Enterprise teams evaluating AI gateway solutions face a critical challenge: published pricing rarely reflects actual operational costs. Kong's Konnect Plus starts at $25/month for each serverless gateway control plane, with separate model, request, analytics, bandwidth, and add-on charges that can materially increase total cost. Understanding this pricing structure is essential for teams building AI agent infrastructure that requires governed access to enterprise tools, LLM routing, and comprehensive audit trails.

This article breaks down Kong AI Gateway's published Konnect Plus rate card for 2026, including gateway, per-model, request, analytics, and bandwidth charges, plus add-ons whose Plus pricing is not publicly listed. We also examine alternative approaches for teams prioritizing MCP governance and agent identity management.

Key Takeaways

• Konnect Plus's $25/month serverless gateway control plane price is only one component: three full-month AI model proxies add $300, and request overages are billed separately
• The 5-model cap on Konnect Plus forces early Enterprise tier negotiations when teams experiment with additional providers
• LLM provider costs from OpenAI, Anthropic, and others remain separate from all gateway platform fees and can dominate total spending depending on model choice and usage patterns
• Enterprise AI governance requires more than API routing: agent identities, scoped permissions, credential management, and comprehensive audit trails are increasingly critical
• Organizations need to evaluate total cost of ownership including infrastructure complexity, custom plugin development, and operational overhead beyond subscription fees
• Kong first shipped its AI MCP Proxy in Kong Gateway 3.12, while AI Gateway 2.0 later introduced a dedicated AI control plane with first-class MCP Server entities

Understanding the AI Gateway Landscape and Kong's Position

The AI gateway market has evolved rapidly as enterprises deploy AI agents across coding assistants, customer-facing applications, and internal automation. Kong AI Gateway occupies a distinct position: it extends an established API management platform with AI-specific capabilities rather than building from AI-first principles.

Kong has maintained its Gartner Leader status for API management for seven consecutive years through 2026, processing over 1 trillion API and AI requests daily. This scale demonstrates proven reliability for organizations already invested in Kong's ecosystem. Forrester's Q3 2026 evaluation noted that Kong's gateway has more policies for LLM governance than any other vendor in its API Management Software Wave assessment.

However, the AI gateway landscape now extends beyond API management into Model Context Protocol (MCP) governance, autonomous agent identity management, and real-time security screening. Teams must evaluate whether extending an existing API gateway or adopting purpose-built AI governance infrastructure better serves their deployment requirements. Key considerations include:

• API gateway extensions work well for organizations with existing platform investments seeking incremental AI capabilities
• MCP-native solutions address teams prioritizing governed tool access, Virtual MCPs, and SCIM-driven access control
• Agent identity requirements differ significantly from traditional API consumer models when deploying autonomous agents at scale

Deconstructing Kong AI Gateway Pricing: Konnect Plus and Usage Fees

Kong Konnect Plus represents the primary commercial tier for teams requiring AI gateway capabilities without full Enterprise commitments. Understanding its component pricing reveals the actual cost structure.

Control Plane Options

• Serverless control plane: $25/month, up to 5 serverless AI Gateway control planes
• Hybrid control plane: $200/month, up to 2 hybrid control planes
• Dedicated Cloud control plane: $500/month, single region deployment

AI-Specific Charges

• AI model proxy fee: $100/model per month, maximum 5 models on Plus tier
• Included API requests: 1 million per control plane
• Additional requests: $200 per million, capped at 10 million/month on Plus
• Dedicated Cloud bandwidth: $0.15/GB for data transfer charges

Additional Platform Fees

• Advanced Analytics: $20 per million requests beyond included 1M, max 10M/month
• Extra Developer Portals: $200 per portal, maximum 2 portals on Plus
• Published API overages: $20/month per additional API up to 10 total APIs, then $10/month per additional API beyond 10

The 5-model cap on Plus creates a significant constraint: teams experimenting with multiple LLM providers (OpenAI, Anthropic, Azure AI, Bedrock, Gemini) quickly exhaust this limit. Testing a sixth model requires upgrading to Enterprise tier, forcing premature contract negotiations.

The True Cost: What Teams Actually Pay for AI Gateway Solutions

Published pricing tells only part of the story. Real deployments accumulate costs across multiple dimensions that compound quickly. A serverless deployment with 3 models and 3 million requests per month incurs $25 for the control plane, $300 for model fees, and $400 for request overages, totaling $725 monthly in Kong fees before LLM provider charges.

