When does Ollama replace the OpenAI API for a small team?
Ollama fits a single developer or light internal use, where prompts stay on a laptop or one server and a handful of people send requests. The OpenAI API fits a team that wants no inference infrastructure to run at all and accepts that prompts are processed by a third party. The decision turns on five questions: how many requests will run at the same time, whether every caller must authenticate, how far the endpoint is exposed on the network, who gets paged when it fails, and whether the team needs to pin its own model versions.
Start with concurrency and exposure. Ollama's local API at port 11434 has no authentication, and it binds to 127.0.0.1 by default. Moving the bind address with OLLAMA_HOST or placing it behind a proxy such as Nginx makes it reachable by other people, so the team must add its own access control before that change. Concurrency is also set low by default: OLLAMA_NUM_PARALLEL allows one request per model unless you raise it, so confirm the expected load against the configured limits in the Ollama FAQ before promising a shared service.
Ownership decides the rest. If nobody on the team wants to operate a model server, keep the hosted API. If someone will own patching, model downloads, GPU capacity and an on-call rota, Ollama is workable for small internal use, and the team should plan for the step up to vLLM once several users or applications depend on it. Ask whether a model version must stay fixed for audit or quality reasons, since a self-hosted model only changes when your team changes it.
Which option fits each team scenario
This table matches common team situations to the better fit between Ollama and vLLM for teams weighing the OpenAI API. Read each row against the condition in the why column, since that condition usually decides the answer more than team size does.
| Scenario | Better fit | Why |
|---|---|---|
| Solo developer prototyping locally | Ollama | Ollama runs on macOS, Windows, Linux and Docker with a default bind to 127.0.0.1, and its local API needs no authentication, which suits one person on one machine. |
| Small internal team sharing one GPU server | Ollama, or vLLM if concurrency grows | Ollama can serve a few users, but its default of one parallel request per model means queueing appears quickly. Measure queue depth with your own users before choosing. |
| Customer-facing traffic with many concurrent users | vLLM | vLLM provides OpenAI-compatible endpoints, Prometheus metrics and multi-GPU parallelism, which are built for concurrent production load. Ollama's default concurrency settings are not designed for that pattern. |
| Prompts must stay inside a network with no outside calls | Either, configured with cloud features off | Both can run without internet access once weights are transferred in. Ollama needs OLLAMA_NO_CLOUD=1 set, and vLLM needs a reverse proxy and network isolation, so confirm the configuration in your environment. |
| Need for OpenAI-hosted model features Ollama does not offer | Neither, keep OpenAI behind a gateway | Neither Ollama nor vLLM hosts OpenAI's models. If your application depends on specific OpenAI-hosted features, confirm them with OpenAI and keep that path through a gateway rather than moving it. |
Where Ollama's local server stops short for multi-user traffic
Ollama's local server is built for one person running models on one machine. Once several users, applications or departments send requests to the same endpoint, the defaults around concurrency, queueing, authentication and network binding decide whether the service holds up. Each card below names one of those settings and what your team should confirm before it carries shared traffic.
Default concurrency per model
Ollama handles one parallel request per model by default, controlled by OLLAMA_NUM_PARALLEL. When two users call the same model at once, the second request waits for the first to finish, which can look like an outage to an application with its own timeouts. For a team, this means a shared endpoint needs load tested against realistic concurrency before users depend on it. Confirm the value your deployment sets, the number of simultaneous users you expect, and the response times your applications can tolerate.
Loaded model limits
OLLAMA_MAX_LOADED_MODELS caps how many models stay in memory at once. When applications call several models, Ollama may unload and reload them, adding latency and memory churn that is hard to see from the client side. Teams running multiple models on one host should confirm which models are used together, how much GPU or system memory each needs, and whether the cap matches that mix. Test the switching behavior before assigning models to production workloads.
Queue depth under load
OLLAMA_MAX_QUEUE sets how many requests can wait before new ones are rejected, with a default of 512 queued requests. A deep queue hides overload: clients keep waiting while latency grows for everyone. Application teams should agree on a timeout and a retry policy, and platform owners should decide whether a rejected request is acceptable or whether traffic must go to a gateway that sheds load earlier. Confirm the queue size in your configuration and what your applications do when a request times out.
No authentication on the local API
The local Ollama API at localhost:11434 does not require authentication. That is acceptable for a single developer machine but not for an endpoint that several people or services reach. Anyone who can reach the port can run models, consume resources and read whatever the calling application sends. A shared deployment needs an authenticating layer in front of Ollama, such as a gateway that issues keys per user or service. Confirm who can reach the host, how access is granted and revoked, and where keys are logged.
