Speedscale vs. Keploy: A Technical Comparison

Speedscale vs. Keploy starts with how you want to use production traffic. Speedscale connects recorded traffic to repeatable regression tests, dependency mocks, and performance tests. Its passive Kubernetes collector records supported traffic without adding a proxy hop, while proxymock brings recording and replay to a developer’s machine. The benefit is a path from real application behavior to tests that developers and platform teams can run again.

Keploy also turns recorded behavior into tests and mocks, and its Apache-licensed core is useful when source availability is a priority. The decision becomes more specific when your service depends on Postgres, runs across developer laptops and Kubernetes, or needs performance testing and production data controls. Those are the workflows where Speedscale deserves a closer look. Both products have multiple capture paths and can run outside Kubernetes; the edition and execution mode matter.

We build Speedscale. This review examines the vendors’ documentation, published plans, and a pinned snapshot of Keploy’s public source, reviewed on October 23, 2026, with the linked feature documentation rechecked on October 26. The recommendations below reflect our assessment of those documented workflows. We have not run a controlled head-to-head benchmark.

Speedscale vs. Keploy feature comparison

Speedscale’s strongest case is the combination of passive cluster capture, local replay, traffic transformations, and performance testing. This matrix compares documented capabilities, including the mode and plan restrictions that affect whether you can use them. A listed feature does not mean it is included in every subscription or works with every runtime.

Feature	Speedscale	Keploy
Passive Kubernetes capture	nettap DaemonSet observes supported traffic without a recording proxy or application sidecar	Optional DaemonSet captures existing pods without a sidecar or restart; default quickstart uses sidecars
Native developer workflows	proxymock on macOS, Linux, and Windows; local proxy configuration required	Native CLI on Linux, Apple Silicon macOS, and x86-64 Windows; protocol coverage differs by OS
Local Postgres mocking	Records and mocks Postgres through mapped ports	Native macOS/Windows capture Postgres as raw bytes that usually do not replay; use a Linux-based path
Protocol-aware replay	HTTP/1.1, HTTP/2, gRPC, Postgres, MySQL, MongoDB, Redis, Kafka, and AMQP, subject to documented limits	Playground, Pro, and Enterprise list broader protocols and databases, including Postgres; Open Source database row lists MySQL; native desktop limits still apply
Regression testing from recordings	Baseline comparison and a failing gate for new mismatches	Recorded requests become tests with dependency mocks; editable YAML artifacts
Load testing from traffic	Runnable workflow with virtual users, throughput, latency percentiles, and a real database alongside a mocked API	Pro advertises load testing; verify the workflow, dependency controls, and reports in your evaluation
Dynamic replay data	Transforms rewrite traffic and carry values across requests; shared engine for cloud templates and local blueprints	Noise and replacement configuration; time freezing requires Linux-based execution
Production data controls	Configured DLP redaction before cloud storage and Enterprise customer-owned storage	Enterprise private and air-gapped deployments; review capture, artifact storage, and secret handling for the chosen setup
CI execution	Headless API-key initialization and scripted replay	Documented CI replay uses a free Keploy account and KEPLOY_API_KEY; the Open Source plan also lists CI replay
Licensing	Commercial platform and proprietary proxymock; free use has eligibility limits	Apache 2.0 public core, with separate commercial features and distribution components

For a platform team, the value is in connecting these capabilities: collect supported production traffic, redact configured sensitive fields, adapt the recording, and use it for local or cluster testing. Speedscale documents each part of that workflow. Keploy has overlapping features, so assess the complete workflow in the edition and mode you would deploy.

Compare the deployment modes first

The useful unit of comparison is a particular binary, capture mode, and subscription. Here is the architecture summary; the sections below explain the requirements and exceptions.

Workflow	Speedscale	Keploy
Kubernetes recording	nettap DaemonSet; passive capture for supported traffic	Sidecar is the quickstart default; an optional DaemonSet avoids application pod mutation
Local recording	proxymock, with explicit proxy or port configuration	Installed CLI instruments the launched app; Linux and native desktop backends differ
Proxy in the recording path	Absent with nettap; present with proxymock	Present in the general proxy architecture; observe-only / low-latency capture is a documented exception
Test artifacts	Recorded exchanges used for replay, comparison, and mocks	Recorded requests and dependency interactions stored as editable YAML
CI authentication	proxymock supports headless API-key initialization	Documented keploy test workflow uses a Keploy account (the free tier works) and KEPLOY_API_KEY

Sources: Speedscale eBPF, proxymock architecture, Keploy Kubernetes modes, Keploy recording architecture, and Keploy’s observe-only exception. The authentication workflows are documented in Speedscale installation and Keploy CI setup.