Organizations with higher requirements face steeper costs. Dedicated Cloud deployments add $500 for the control plane, while bandwidth charges scale with data transfer volume. Request overages continue at $200 per million through the 10-million-request Plus cap.

Enterprise Scale Considerations

Kong Enterprise uses custom pricing billed annually. Pricing depends on usage, deployment, regions, support, and negotiated terms. Customer-hosted data planes can add infrastructure and operations costs beyond the software subscription.

Hidden Cost Categories

Infrastructure complexity: Self-managed deployments can add compute, networking, monitoring, upgrade, and high-availability work. In Konnect hybrid mode, Kong manages the control plane database, while customer-managed data planes can run on Docker, Linux, or Kubernetes, so PostgreSQL and Kubernetes are not universal requirements.

Plugin development: Kong supports custom plugins through Lua, Go, Python, and JavaScript PDKs, although deployment compatibility varies by topology and Konnect mode. Bespoke plugins still add development and maintenance cost, but Lua is not the only supported option.

LLM provider charges: Model provider inference charges are separate from Kong platform fees and can be larger or smaller depending on model choice, token volume, caching, and traffic patterns. Model provider spend should be budgeted separately rather than assumed to follow a fixed ratio to gateway fees.

Support tier requirements: Konnect Plus includes email support with a 2-business-day SLA. Organizations requiring faster response times or dedicated support need Enterprise support options, whose pricing is custom.

For teams focused on understanding and managing these costs, AI agent cost tracking capabilities become essential for operational visibility and budget governance.

Comparing AI Gateway Deployment Options

The managed AI gateway market spans multiple architectures, from API gateway extensions to AI-native platforms to MCP-first governance solutions. Teams should evaluate whether their primary need is LLM traffic routing, MCP governance with tool curation, or autonomous agent identity management.

Evaluation Dimensions

When comparing gateway options, verify the exact controls needed rather than assume a shared feature profile:

• Model coverage: Provider and model support, load balancing, fallbacks, budgets, and rate controls
• MCP governance: Remote MCP proxying, API-to-MCP conversion, aggregation, tool-level policy, and authentication
• Agent identity: Whether agents are first-class principals or use application, consumer, or service credentials
• Directory integration: SSO, SCIM, directory-group support, and tier requirements
• Connector model: Vendor-hosted connectors, remote servers, built-in connectors, and self-hosted options

Deployment Models

Managed SaaS-first solutions minimize infrastructure burden by handling hosting, scaling, and maintenance. Teams connect through authenticated endpoints without operating gateway runtime. This approach suits organizations prioritizing speed to deployment over infrastructure control.

Hybrid architectures combine managed control planes with self-hosted data planes. Kong Konnect exemplifies this model: the control plane runs in Kong's infrastructure while data planes can deploy in customer environments for data residency or latency requirements.

Open source self-hosted options provide maximum control but transfer all operational responsibility to customer teams. Depending on the gateway and deployment, teams may need to operate databases, compute, monitoring, and upgrades. The gateway runtime can remain in customer infrastructure, but request data may still leave when routed to external providers.

Cloud Platform AI Gateway Options

Cloud providers and infrastructure vendors have introduced AI gateway capabilities that integrate with their broader platforms.

Cloudflare AI Gateway

Cloudflare AI Gateway is available on all plans, with core features such as analytics, caching, and rate limiting offered for free. Cloudflare also offers generally available MCP server portals that aggregate approved MCP servers behind one endpoint, with Access logging, Gateway-based DLP scanning, service-token authentication for autonomous agents, session management, and Logpush export.

This model suits teams already invested in Cloudflare's ecosystem and now covers both model traffic and governed MCP access.

Databricks Unity Gateway

Databricks now positions Unity Gateway as its control plane for enterprise AI, built on Unity Catalog. It governs model services, external model providers, MCP servers, agents, skills, and tools with centralized access controls, cost controls, guardrails, and observability.

Teams already standardized on Databricks and Unity Catalog may benefit from keeping model and MCP governance inside the same platform.

Azure API Management AI Gateway

Azure API Management supports AI gateway capabilities across Microsoft and external model providers, along with remote MCP servers, API-to-MCP exposure, and centralized policies. Microsoft also has a dedicated AI Gateway tier in public preview for governed model and MCP tool access, currently limited to East US 2 and Sweden Central, with pricing not yet announced.

For Azure-centric teams this can reduce platform sprawl, but buyers should verify region availability, service-tier requirements, and the current preview limitations for the capabilities they need.