Binding to 127.0.0.1 by default
Ollama binds to 127.0.0.1 on port 11434 by default, so other machines cannot reach it at all. Sharing it means changing the bind address with OLLAMA_HOST and, in most cases, placing a reverse proxy such as Nginx in front to handle TLS, access control and allowed paths. Exposing the bind address directly skips every control the proxy would add. Confirm the bind address, which network segments can reach the proxy, how TLS certificates are managed, and that Ollama itself is not reachable from outside the intended network.
Observability and capacity planning
Ollama's documentation does not describe a metrics endpoint comparable to what a production inference server provides, so teams usually have less visibility into queue depth, token throughput and latency per user. Capacity planning then relies on guesses about peak load. Before go-live, decide which signals you need, such as request counts, latency and queue length, how they are collected, and who reviews them. If those signals are missing, consider whether a production server such as vLLM, which publishes Prometheus metrics, fits the workload better. Confirm the monitoring you have today.
How the OpenAI API and Ollama compare on capability and control
This table compares the hosted OpenAI API with Ollama across compatibility, hosting, data location, authentication, concurrency, model control, licensing, SDKs and cloud features. Read the Ollama column as facts from its documentation and the OpenAI column as points to confirm on OpenAI's own pages, since those terms and plans change. The main takeaway is that Ollama removes per-token billing and keeps prompts local, but its defaults are built for one machine rather than shared production traffic.
| Capability | OpenAI API (hosted) | Ollama (open source) |
|---|---|---|
| API compatibility | Native OpenAI API. Confirm current endpoints and parameters in OpenAI's API reference before porting code. | REST API on port 11434 with OpenAI compatibility, so many OpenAI-style client calls can point at it. Test each endpoint and parameter your application uses. |
| Hosting | Run by OpenAI in OpenAI's cloud. Confirm the regions and deployment options available to your account. | Runs on hardware you choose: macOS, Windows, Linux or Docker. Capacity equals the machines you provision and operate. |
| Data location | Prompts are processed on OpenAI's infrastructure. Confirm retention and regional processing terms in OpenAI's data controls documentation. | Local runs keep prompts and data on your machine, according to Ollama's FAQ. Cloud features send requests to Ollama's servers unless disabled. |
| Authentication | API key managed in OpenAI's account settings. Confirm key scoping and project controls in OpenAI's documentation. | The local API at localhost:11434 has no authentication by default. Put a reverse proxy such as Nginx with access control in front of any shared network exposure. |
| Multi-user concurrency and queuing | Capacity and rate limits are managed by OpenAI. Confirm your account's limits in OpenAI's rate limit documentation. | Defaults to 1 parallel request per model (OLLAMA_NUM_PARALLEL), with a queue of 512 requests (OLLAMA_MAX_QUEUE). Shared production load needs tuning or a server such as vLLM. |
| Model choice and version pinning | Hosted models that OpenAI may deprecate on its own schedule. Confirm current model lifecycle notices on OpenAI's site. | Open-weight models stored locally, so you control which version runs. Model storage path is set in the OLLAMA_MODELS variable. |
| Licensing | Proprietary hosted service under OpenAI's terms. Confirm terms for your use case with OpenAI. | MIT licensed, per Ollama's GitHub repository. Model licenses vary by model and must be checked separately. |
| SDKs | Official SDKs maintained by OpenAI. Confirm supported languages in OpenAI's documentation. | Python and JavaScript SDKs, per Ollama's GitHub repository. |
| Cloud features | Not applicable: the service is hosted by default. | Direct cloud inference at ollama.com requires an API key. Turn off cloud features with OLLAMA_NO_CLOUD=1 or the server configuration file. |
How to turn off Ollama cloud features and keep prompts local
This sequence confirms that the Ollama server listens only on the local loopback interface, disables cloud features, removes any dependency on ollama.com, and checks with outbound network logs that prompts stay inside your network. The result is a documented, verifiable local deployment you can audit.
Confirm the server binds to loopback
The platform or infrastructure engineer checks the address Ollama listens on. By default Ollama binds to 127.0.0.1 on port 11434, so only processes on the same host can reach it. If you have set OLLAMA_HOST to expose the server to other machines, record the value and the reason. Done when a socket listing on the host shows the expected address and port, and you have documented any change from the default.