Capture and TLS change with the mode

Speedscale: passive cluster capture, explicit local proxy

For new Kubernetes installations, Speedscale recommends nettap. It observes TCP traffic at the kernel boundary and supported TLS traffic through library probes or the Java agent. The application continues talking to its original destination; recording doesn’t add a proxy hop. For platform teams, that removes the need to route application connections through a recording proxy on the supported nettap path. The sidecar TLS guide retains the older proxy instructions for existing installations and environments outside the eBPF requirements.

Supported TLS capture has specific prerequisites. Go requires preserved ELF symbols; rustls has collector-version and build requirements; OpenSSL capture targets 3.x. Java uses an instrumentation agent and needs a JVM restart. Check the TLS capture requirements and Java compatibility matrix for the application you intend to record.

On a laptop, proxymock uses a different design. You route the relevant app or client connections through its proxy. TLS interception requires the corresponding certificate trust setup. During dependency replay, a mock responder also needs to answer connections in the dependency’s place. Passive cluster recording doesn’t make local proxying or replay certificate configuration disappear. The proxymock architecture guide shows those separate flows.

Kubernetes recording keeps the collector outside the application’s connection path:

flowchart TD
    app[Application] --> dependency[Real dependency]
    app -. Observed data .-> nettap[nettap]

Local recording routes the configured connection through proxymock:

flowchart TD
    client[Application] --> proxy[proxymock]
    proxy --> target[Real dependency]

These paths have different networking requirements. The TLS requirements above apply to each path.

Keploy: distinguish proxy recording from observe-only capture

On Linux, Keploy’s general architecture describes eBPF redirecting outgoing connections into a transparent network proxy; on macOS and Windows, which have no eBPF, it hooks the application in user space. The proxy decodes dependency exchanges and intercepts TLS with a substitute certificate chain. This is the documented proxy-based recording path.

It would be incorrect to conclude that every Keploy recorder therefore proxies traffic. Its public connection-handling reference explicitly excludes observe-only / low-latency capture from redirection. Its capture-loss guide identifies eBPF capture in keploy record --low-latency, sidecar low-latency mode, and the DaemonSet agent.

The Kubernetes quickstart distinguishes two deployment choices. Default sidecar mode injects an agent and rolls the application deployment on first recording. DaemonSet mode targets existing pods through recording-session resources without injecting a sidecar or restarting the application. Neither the word eBPF nor the word DaemonSet settles the TLS compatibility of an individual workload. Have Keploy demonstrate your TLS provider, connection pooling, and capture mode together.

Permissions and failure behavior still matter

Speedscale’s collector runs as a non-root user without privileged-container mode, but needs host visibility and capabilities including CAP_BPF, CAP_PERFMON, and CAP_SYS_ADMIN. Those are file capabilities on the executable, so the container also sets allowPrivilegeEscalation: true. Its kernel baseline is 5.15+ on x86_64 and 6.0+ on arm64, with BTF enabled; RHEL has a 9.2+ baseline. These are real deployment requirements, documented in the eBPF reference.

Keploy’s Kubernetes guide specifies Linux 5.10+ on x86-64 and 6.4+ on arm64 for its DaemonSet recorder. Its DaemonSet architecture guide describes read-only access to the application namespace and a separate control plane that creates recording-session resources. Read-only application access should not be interpreted as an absence of cluster permissions.

For either observer, missing capture bytes can invalidate a recording. Speedscale publishes ring-buffer drop metrics and a procedure for measuring workload impact. Keploy documents gaps caused by zero-copy operations, missed hooks, and oversized calls, including how they affect parsing. A recorder continuing to serve application traffic is a different property from that recorder producing complete replay inputs.

Local support depends on the actual binary

proxymock runs natively on macOS, Linux, and Windows. Its installation guide covers native binaries and activation. This path doesn’t require a Kubernetes cluster, but it does require application networking configuration appropriate to the protocol and runtime.

Keploy also has native desktop support. Its current installation instructions specify Apple Silicon for native macOS and x86-64 for native Windows, with Linux-based alternatives. More importantly, those instructions limit native desktop protocol-aware handling to HTTP/HTTPS, MySQL, and MongoDB for the documented workflows. They explain that other dependencies, including Postgres, Redis, Kafka, and gRPC, are captured as raw bytes and usually do not replay. A native CLI existing on your OS is not evidence that your whole dependency graph is supported there.

