireworks	100+ models, including Groq, Together, Fireworks (as library)	Many models from various providers, including Groq, Together, Fireworks (as service)
Performance Overhead	Ultra-low (11µs at 5,000 RPS)	Minimal (as a Python library, depends on environment)	Depends on OpenRouter's hosted service latency
Unified API	OpenAI-compatible, drop-in replacement	OpenAI-compatible (via `litellm.completion()`)	OpenAI-compatible
Automatic Failover	Yes, intelligent and configurable	Yes, configurable	Limited to what the hosted service provides
Load Balancing	Yes, intelligent and adaptive	Yes, via API key management	Limited to what the hosted service provides
Semantic Caching	Yes, native	No, requires external integration	No
Governance (Virtual Keys, Budgets)	Yes, comprehensive at gateway and endpoint (with Bifrost Edge)	No, requires custom implementation	Limited, primarily through API key usage
Guardrails	Yes, native (Secrets Detection, Custom Regex), AWS Bedrock, Azure Content Safety, third-party integrations	No, requires external integration	No
Deployment Model	Self-hosted (on-prem, VPC, cloud), open-source core	Library (self-hosted, requires custom deployment)	Hosted service
Enterprise Features	Clustering, RBAC, DAC, OIDC, audit logs, custom plugins	Requires extensive custom development	Not applicable for self-managed enterprise features
Endpoint Governance	Yes, via Bifrost Edge	No	No

Recommendation / Next Steps

For engineering teams prioritizing the speed benefits of specialized LLM providers like Groq, Together, and Fireworks, while also demanding robust reliability, sophisticated governance, and full control over their AI infrastructure, Bifrost presents a compelling solution. Its architectural design ensures minimal latency overhead, making it uniquely suited to maintain the performance edge of these fast models. When combined with its comprehensive enterprise-grade features and the innovative endpoint governance offered by Bifrost Edge, it provides an all-in-one platform for securing and scaling AI applications. Teams evaluating AI gateways can request a Bifrost demo or review the open-source repository to see how it can meet their specific needs.

Sources

Groq. (n.d.). The Groq LPU™ Inference Engine. https://groq.com/lpu/
Together AI. (n.d.). Together AI: Build with the best open models. https://www.together.ai/
Fireworks AI. (n.d.). Fireworks AI: Ultra-fast LLM inference. https://fireworks.ai/
Bifrost Docs. (n.d.). Supported Providers. https://docs.getbifrost.ai/providers/supported-providers/overview
Bifrost Docs. (n.d.). Drop-in Replacement. https://docs.getbifrost.ai/features/drop-in-replacement

Centralizing Access: A Guide to SSO and OIDC for AI Gateways

Pavel Horak — Thu, 02 Jul 2026 17:20:12 +0000

Integrating Single Sign-On (SSO) with an AI gateway using OpenID Connect (OIDC) is critical for enterprise security and compliance. An open-source AI gateway like Bifrost centralizes user authentication, simplifies access management, and enables detailed audit trails for production AI workloads.

As engineering teams scale their use of large language models, managing developer access to different AI providers becomes a significant security and operational challenge. Each developer managing their own set of API keys for OpenAI, Anthropic, and Google Gemini creates security blind spots, complicates access control, and makes auditing impossible. AI gateways help solve this by centralizing requests, but securing the gateway itself is the next critical step. This is where Single Sign-On (SSO) and OpenID Connect (OIDC) become essential. An open-source AI gateway like Bifrost, from Maxim AI, can integrate with enterprise identity providers to enforce centralized, auditable access policies for all AI development.

What Are SSO and OIDC?

Single Sign-On (SSO) is an authentication method that allows users to log in with a single set of credentials to multiple independent software systems. Instead of maintaining separate passwords for every application, a user authenticates once against a central identity provider (IdP).

OpenID Connect (OIDC) is the protocol that makes this possible for modern applications. It is a simple identity layer built on top of the OAuth 2.0 protocol. While OAuth 2.0 is designed for authorization (granting permission for an application to access resources on behalf of a user), OIDC is designed for authentication (verifying a user's identity). When a user signs into an OIDC-enabled application, the application receives an ID Token, which is a secure JSON Web Token (JWT) containing information about the authenticated user.