Set OLLAMA_NO_CLOUD to 1
The engineer sets the OLLAMA_NO_CLOUD variable to 1 in the environment of the Ollama service, or adds the equivalent setting to the server configuration file, then restarts the service so the change takes effect. Ollama's FAQ describes this variable as the way to turn off cloud features. Done when the running service picks up the setting from its configuration, which you confirm by checking the service environment or the configuration file in version control.
Decide whether direct cloud inference is needed
Product owners and the security lead decide whether any workload should call Ollama's hosted cloud models at ollama.com. Those calls send requests to a third party and require an API key sent as a Bearer token. If the answer is no, record that decision and skip any key issuance. If the answer is yes, route those workloads through a separate, approved path with its own data classification review. Done when the decision is written down with an owner.
Confirm no application calls ollama.com
The application and platform teams search code, configuration, container images and CI variables for references to ollama.com, including the /api and /v1 paths on that host. They also confirm that client SDKs and SDK defaults point at the internal Ollama address. Remove any hardcoded cloud endpoints and any stored API keys for ollama.com that are no longer needed. Done when a repository and configuration search returns no active cloud endpoints and the applications only reach the internal address.
Block outbound traffic to Ollama cloud hosts
The network team adds egress rules on the host or the perimeter firewall that deny outbound connections from the Ollama host to ollama.com, leaving only the internal routes it needs for updates that your change process approves. This turns the configuration choice into an enforced control. Done when a test request from the Ollama host to the cloud endpoint is refused by the firewall and the rule appears in the change record.
Verify prompts stay inside the network with outbound logs
The security or operations engineer runs a test prompt through the local API, then reviews outbound network logs from the firewall, proxy or flow records for the test window. The check confirms that no connection to an external model host carried the prompt. Repeat the check after each Ollama upgrade. Ollama states that when it runs locally it does not see your prompts, but confirm this against your own deployment and version rather than relying on the vendor statement. Done when logs for the test window show only internal traffic and the result is saved with the version tested.
Record the configuration and review it on upgrade
The platform team writes the verified settings, the bind address, the variable value, the firewall rule and the test evidence into the runbook and the infrastructure repository. Each Ollama release can change defaults or add features, so the team repeats the outbound log check and reviews the release notes before upgrading. Done when the runbook names an owner for the review and the evidence is attached to the change ticket for each upgrade.
How data control changes by deployment environment
On a developer laptop, Ollama is the usual runtime. Its local server listens on 127.0.0.1 port 11434 by default, so only processes on that machine can reach it, and the local API does not require authentication. Ollama states that when it runs locally it does not see your prompts or data. Model files sit in the user's profile unless you move them, for example ~/.ollama/models on macOS, /usr/share/ollama/.ollama/models on Linux or the .ollama\models folder on Windows. The OLLAMA_MODELS variable relocates them, which matters when the laptop's disk is encrypted differently from the rest of the organization's storage.
- Model storage: Weights are stored under the user profile by default, so check where
OLLAMA_MODELSpoints and whether that volume is encrypted. - Reach: The default bind limits access to the local machine. Setting
OLLAMA_HOSTto a wider address exposes it to the network, and that change must be a deliberate decision. - Access control: Nothing is enforced on the local API, so access depends on who holds the device and its login.
- Logging: Ollama does not provide per-user audit logs for local use. Teams that need request records must add them in an application or a gateway.
- Cloud features: Set
OLLAMA_NO_CLOUD=1or the equivalent server configuration so no request is routed to cloud models.
Before relying on a laptop for regulated data, confirm the disk encryption, the device management policy and whether anyone else can read the user's model directory.
A single shared server, on premises or in a cloud account, serves many users from one machine. The control question moves from the device to the network path and the service in front of the model. Ollama can be exposed through a reverse proxy such as Nginx, and vLLM's OpenAI-compatible server can run the same way. Neither gives you user identity by default, so the team must place an authenticating proxy in front and decide which endpoints users may call. The vLLM security documentation warns that its --api-key option does not cover every route, including /metrics and /health, and recommends a reverse proxy that allowlists only the endpoints meant for users.
- Model storage: Weights live on the server's disk, so the volume, its encryption and its backup scope need written owners.
- Reach: Bind the service to a private interface and restrict the port with host or network firewall rules, so only the application subnet can connect.
- Access control: Put identity-aware authentication at the proxy, and issue per-application keys. Ollama's local API has no authentication, so it must not be exposed without a proxy in front.
- Logging: vLLM exposes Prometheus metrics at
/metricsfor request counts and latency. Request and response content is not logged by default, so decide whether prompt logging is permitted and where it is stored. - Evidence the team produces: Access review records, firewall rule history, proxy configuration, patch records and the data retention decision for prompts and logs.
