AI Gateway vs Portkey
Detailed side-by-side comparison to help you choose the right tool
AI Gateway
Integrations
Databricks central AI governance layer for LLM endpoints, MCP servers, and coding agents. Provides enterprise governance with unified UI, observability, permissions, guardrails, and capacity management across providers.
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CustomPortkey
🔴DeveloperLLM Gateways & Infrastructure
AI gateway and control plane for production GenAI: routes calls across 250+ LLMs with one unified API, plus guardrails, prompt management, observability, budgets, and an MCP-aware agent runtime.
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💡 Our Take
Choose Databricks AI Gateway if you are already on Databricks and want LLM, MCP, and coding-agent governance that inherits Unity Catalog permissions and logs into Delta tables. Choose Portkey if you are cloud-agnostic, want a standalone SaaS gateway with a published pricing page and free tier, and do not need MCP server governance.
AI Gateway - Pros & Cons
Pros
- ✓Native integration with Unity Catalog means permissions, audit logs, and lineage work identically to the rest of your Databricks data assets without extra IAM plumbing
- ✓OpenAI-compatible client interface allows existing application code to point at AI Gateway endpoints with minimal refactoring
- ✓Governs three distinct asset types (LLM endpoints, MCP servers, coding agents) in a single pane of glass — rare across the 870+ tools in our directory
- ✓No charges during Beta (confirmed on docs as of April 15, 2026), letting teams pilot full governance workflows before committing to enterprise pricing
- ✓Supports major coding agents including Cursor, Claude Code, Gemini CLI, and Codex CLI, covering the dominant agent tools developers use in 2026
- ✓Inference tables land as Delta tables in Unity Catalog, making audit and monitoring queries trivially accessible via SQL or notebooks
Cons
- ✗Only available inside the Databricks platform — teams not already on Databricks cannot adopt AI Gateway as a standalone product
- ✗Currently in Beta, meaning feature set, APIs, and limits may shift before GA and enterprise SLAs may not apply
- ✗Two parallel versions exist (new AI Gateway in left nav vs. previous AI Gateway for serving endpoints), which creates documentation and migration ambiguity
- ✗Custom MCP server hosting requires packaging as a Databricks App, adding a layer of platform-specific deployment knowledge
- ✗Pricing is opaque enterprise-contract based with no public tier breakdown, making TCO comparisons against standalone gateways difficult
Portkey - Pros & Cons
Pros
- ✓Provider-agnostic routing with declarative configs that an SRE can change without app deploys
- ✓Strong governance primitives (virtual keys, budgets, RBAC) that internal gateways usually skip
- ✓Built-in guardrails and observability remove the need for a separate vendor for each
- ✓Self-hosted and BYOC options make it viable for regulated and air-gapped deployments
Cons
- ✗Pricing page is JS-rendered and dollar amounts must be confirmed manually on site
- ✗Adds a network hop and latency overhead — small but non-zero next to direct provider calls
- ✗Overkill for single-provider single-app teams who do not need governance
- ✗Some advanced features (Agents, BYOC) require Enterprise contracts
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