Open Source Gateway Options

Open source AI gateways attract teams prioritizing control, avoiding vendor lock-in, or operating under budget constraints. LiteLLM is an open source AI gateway with a self-hosted proxy, virtual keys, budgets, and broad model-provider coverage.

LiteLLM supports 100+ LLM providers and self-hosted deployment. It includes an MCP Gateway with key, team, and organization access controls. Enterprise adds features including SCIM, audit logs, delegated administration, and broader RBAC; SSO is free for up to five users before an Enterprise license is required.

While open source eliminates subscription fees for core capabilities, infrastructure and operational costs can exceed managed service pricing. Software licensing costs are often $0 for the open-source core, though enterprise add-ons vary. Infrastructure remains customer-funded and deployment-dependent, while managed SaaS often bundles these costs. DevOps staffing is customer-operated for self-hosted options versus lower runtime burden for managed services. Security patching is the customer's responsibility for self-hosted deployments versus provider-managed for hosted components.

Key Features Influencing AI Gateway Pricing

AI gateway pricing reflects the complexity of features included at each tier. Understanding which capabilities drive costs helps teams select appropriate solutions.

Access Control and Authentication

Basic API key authentication comes standard in most gateways. Enterprise features that increase pricing include SSO integration with Okta, Azure AD, or Google Workspace, SCIM provisioning for directory synchronization, granular RBAC at the tool level rather than gateway level, and first-class agent identity support for autonomous agents.

Mintive's agent identities provide each autonomous agent with independent credentials, scoped permissions, and audit trails separate from human users. This capability enables credential rotation and revocation without affecting other agents or users.

Audit Logging and Compliance

Compliance requirements drive significant pricing differentiation. Basic logging of request and response metadata typically comes included, while full audit trails capturing arguments, results, and guardrail decisions may require higher tiers. SIEM export to Splunk, Sentinel, or OTLP streaming is often Enterprise-only, as are tamper-evident records with signed, verifiable audit history for regulatory requirements.

Runtime Security

Security screening adds both capability and cost. Prompt injection detection with managed detection policies, PII and secrets detection, custom rules with declarative pattern matching, and DLP integration with AWS Bedrock Guardrails, GCP DLP, or Microsoft Purview are increasingly expected capabilities.

Mintive's Mint Guard provides managed detection for prompt injection, credentials, PII, and harmful content with enforcement or monitoring modes. Gateway middleware enables custom JavaScript logic for organization-specific security requirements.

Cost Tracking and Observability

Visibility into AI spend has become critical as deployments scale. Essential capabilities include token tracking by model, user, session, and agent, cost attribution for chargeback-ready reporting by team or project, anomaly detection for runaway agents, and budget enforcement with hard caps and alerts.

Agent Monitor captures activity across supported AI clients including prompts, commands, file access, and MCP tool calls, enabling comprehensive visibility beyond gateway traffic alone.

Optimizing Your Investment: Cost Management Strategies

Managing AI gateway costs requires attention to both platform expenses and underlying LLM provider charges.

Model Selection and Routing

Not every request requires the most capable model. Task-appropriate routing uses smaller models for summarization, classification, or simple queries. Fallback strategies start with cost-effective models and escalate only when needed. Provider diversity enables cost optimization and resilience.

Per-team model restrictions and budgets prevent individual teams from selecting expensive models inappropriately. Gateway-level controls enforce these policies without requiring application changes.

Caching and Request Optimization

Reducing redundant LLM calls directly impacts costs. Semantic caching returns cached responses for semantically similar queries, while prompt deduplication identifies and consolidates repeated requests. Context optimization trims unnecessary context to reduce token consumption.

Organizations should verify whether their gateway supports caching and understand cache hit rates to estimate savings. LLM cost optimization strategies extend beyond gateway selection to encompass the full agent lifecycle from development through production monitoring.

Governance and Policy Controls

Strong governance reduces waste and risk. Tool curation limits available tools to reduce context window bloat and unnecessary calls. Budget hard caps prevent runaway costs with enforced spending limits. Approval workflows require review for high-cost or high-risk tool access.

The Agentic Enterprise: How AI Gateways Empower Scaled AI Deployments

Enterprises are deploying AI agents faster than they can govern them. As adoption scales, organizations need infrastructure that keeps agents deployable, governed, measurable, and swappable across models and providers.