Ask the implementer who owns the proxy and whether /metrics is reachable by anyone outside the monitoring network. DeployOpen prepares controls and evidence for these checks, but independent auditors and certification bodies issue any attestation.
In a Kubernetes cluster run by a platform team, the controls are expressed as cluster objects: namespaces, network policies, secrets, service accounts and ingress rules. vLLM's documentation lists native manifests, Helm charts and several production stacks as deployment options. Model weights are usually mounted from a persistent volume or pulled from an internal object store, so the storage class, the bucket policy and the image registry become part of the data path. For multi-node runs, vLLM states that inter-node communication is insecure by default and must sit on an isolated network.
- Model storage: Weights sit on persistent volumes or internal buckets. Confirm encryption at rest, who can mount the volume and whether model files are copied into container images.
- Reach: Use network policies so only the gateway namespace can reach the inference pods, and keep the node-to-node traffic on an isolated network.
- Access control: Use cluster RBAC for operators, service accounts for workloads and a gateway for end users. Store keys in a secrets manager, not in manifests.
- Logging: Cluster audit logs record API calls to the Kubernetes control plane. Metrics come from the
/metricsendpoint, and request content logging depends on the gateway configuration. - Evidence the team produces: Policy manifests as code, RBAC reviews, image provenance records, audit log retention and change records for each upgrade.
Confirm with the platform team which logs are retained, for how long, and who can read them. Assessors will ask for this evidence, and DeployOpen prepares it, but the attestation itself comes from the independent auditor or certification body.
What does running Ollama cost compared with paying per token?
The main cost of running Ollama is not the software, which is MIT licensed, but the hardware and the people who keep it running. Ollama's pricing page states that running models on your own hardware is always unlimited, and its paid tiers (Pro, Max, Team and Enterprise) apply to cloud models. So a self-hosted local server carries no per-token fee from Ollama. Your costs are the machines or GPU servers that hold the models, which you either already own or buy once and refresh on a schedule, plus power, space and the network. The OpenAI API bills per token on OpenAI's own terms, so confirm current rates and any enterprise commitments directly with OpenAI before comparing.
The staff cost is the one teams underestimate. Someone must install and update Ollama, pin model versions, restrict the API (it has no authentication locally, so a reverse proxy with access control is needed before any user traffic arrives), monitor the host and respond when it fails. Each of these is recurring labor, and it is paid whether traffic is light or heavy. The risk cost of a single host is also real: if that machine goes down, every dependent application stops, and Ollama alone offers no failover. Ask whether the work is covered by internal engineers, a managed retainer or a fixed-scope implementation, and who is on call outside office hours.
Four factors move the balance. Traffic volume and utilization decide whether hardware sits idle or saturates; Ollama's default of one parallel request per model (OLLAMA_NUM_PARALLEL) means a busy team hits limits quickly. Model size decides how much memory and which GPU a model needs, so a larger model can change the hardware bill more than any other choice. The number of people depending on the service raises the cost of an outage and the case for redundancy. Per-token API spend rises with usage instead. In a written quote, ask for hardware specifications and refresh cycle, support and on-call hours, an outage and recovery plan, the update and security patching schedule, and a statement of which costs recur monthly and which are one-time.
What a move from Ollama up to vLLM involves
This sequence takes a team from a single-user Ollama setup to a vLLM endpoint that serves multiple users, with a reverse proxy, metrics, validated outputs and a rollback path in place before traffic moves.
Confirm the traffic has outgrown Ollama defaults
Before any hardware is ordered, the team checks whether real usage has hit the limits of a single-user setup. Ollama's defaults allow 1 parallel request per model and queue up to 512 requests, and the local API has no authentication. Note how many concurrent users, which models and what context lengths the workload needs. The output is a written traffic profile that names the concurrency target and the model. If the profile fits within one developer's machine or a small Ollama server, the move may not yet be needed, and the team should confirm that before continuing.
Size GPU capacity for the target model
Using the traffic profile, the platform or infrastructure team estimates accelerator memory for the chosen open-weight model, the context length and the number of concurrent sequences. vLLM runs on NVIDIA CUDA, AMD ROCm and Intel XPU GPUs, and it can split a model across GPUs within a node with tensor parallelism. Ask the implementation partner and your hardware vendor to confirm the sizing against a benchmark run on your own data. The output is a hardware bill of materials and a capacity note showing which concurrency level each configuration supports.
