Kong AI vs Solo.io: Which AI Gateway Fits Enterprise AI Teams?

Enterprise teams increasingly compare Kong AI vs Solo.io as gateway decisions expand beyond traditional APIs. Modern AI applications require model routing, access to tools, cost controls, and agent governance. These requirements place an AI gateway beside the traditional API gateway as a separate infrastructure decision.

Both platforms have also evolved quickly. Kong AI Gateway 2.0 became generally available on September 1, 2026. It now treats models, MCP servers, and agents as first-class entities. Solo.io continues to develop agentgateway for LLM, MCP, HTTP, and A2A traffic. 

That changes how Kong AI and Solo.io should be compared. Kong no longer represents an API platform with only several AI plugins. Solo.io no longer has a clear advantage simply because agents matter. The stronger distinction now lies in operating model, existing infrastructure, deployment preferences, and surrounding platform requirements.

TrueFoundry provides a third approach for enterprises entering the agentic AI phase. It focuses on governed access across models, tools, and agents rather than broader API or networking estates.

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Start With Gateway DNA Before Comparing Features

The most useful new method for comparing these platforms starts with their architectural roots. A buyer’s guide or white paper can explain categories. Current documentation should still verify capabilities because both product portfolios are changing quickly.

Kong comes from API management and Kong Gateway. Earlier AI capabilities extended those familiar patterns into LLM models, provider routing, prompt controls, and token-aware policies. Kong AI Gateway 2.0 now has its own runtime and control plane, with first-class models, agents, MCP servers, policies, and consumers.

This means Kong can now route LLM traffic, MCP requests, and A2A interactions through one managed AI layer. The data plane remains separate from the Konnect control plane. By default, model and agent payloads stay outside the managed control-plane path.

Solo.io comes from service mesh and cloud-native networking. Agentgateway provides an AI-first proxy for models, tools, agents, and conventional HTTP traffic. Its Kubernetes deployment uses Kubernetes Gateway API resources alongside Solo-specific policies and backends. A standalone mode provides another operating pattern outside Kubernetes.

For a deeper explanation of why these gateway categories differ, see TrueFoundry’s AI gateway vs API gateway comparison. 

Where Kong AI Gateway Fits Best

Kong AI Gateway fits enterprises that already use Kong or prefer a managed AI connectivity layer. Its current platform handles models, MCP servers, and A2A agents through first-class entities. The gateway also provides authentication, policy enforcement, routing, cost visibility, and observability through Kong Konnect.

The biggest update is Kong AI Gateway 2.0. Kong moved beyond its earlier plugin-centered approach and introduced a dedicated AI runtime. The platform can now centrally manage AI providers, models, MCP endpoints, and agents. This improves operational simplicity for organizations already using the Kong ecosystem.

Rate limiting also works at several AI layers. Kong can limit requests by model, agent, MCP server, consumer, or consumer group. AI-specific policies can calculate limits based on tokens and cost rather than on request count alone.

Kong’s Model Context Protocol capabilities are also substantial. AI MCP Server entities can expose REST services as tools, proxy existing servers, or combine tools. The gateway supports authentication, access control, traffic policies, and MCP-specific observability.

Kong now supports A2A traffic as well. AI Agent entities expose upstream agents and apply protocol-aware telemetry, authentication, ACLs, and policies. This removes an important gap from earlier Solo.io vs Kong AI comparisons.

Use Kong AI when:

• The existing Kong Gateway infrastructure supports important enterprise traffic.
• Teams prefer Kong Konnect for centralized gateway configuration and monitoring.
• Model and agent traffic should share familiar security controls.
• Token-aware budgets and rate limits matter across production workloads.
• Enterprise support should cover both API and AI connectivity.

Kong remains attractive for an engineering team already invested in its wider ecosystem. Its advanced features also benefit organizations that want a single vendor for API and AI connectivity.

Where Solo.io Fits Best

Solo.io remains a very capable competitor when cloud-native networking drives the platform decision. Solo Enterprise for agentgateway supports Kubernetes and standalone operating models. Its scope covers LLMs, MCP tools, agents, HTTP services, authentication, resiliency, and policy controls.

Kubernetes deployments use a dedicated control plane that converts Gateway API resources into agentgateway configuration. This makes Solo attractive to teams already operating a Kubernetes gateway model. The platform can also fit organizations moving away from Ingress NGINX toward newer gateway standards.

Solo’s broader networking portfolio also matters. Its history includes the Envoy proxy, Istio, and service-mesh infrastructure. Solo Enterprise for Istio supports Istio ambient mode, while current Solo documentation includes enterprise ambient multicluster support.

