Choosing a Tyk alternative: when open source plus licenses stops adding up

Tyk earned its place in a lot of enterprise stacks honestly. The gateway is a single Go binary, it is genuinely open source, and it is fast. For platform teams burned by JVM-based gateways, Tyk looked like the modern answer, and for pure traffic proxying it still performs well. When those teams start evaluating a Tyk alternative, it is rarely because the gateway process is slow. It is because the product around the gateway turned out to be a different architecture than the one they evaluated.

The pattern is consistent across regulated enterprises we talk to. The team adopts the open-source gateway, then discovers that everything an enterprise actually needs to operate it is a separately licensed, closed-source component: the Dashboard for management, RBAC for access to that Dashboard, the analytics layer, the Enterprise Developer Portal, and MDCB if you run more than one data centre. The open-source core is real, but it is not the product you end up running. What you run is a distributed system of five or six components, one of which (Redis) holds your entire credential store in memory.

For a bank, a healthcare platform, or a government team, the question is not whether Tyk can route requests. It is whether the operational and audit posture of that component set still makes sense, and whether it can govern AI agent traffic without adding yet another component. That is the standard a Tyk alternative has to meet.

Where Tyk's component model works against regulated teams

Three architectural decisions define what it is like to operate Tyk at enterprise scale, and none of them are bugs. They are the design.

First, Redis is not a cache in Tyk. It is the keystore. API keys, session objects, rate limit counters, and OAuth tokens live in Redis, and the gateway consults it on the hot path. If Redis becomes unavailable, the gateway cannot validate credentials. Teams respond by running Redis with the same availability engineering as a tier-one database: Sentinel or Cluster, memory headroom monitoring, failover drills. That is a significant operational program for a dependency most teams initially filed under "cache".

Second, analytics is a pipeline you assemble, not a feature you enable. The gateway buffers request records into Redis, and a separate service, Tyk Pump, drains them into a sink such as MongoDB, Elasticsearch, or Splunk. If Pump lags or dies, records accumulate in Redis until memory pressure forces a choice between losing analytics and destabilising the keystore, because they share the same Redis. For a compliance team that treats request logs as evidence, "the analytics mover was down for four hours" is a finding, not an inconvenience. Full payload recording exists but is off by default and expensive to run through this path.

Third, the management surface is split. API definitions can live in files on the gateway, in the Dashboard's database, or both, depending on the deployment mode. The Dashboard, its RBAC, and the Enterprise Developer Portal (a separate application with its own database) are licensed per deployment, priced per node or by negotiation. The audit story fragments the same way: gateway logs in one place, Dashboard audit in another, analytics in the sink Pump writes to. When a regulator asks "show me every request this partner made in March, and every configuration change that affected them," the answer spans three systems.

Kong has a version of this same shape, with its own split between open-source core and licensed enterprise plane, and we have written about why teams leave Kong for similar reasons. The Tyk-specific twist is how much weight Redis carries.

What a Tyk alternative needs beyond a faster gateway

Zerq starts from the assumption that the control plane, the data plane, the developer portal, and the audit trail are one product, because in a regulated environment they are audited as one product. The architecture is a single Go binary that serves gateway traffic, the management API, the MCP endpoints, and request logging. State lives in one place: a MongoDB instance you own. There is no separately licensed dashboard, no closed-source tier of the product, and no per-node licensing. Pricing is a flat annual fee per deployment environment with everything included, which you can read plainly on the pricing page.

One state store, and Redis demoted to a cache

Zerq's runtime configuration makes the storage model explicit. This is the relevant portion of the backend environment:

# backend/.env — storage and cache configuration
MONGODB_URI=mongodb://mongo.internal:27017  # config, request logs, and audit events all live here
DB_NAME=zerq                                # database name in your own MongoDB cluster

CACHE_TYPE=memory                           # single replica: in-process cache, no Redis at all
# CACHE_TYPE=redis                          # multi-replica: shared cache and rate limit state
# REDIS_URL=redis.internal:6379             # only read when CACHE_TYPE=redis

ENCRYPTION_KEY=<32-byte-key>                # encrypts stored credentials at rest
AUDIT_ENABLED=true                          # management audit trail on config changes

Two things in that file are the direct answer to the Tyk operational model. Credentials are stored encrypted in MongoDB, not in Redis memory, so the durability and backup story for your credential store is the same as for the rest of your configuration. And Redis is optional: a single-replica deployment runs with CACHE_TYPE=memory and no Redis at all, while multi-replica deployments use Redis only to share cache entries and rate limit state. If Redis fails in a Zerq deployment, the gateway logs the failure and falls back to in-memory caching. Traffic keeps flowing and authentication keeps working, because Redis was never the keystore.

Request logs without a pump

Zerq writes request logs to MongoDB through an asynchronous logging path inside the gateway process. There is no separate mover service to deploy, monitor, or explain to an auditor. Every request log entry carries the request ID (also returned to the caller in the X-Request-ID header), timestamp, HTTP method, incoming path, the target endpoint the request was forwarded to, status code, end-to-end latency in milliseconds, client ID, profile ID, the matched collection, the client IP, and the full request and response headers and bodies.