The public repository explains why an installed Keploy binary can differ from a source build. In its platform-support code, the default in-tree interception backend is Linux eBPF; native macOS and Windows backends ship in the installed product. This is a useful distinction if your organization requires source builds or restricts proprietary components.

For an HTTP API backed by Postgres, this gives Speedscale a concrete advantage over Keploy’s documented native macOS and Windows workflows: proxymock has a Postgres recording and mocking guide that uses mapped ports on the developer’s machine. Keploy directs users of those dependencies to a Docker or Linux-based path. Validate your driver’s prepared statements and TLS settings, but account for that extra execution environment when comparing setup effort.

Protocol support has three separate gates

First, can the recorder see the bytes? Second, can a parser turn them into meaningful requests and responses? Third, can the replay engine answer the client’s next exchange correctly? Generic TCP capture only answers the first question.

Speedscale’s technology matrix notes that capture, analysis, and replay are supported separately. It lists HTTP/1.1, HTTP/2, gRPC, Postgres, MySQL, MongoDB, Redis, Kafka, and AMQP, with restrictions. For example, Redis RESP2 and RESP3 are supported, while Pub/Sub and MONITOR are not. mTLS is listed as partial. A broad matrix still needs an application-specific verification.

Keploy’s public parser registration file imports generic, HTTP, and MySQL parsers. Its build workflow also has an explicit private-parser setup action. Those facts explain why inspecting the public repository is useful but insufficient to establish the capabilities of every distributed binary.

Keploy’s plan matrix separates Open Source, Playground, Pro, and Enterprise. At the review date, its Open Source database row lists MySQL, while the other columns list additional databases. Its recording environments row lists Kubernetes under Pro and Enterprise, although the Enterprise card presents recording inside Kubernetes as an Enterprise addition. Production capture is listed under Enterprise. Confirm the exact edition, mode, and protocol in your evaluation agreement; the product-wide list and the source repository are not interchangeable entitlements.

Replay correctness requires deliberate controls

Both products can create replay inputs from observed traffic. Both still need rules for changing values and an explanation of how mocks are selected. A recorded response can contain a production bug, and a broad ignore rule can make a regression disappear.

Keploy exposes noise and replacement configuration and documents a time-freezing feature for expired tokens and clock-sensitive behavior. Its time-freezing guide says Go replay patches a copy of the compiled binary, while other runtimes use a clock library. Native macOS and Windows replay do not support that feature; Docker or Linux-based execution is required. This can be a useful capability when time itself is an input to the application, subject to its documented execution requirements.

Speedscale’s transforms can refresh or rewrite values such as timestamps and JWTs. Cloud templates and local blueprints use the same transformation engine, so teams can apply the same transformation model when adapting traffic for local development and cluster replay. Its regression-test tutorial demonstrates a known-good baseline, a deliberately changed application, and a failing gate for new mismatches. Rewriting replay traffic and freezing the application’s clock solve different problems; they should not be presented as equivalent checkboxes.

Mocking also needs a network boundary. The proxymock architecture documents passthrough when a request doesn’t match a mock. Keploy’s configuration supports bypass rules and disabling mocking; its cluster replay architecture documents a deny-egress NetworkPolicy. Decide which dependencies may remain real, enforce that decision, and deliberately trigger an unmatched call. A green test that silently reached a real payment API proves less than it appears to.

Performance tests must say what remains real

Speedscale documents using recorded traffic for both regression replay and load generation. The proxymock performance tutorial keeps Postgres real while mocking an external API, then compares a baseline with an application change that adds database queries. That setup can expose a database bottleneck that a fully mocked database would hide. The tutorial includes virtual-user settings, latency percentiles, throughput, and the SQL statement evidence behind the slowdown. Teams can follow the example to evaluate an application change under load with the dependency boundary made explicit.

flowchart TD
    generator[Load replay] -->|Measure latency and throughput| app[Service under test]
    app --> database[(Real Postgres)]
    app --> api[API mock]

Keeping Postgres real lets the test expose database costs while the recorded API mock controls the external dependency. High-throughput load mode skips response scoring; the regression workflow supplies the correctness check.

Keploy’s Pro pricing card advertises load testing. It would therefore be inaccurate to say Keploy has no load-testing capability. Before treating the implementations as interchangeable, request the actual workflow: how recorded requests become load, which protocols participate, which dependencies stay real, and what concurrency controls and percentile reports you receive. The published plan establishes an advertised capability; it does not establish comparable throughput or overhead.