Why SSO is Critical for Enterprise AI Gateway Security

For enterprises, integrating an AI gateway with an existing SSO provider is not just a convenience, it is a core security requirement. It addresses several critical challenges in managing AI infrastructure.

Centralized User Management: When a developer joins or leaves the company, their access to the AI gateway is automatically managed through the central IdP. There is no need to manually issue or revoke gateway-specific credentials, which eliminates the risk of orphaned accounts and unauthorized access.
Consistent Authentication Policies: Enterprises can enforce consistent security policies, such as multi-factor authentication (MFA), password complexity, and session timeouts, for AI gateway access. These policies are managed in one place—the IdP—and inherited by all connected applications.
Simplified Auditing and Compliance: SSO provides a centralized audit trail of all authentication events. For organizations that must comply with standards like SOC 2, HIPAA, or ISO 27001, the ability to demonstrate who accessed the AI gateway and when is essential.
Improved Developer Experience: Developers can access the AI gateway using the same credentials they use for all other company applications. This removes the friction of managing another set of keys and streamlines the development workflow.

How OIDC Integration Works with an AI Gateway

Integrating an AI gateway with an IdP via OIDC follows a standard authentication flow. This process ensures that every request to the gateway is securely associated with a verified user identity.

Initial Request: A developer's application or CLI tool makes a request to the AI gateway. If the request lacks a valid session or token, the gateway initiates the OIDC flow.
Redirect to Identity Provider: The AI gateway redirects the user to the organization's IdP (e.g., Okta, Microsoft Entra ID, Keycloak) to authenticate.
User Authentication: The user enters their standard corporate credentials and completes any required MFA challenges.
Token Issuance: Upon successful authentication, the IdP generates a signed ID Token and an access token and sends them back to the AI gateway via the user's browser.
Token Validation: The gateway validates the signature and claims of the ID Token to confirm the user's identity. It can then use the information within the token, such as user ID, group memberships, and email, to make authorization decisions.
Session Creation: The gateway establishes a secure session for the user, allowing subsequent API requests to be processed without re-authentication until the session expires.

This token-based architecture allows for stateless authentication, making it highly scalable and suitable for distributed, cloud-native environments where AI gateways are often deployed.

Implementing SSO with the Bifrost AI Gateway

An AI gateway like Bifrost is designed for enterprise environments and offers direct integration with OIDC-compliant identity providers. The platform provides documented guides for connecting to major IdPs, including Okta, Microsoft Entra ID, and Keycloak.

The configuration involves setting up Bifrost as a client application within the IdP and configuring the gateway with the provider's discovery URL and client credentials. Once connected, Bifrost can automatically provision users upon their first login and sync their team or group memberships.

This user provisioning capability is what connects authentication to authorization. A developer's group memberships from the IdP can be used to assign them specific roles and permissions within the AI gateway.

For instance, a developer in the "AI-Research" group in Okta could automatically be granted access to high-cost experimental models, while a developer in the "App-Dev" group might be restricted to production-approved, cost-effective models.

Benefits Beyond Authentication

Integrating SSO with an AI gateway unlocks a suite of advanced governance and security features that are critical for managing AI at scale.

Role-Based Access Control (RBAC): By mapping IdP groups to roles, organizations can implement fine-grained role-based access control (RBAC). This ensures that developers only have access to the AI models, providers, and tools necessary for their specific roles, enforcing the principle of least privilege.
Detailed Audit Logs: With every request tied to a specific SSO identity, the AI gateway can produce comprehensive audit logs. These logs are essential for security investigations, cost attribution, and demonstrating compliance with internal and external regulations.
Per-User Budgets and Rate Limits: Gateways can apply spending limits and rate limits on a per-user or per-team basis. This is only possible when the gateway has a reliable sense of user identity, which SSO provides.
Endpoint Governance: Centralized identity is also a prerequisite for extending governance to the endpoint. Tools like Bifrost Edge use the same SSO sign-in on an employee's machine to link their desktop AI usage (apps, CLIs) to their corporate identity, ensuring all AI traffic is routed through the gateway and subject to its security and governance policies.

As AI becomes more integrated into core business operations, treating AI infrastructure with the same security rigor as any other production system is non-negotiable. For any organization operating an AI gateway, SSO integration is the foundational step toward achieving secure, scalable, and compliant AI development. Teams evaluating AI gateways can request a Bifrost demo or review the open-source repository to see how these integrations work.