That ecosystem can matter when service mesh operations remain part of the same platform responsibility. Teams may need workload identity, waypoint proxies, or an ambient mesh alongside agent connectivity. In these environments, Istio ambient mesh capabilities can influence the gateway decision.

The surrounding operational model deserves attention. The Gloo Operator can manage Istio lifecycle in supported Solo environments. An existing ambient mesh lab can therefore provide a useful place for teams to test migration patterns before production adoption.

Teams should also compare resilience requirements such as retries, circuit breaking, and outlier detection. Upgrade procedures should support zero downtime wherever production workloads require continuous availability.

Use Solo.io when:

• Kubernetes-native ownership already forms part of platform best practices.
• Agent and MCP connectivity sit beside broader networking requirements.
• Teams require detailed infrastructure-level policy customization.
• Existing Solo or Istio investments influence future architecture.
• A self-operated native gateway matches platform ownership preferences.

Organizations running Google Cloud, on-premises clusters, or other cloud infrastructure should test the exact deployment path required. Teams using Amazon ECS should separately confirm how standalone deployment fits their operating model.

How Kong AI and Solo.io Compare on MCP, Agents, and Governance

Agent governance has become central to Kong AI vs Solo.io because modern systems extend beyond model requests. An AI agent can discover tools, call business systems, and communicate with other agents. These agentic systems require identity and policy controls across every interaction.

Kong AI Gateway 2.0 now provides strong MCP security through AI MCP Server entities. Teams can proxy existing MCP endpoints or expose REST APIs as MCP tools. ACLs, authentication strategies, logging, and traffic policies apply through the gateway.

A2A has also become a first-class capability. Kong can rewrite agent cards, route A2A requests, collect structured telemetry, and enforce security policies. This means the earlier claim that Kong lacked agent-to-agent governance no longer applies.

Solo reaches similar outcomes through agentgateway. Its MCP support includes both static and dynamic servers, as well as virtual MCP configurations. Tool-level authorization can inspect identity information before permitting specific actions. MCP-specific limits can also protect expensive operations.

Solo also provides cost controls for model traffic. Current agentgateway documentation covers virtual keys, per-key budgets, token limits, model-cost catalogs, and a built-in cost dashboard. These capabilities reduce several earlier missing pieces in AI cost governance.

TrueFoundry becomes relevant when enterprises want these controls in a single AI-focused governance layer. Its MCP governance layer centralizes authentication, authorization, tool discovery, and policy enforcement. Its agent workflow controls extend governance across autonomous execution.

The selection should therefore focus on use cases, rather than assuming one platform lacks core protocol support. Kong AI and Solo.io now cover much of the same model, MCP, and agent surface.

Where the three actually separate is the workflow layer above individual calls. TrueFoundry expresses per-workflow limits as gateway defaults that individual workflows override:

# Gateway default applies to every workflow unless overriddendefaults:  token_budget_per_request: 50000  loop_detection: onworkflows:  research-crew:    token_budget_per_request: 120000   # overrides the 50k default  support-router:    # no override, inherits the gateway default

How Should Buyers Compare Cost and Operational Ownership?

Cost comparisons between Kong AI vs Solo.io should include licensing and internal operations. Gateway software represents only one part of total ownership. Staffing, observability storage, upgrades, policy maintenance, and incident response can create substantial operational overhead.

Kong runs three models. Open-source Kong Gateway costs nothing to self-host. Konnect Plus bills per gateway per month, with included request volume and per-million overages. Konnect Enterprise runs on custom annual terms.

The AI Gateway add-on meters per unique LLM model, so every model a team routes to becomes a billable dimension rather than a configuration change. TrueFoundry's Kong Gateway pricing analysis works through the stacking effect.

Solo.io licenses its enterprise agentgateway build separately from the open-source project, with the governance features regulated buyers need sitting on the commercial side. The Solo AI Gateway pricing breakdown covers that step-up.

The larger number is usually staffing. Kong AI stays efficient for teams already running Kong Gateway. Solo.io stays efficient for teams already operating Kubernetes-native gateways. Either becomes expensive when a team underestimates policy design, observability storage, upgrade ownership, and compliance evidence.

TrueFoundry reduces assembly work through an AI Gateway control plane that spans model access, routing, governance, budgets, and audit logs.