We found no controlled head-to-head benchmark in the sources reviewed. Speedscale’s published probe execution measurements describe one capture environment, not how much faster an application would run than with a particular Keploy mode. A defensible comparison needs the same application, hardware, traffic, TLS configuration, and dependency topology, with capture overhead measured separately from replay throughput.

Data residency and licensing need explicit boundaries

With local proxymock, captured traffic stays on the machine unless you authorize sharing. Its data and privacy documentation also says it sends telemetry and validates the API key on each run. Local traffic storage should not be described as a completely disconnected tool.

For cluster capture, Speedscale documents forwarder DLP rules that redact configured sensitive fields before cloud storage. Its Enterprise BYOC option supports customer-owned destinations. The direct S3 replay-storage path still uses the Speedscale API for account validation, registration, and configuration. Customer-owned payload storage and an air-gapped control plane are different requirements.

Keploy advertises private and air-gapped Enterprise deployments. Its Kubernetes architecture documents MinIO and MongoDB artifact storage, plus replay in a Docker runner or a separate customer-provided cluster. The separate-cluster flow can copy referenced ConfigMaps and Secrets into the replay namespace. Include that secret lifecycle and the permissions of the replay kubeconfig in your design review. These are documented options in Keploy’s plans and replay architecture, not evidence that the default installed CLI operates offline.

Keploy’s public core has an Apache 2.0 license. Speedscale’s proxymock CLI is proprietary; its EULA limits use without a paid subscription to specified non-commercial open-source use or commercial undertakings with fewer than 250 employees and less than US $10 million in annual revenue, and requires paid subscriptions for government entities. Our own pricing page describes the Free, Pro, and Enterprise offerings, but the applicable license remains part of the comparison.

Keploy’s Apache license is a concrete advantage when you need to inspect and build the public core. For a production testing decision, compare the complete workflow as well: required protocols, native desktop backends, cluster capture, data controls, CI access, and support. Speedscale’s commercial offering should be evaluated on the work it covers across that lifecycle, with its license and subscription terms included in the decision.

Run the same evaluation against both products

Use one representative service with an inbound API, a database, and a second API dependency. Choose a request that performs a write followed by a read, and another that returns a handled error. Use your actual runtime, driver, TLS configuration, and deployment restrictions. Record tool versions, editions, and capture modes alongside the results.

Evaluation	Evidence to retain
Establish a baseline	The unchanged build passes twice with stable assertions and documented noise rules
Introduce a response regression	A changed response field or status fails the gate and identifies the difference
Exercise dependency behavior	Prepared statements, transactions, reconnects, and your required streaming calls replay correctly
Remove a mock	The result exposes the miss; outbound monitoring confirms which real dependencies were contacted
Refresh credentials	Replay works after recorded tokens expire, without bypassing the application’s intended checks
Measure recording impact	Baseline and capture runs retain p50, p95, p99, CPU, memory, and capture-loss evidence
Repeat the load test	Identical requests and database starting state; the report names real and mocked dependencies
Inspect the artifacts	Sensitive fields are absent where required, and storage, retention, and account connections are understood

Keep the captured requests and dependency interactions available for inspection. A pass rate without evidence of which calls were mocked, ignored, dropped, or sent to real services is an incomplete result. For async or streaming systems, add a scenario that proves message ordering and termination; an HTTP happy path cannot stand in for that work.

Why we recommend Speedscale for production traffic testing

For a Speedscale vs. Keploy evaluation spanning Kubernetes and developer machines, we recommend starting with Speedscale. Passive cluster capture, local Postgres mocking, reusable transforms, DLP, customer-owned storage options, and documented regression and performance workflows address concrete needs across that process. The local Postgres distinction is especially relevant for macOS and Windows teams that would otherwise need a Docker or Linux-based Keploy workflow.

Keploy remains a reasonable choice when an Apache-licensed core and editable YAML artifacts are the deciding requirements. It also offers time freezing and commercial deployment options. Those strengths do not establish equivalent protocol coverage on every OS or equivalent testing workflows in every plan.

Start with the proxymock CLI quickstart, record a representative service, and run the regression and performance tutorials against it. Use the evaluation above to confirm protocol support, replay behavior, and deployment fit. For specific inaccuracies in Keploy’s comparison page, our separate response addresses them directly.