For a compliance review, that means one query surface. The observability views in the management UI filter by client ID, profile ID, path with wildcards, status code or class (4xx, 5xx), latency threshold, time range, and full-text payload search across request and response bodies in a single query. Filters are written into the URL, so an auditor can bookmark "all requests from this partner, last 30 days, non-2xx" and rerun it every review cycle. Denied requests are first-class evidence: a 401, 403, 405, or 429 appears in the same log with the client identity and the reason, which is exactly what a control-verification exercise needs.

The management side has its own audit trail: publish events, auth method and policy changes with actor identity, and credential create, rotate, and revoke events, all in the same MongoDB. Config intent and runtime enforcement sit side by side instead of across three systems.

An access model that reads like your partner contracts

Tyk expresses consumer access through keys and policy objects that live in Redis and the Dashboard database. Zerq's model is flatter. Every consumer is a client. Each client authenticates through one or more profiles, where a profile fixes the auth method (token, JWT, OIDC, or mTLS), optional IP restrictions in CIDR notation, and optional HTTP method limits. Each client's traffic volume is governed by an attached policy, which defines a short-window rate limit (1m, 5m, or 1h intervals) and a long-window quota (1d, 7d, or 30d). Every request carries X-Client-ID and X-Profile-ID, so the identity in the security model is the identity in the request log, with no join required.

AI agents on the gateway you already run

Tyk predates the Model Context Protocol, so teams putting AI agents in front of their APIs face the second-gateway problem: another component, another credential path, another audit gap. In Zerq, MCP is a built-in surface of the same binary. An AI agent connects to the gateway's MCP endpoint, authenticates as a client profile like any other consumer, and discovers only the collections that client is authorized to see through list_collections, list_endpoints, and endpoint_details. When it calls execute_endpoint, the request passes through the same auth, policy, and rate limit checks and lands in the same request log with the same fields. Governing agent traffic is not a new project; it is a new profile.

Migrating from Tyk in practice

The migration is mostly a re-mapping exercise, because the concepts line up. A workable sequence for a team running Tyk OSS plus Dashboard:

Inventory your Tyk API definitions and export the OpenAPI specs for the services behind them. In the Zerq management UI, create a collection per API product and import each spec (Collections, then Import OpenAPI); the proxies and endpoint schemas are generated from the spec.
Map each active Tyk key or policy holder to a Zerq client. Go to Clients, click New Client, name it after the business identity (not a random key), select the collections it may access, and attach a policy.
Recreate your Tyk policy tiers as Zerq policies under Policies, then New Policy. Translate Tyk's rate and per-second window into a rate limit with a 1m, 5m, or 1h interval, and Tyk's quota into a quota with a 1d, 7d, or 30d interval.
Add a profile per environment for each client (Add Profile inside the client): choose the auth type, set allowed methods, and add the partner's egress CIDR range. Copy the X-Client-ID and X-Profile-ID values into your partner records.
Validate one allow path and one deny path per client with curl before cutover: an authorized call returns 200, a blocked method returns 405, an out-of-range IP returns 403, and a burst past the rate limit returns 429. Each outcome should appear in the request log with the client's identity.
Run Zerq in parallel with Tyk, move partners by DNS or by issuing the new headers alongside the old keys, and keep Tyk available for rollback until the request logs show all traffic on the new gateway.

Steps 1 through 4 are configuration work in one UI backed by one database. There is no key export from Redis, because Zerq credentials are created in, and encrypted into, MongoDB from the start.

What this looks like in practice

A payments fintech running Tyk OSS with the licensed Dashboard had built the standard stack around it: clustered Redis sized for both the keystore and the analytics buffer, Tyk Pump feeding Elasticsearch, and a homegrown partner portal because the Enterprise Developer Portal licence had not survived budget review. Two incidents pushed them to evaluate alternatives. A Redis failover during a deploy caused several minutes of failed key validations on live payment traffic, and a Pump outage silently dropped a day of analytics records that a scheme audit later asked for.

They rebuilt on Zerq over six weeks. Twenty-two partner integrations became twenty-two clients, each with sandbox and production profiles and a policy matching the partner's contracted tier. The Redis cluster shrank to a small instance for multi-replica cache and rate limit coordination, with the knowledge that losing it degrades cache hit rates rather than authentication. Request logs land directly in the same MongoDB their compliance team already queries for configuration audit, and the audit-only role gives that team log access with no ability to change configuration. The homegrown portal was retired in favour of the built-in developer portal, where partners sign in by magic link and see only their own products. When the team later piloted an internal AI agent against the payments sandbox, it took one new client with one profile scoped to the sandbox collection, not a new gateway.

Closing

Tyk's gateway is good, and its problems are structural rather than incidental: the credential store lives in Redis memory, analytics is a pipeline with its own failure modes, the management and portal layers are separately licensed closed-source components, and agent traffic has no native home. Zerq gives you one binary and one database for gateway, management, portal, MCP, and audit. It gives you credentials encrypted at rest in storage you back up like everything else. It gives you request logs written directly to your MongoDB with client identity on every entry, one flat fee instead of per-node licensing, and AI agents governed by the same profiles, policies, and audit trail as every other consumer.

Zerq is an enterprise API gateway built for regulated industries — one platform for API management, AI agent access, compliance audit, and developer portal, running entirely in your own infrastructure. See how it works or request a demo to walk through your specific requirements.