Published tiers run Developer at $0 for 50,000 requests a month, Pro at $499 for 1 million requests, Pro Plus at $2,999, and Enterprise on custom terms. Teams should also decide whether a declarative code mode or managed control experience better matches internal skills. There are different ways to achieve the same technical control. The operating model determines how much engineering effort each approach requires.

What Do Kong AI and Solo.io Still Leave for Enterprise Teams?

Kong AI and Solo.io solve real gateway problems. Neither one automatically solves every enterprise AI governance problem. Buyers still need to define how identity, budgets, tool permissions, model routing, agent limits, and audit records work across teams.

The gap sharpens when production agents use models and tools together. A request may start as a model call, continue as an MCP tool call, and then trigger another workflow. Enterprises need one place to govern that chain, and a per-request counter reconstructs none of it afterward.

Whether the shortlist ends on Solo.io or Kong AI, that chain still needs an owner.

Common gaps to evaluate:

• Per-team and per-agent budget enforcement.
• Identity propagation across models and tools.
• Audit logs connecting users, agents, tools, and costs.
• Circuit breakers for runaway agent workflows.
• Private deployment for prompts, traces, and logs.
• Governance across multiple providers and agent frameworks.

These questions become increasingly important as generative AI shifts toward autonomous workloads. Teams should also distinguish product enterprise features from capabilities requiring additional integration or infrastructure.

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Where TrueFoundry Fits in the Kong AI vs Solo.io Decision

TrueFoundry provides another option when governance becomes the primary buying requirement. Its TrueFoundry AI Gateway connects model traffic with budgets, guardrails, routing, and observability. Enterprises can apply shared policies without making general API infrastructure the center of the design.

The LLM Gateway centralizes provider access, routing, fallback, cost visibility, and controls. The MCP Gateway governs tool access through three independent layers of inbound authentication, access control, and outbound authentication. The Agent Gateway adds workflow limits, circuit breakers, and traceable execution.

Enforcement is declarative and lives in version control. Rules evaluate in order, and the first match wins:

name: ratelimiting-configtype: gateway-rate-limiting-configrules:  # Cap one contractor account on a specific model  - id: "contractor-gpt4-daily"    when:      subjects: ["user:contractor@example.com"]      models: ["openai-main/gpt4"]    limit_to: 1000    unit: requests_per_day  # Give every user an independent daily token budget  - id: "user-daily-limit"    when: {}    limit_to: 1000000    unit: tokens_per_day    rate_limit_applies_per: ['user']

The `rate_limit_applies_per` field creates a separate counter per entity, so a single rule covers all users without generating a rule per identity. A request over its limit returns HTTP 429, naming the rule that fired, alongside an `x-tfy-applied-rules` header:

{  "status": "failure",  "message": "Rate limit exceeded for model: openai-main/gpt4 with rule: contractor-gpt4-daily",  "error": {    "type": "RateLimitError",    "code": "429"  },  "error_origin_level": "rate_limit_budget"}

Choose TrueFoundry when:

• Unified AI governance matters more than gateway customization.
• Multiple teams need a governed model and access to tools.
• Agents require workflow controls before execution.
• Compliance teams need audit evidence inside approved environments.
• VPC, on-prem, or air-gapped deployment is required.
• Cost controls must work across models, agents, and teams.

This does not make either alternative weak. Both are technically strong platforms with different foundations. TrueFoundry provides a distinct fit when enterprise AI governance is the primary control-plane requirement.

Final Verdict: Kong AI or Solo.io?

The final Kong AI vs Solo.io decision should begin with the infrastructure your organization already operates. Kong fits enterprises with strong API platform investments and teams that value Konnect. Its latest AI Gateway also directly supports models, MCP, and A2A traffic.

Solo.io fits organizations that want deeper infrastructure ownership and Kubernetes-native control. Its agentgateway covers models, tools, agents, and HTTP services. The wider Solo ecosystem also matters when ambient mode, mesh infrastructure, or other networking requirements remain part of the roadmap.

Choose Kong AI or Solo.io based on the operating model your team can support. Kong emphasizes an integrated API and AI platform. Solo emphasizes cloud-native control and closer ownership of gateway infrastructure.

For Solo.io or Kong AI, current capabilities are much closer than older comparisons suggest. The decisive factors are now deployment, ownership, ecosystem alignment, and governance requirements.

TrueFoundry becomes the stronger third option when unified AI governance matters more than traditional gateway lineage. Its platform focuses on models, MCP tools, agents, budgets, identity, and enterprise policy, all within a single AI infrastructure layer.

Book a demo to compare your current gateway stack and design governed AI infrastructure for production agentic workloads.