Moving from pilots to enterprise deployments introduces several challenges:

• Credential sprawl: API keys scattered across developer laptops increase security risks.
• Audit gaps: Limited visibility makes it difficult to attribute actions to specific agents.
• Access control: Agents may access connected systems without appropriate oversight.
• Configuration drift: Individually managed MCP servers create inconsistent configurations and permissions.

Addressing these challenges requires treating autonomous agents as first-class identities rather than relying on shared human credentials.

Virtual MCPs provide centralized governance through:

• Role-based tool bundles: Curated tools accessible through a single endpoint per role or use case.
• SCIM-driven access: Directory groups determine which tools users can access.
• Credential management: Credentials are securely injected into connector calls.
• Unified audit trails: Tool activity is logged and attributed to users or agents.

As organizations scale from 10 to 100+ agents, each agent needs independent credentials, scoped permissions, and attributable audit trails. Governance should also remain portable across changing AI environments, supporting Claude, Cursor, ChatGPT, Gemini, Copilot, and custom agent frameworks without rebuilding access controls whenever the underlying stack changes.

Mintive's Approach to Agent Governance and Cost Management

While Kong and other API gateways extend proven platforms with AI capabilities, organizations building agent-first infrastructure face distinct governance challenges that require purpose-built solutions.

Mintive addresses enterprise agent governance through three integrated capabilities:

• Virtual MCPs with SCIM-driven access: Instead of managing 60 MCP configurations per employee, IT teams provision role-based tool bundles where directory group membership automatically determines available tools. Engineers, analysts, and support teams each receive curated tool sets through a single endpoint. Credential injection ensures connectors never hold long-lived secrets, while unified audit trails attribute all activity.
• First-class agent identities: Each autonomous agent operates with independent credentials, scoped permissions, and isolated audit trails. M2M authentication, workload identity federation, and OAuth flows support per-agent credentials where connectors require them. This eliminates credential sharing and enables surgical revocation without affecting other agents or users.
• Comprehensive agent observability: Agent Monitor tracks activity across Claude Desktop, Cursor, and other supported clients, capturing prompts, commands, file access, and MCP tool calls. Combined with token tracking and cost attribution, teams gain visibility into both gateway usage and actual agent behavior for AI agent cost tracking and optimization.

This architecture supports provider independence, allowing teams to swap underlying models without rebuilding governance, and scales from pilot deployments to enterprise agent fleets while maintaining consistent policy enforcement and audit trails.

Frequently Asked Questions

How does Kong AI Gateway pricing compare to building custom API gateway infrastructure?

Custom infrastructure eliminates subscription fees but introduces significant hidden costs. Engineering teams must implement and maintain load balancing, rate limiting, authentication, logging, and AI-specific features. Security patching, high availability, and compliance audit capabilities require ongoing investment. Most organizations find the fully loaded cost of custom infrastructure exceeds managed gateway pricing when accounting for engineering salaries and opportunity costs.

Can organizations use Kong AI Gateway for MCP traffic alongside traditional API management?

Kong supports MCP proxying, API-to-MCP conversion, and tool aggregation. Kong first shipped its AI MCP Proxy in Kong Gateway 3.12, while AI Gateway 2.0 later introduced a dedicated AI control plane with first-class MCP Server entities. This allows organizations with existing Kong deployments to extend their platform for AI traffic, though teams requiring Virtual MCPs with SCIM-driven membership should evaluate whether Kong's MCP features match their governance requirements.

What happens when AI gateway request volumes exceed tier limits?

Kong Konnect Plus caps request volumes at 10 million per month. Organizations exceeding this threshold must upgrade to Enterprise tier through custom contract negotiation. Teams should verify overage handling policies and establish alerting thresholds well below limits to avoid service disruption. Budget-conscious organizations may implement request sampling or caching to reduce gateway traffic while maintaining AI functionality.

How do AI gateway costs scale with multi-region deployments?

Global deployments multiply control plane costs and may incur inter-region data transfer charges. Kong's Dedicated Cloud option charges $500 per control plane per region. Organizations requiring low-latency AI access across geographies should model costs for each required region, accounting for both control plane fees and data transfer between regions and to LLM providers.

What audit capabilities are essential for enterprise AI compliance?

SOC 2 Type II attestation does not prescribe a fixed AI-agent logging schema. Organizations need controls and evidence appropriate to the Trust Services Criteria and their own risks, including access control, system monitoring, and security-event logging where relevant. For AI agents, tool-level attribution, arguments and results, guardrail decisions, SIEM export, retention, and tamper-evidence can strengthen auditability when those controls fit the organization's risk model.