Deploy vLLM with vllm serve
The team installs vLLM on the target hosts, starts the model with vllm serve and sets the API key with the --api-key option. Applications keep their OpenAI client code and change only the base URL, because the server exposes /v1/chat/completions, /v1/completions, /v1/embeddings and /v1/responses. Run the server on an internal address first. The output is a working endpoint that answers a test chat request from an application host, confirmed by a successful response in the server logs.
Place a reverse proxy in front
The --api-key option does not protect /metrics, /health, /pause, /resume, /abort_requests or /invocations, and the vLLM security documentation warns against relying on the key alone. Put a reverse proxy such as Nginx in front of the server and allowlist only the /v1 paths that users need. Block everything else at the proxy, including the metrics and health routes from external clients. The output is a proxy configuration and a test showing that a blocked path returns a refusal from outside the network while the allowed chat path still works.
Enable Prometheus metrics at /metrics
vLLM exposes Prometheus-format metrics at /metrics covering request counts, token throughput, latency histograms and KV cache use. The monitoring team scrapes these from the server on an internal network, not through the public proxy, and sets histogram buckets that match the latency targets in the traffic profile. Alerts should cover queue growth and KV cache pressure. The output is a dashboard that shows live throughput and latency for the endpoint, plus an alert that fires in a controlled test.
Test outputs against current prompts
The application team reruns its production prompts against vLLM and compares answers with the outputs from Ollama or the previous hosted model. They check format compliance, tool or JSON output where the application depends on it, and any embeddings used for search, because changing an embedding model requires re-embedding stored vectors. Reviewers sign off on a set of test cases. The output is a written evaluation record with pass and fail results for each prompt group, and the open items that block cutover.
Shift traffic gradually and keep a rollback path
The team routes a small share of requests to vLLM while the remaining users stay on the previous path, then increases the share as dashboards and evaluations stay clean. A gateway or a configuration switch in each application makes the routing change reversible. The rollback path keeps the previous Ollama server or hosted endpoint available until the new service has run stably. The output is a documented cutover log and a tested rollback command that the on-call engineer has run once in a non-production setting.
Plan for vLLM upgrades and recurring costs
vLLM has no paid edition, so its license cost is zero, and the recurring cost comes from accelerator capacity, the surrounding infrastructure, implementation and the staff who operate it. vLLM ships new minor releases about every two weeks, for example v0.31.0 on October 5, 2026, so the team needs a schedule for testing and applying upgrades in staging before production. Ask your implementation partner for a written upgrade and support scope. The output is a runbook that names who tests each release, who approves production changes and how the team confirms a release is safe to adopt.
What do teams ask before they switch from the OpenAI API to Ollama?
How much code change does an application written for the OpenAI API need to move to Ollama?
Often less than you might expect, but the exact amount depends on which endpoints and parameters your application uses. Ollama exposes a REST API on port 11434 with OpenAI compatibility, so the base URL and model name are the first things to change. Anything outside the compatibility layer, such as assistants, file handling, fine-tuning, batch jobs or provider-specific parameters, needs a rewrite or a different path. Build an inventory of every OpenAI SDK call in your codebase, then test each one against a local Ollama instance. Ask any consultant to confirm the exact endpoints and parameters before estimating the work.
Can a small team do the Ollama switch without outside help?
A small team with one or two engineers who already run Linux servers can usually handle a single-machine pilot without outside help. The harder part is making it safe for several users, which requires a reverse proxy, access control, monitoring and a plan for updates. Ollama's own documentation describes exposing it through proxies such as Nginx, so someone must own that layer. If nobody on the team has operated a production service before, plan for external support or a fixed-scope engagement with written deliverables.
How long does a move from the OpenAI API to Ollama take?
The timeline is driven by the number of integration points, the testing you need to trust output quality, and how many users depend on the service. A pilot with one application and a handful of developers can move faster than a rollout across several products. Factors that add time include custom prompts that were tuned for OpenAI models, retrieval or embedding pipelines, authentication, logging and cutover planning. No duration figure is supplied for this work, so ask any provider to base the timeline on a written scope with named deliverables.
What skills and roles does running Ollama in-house require?
You need Linux administration, basic networking, GPU driver setup if you use a GPU, and someone who owns the service after launch. The model files live in a directory that Ollama manages, configured through the OLLAMA_MODELS variable, so storage planning matters. Networking covers the bind address set by OLLAMA_HOST, TLS, and the proxy in front. Name a single owner for upgrades, since Ollama ships frequent releases and each update should be tested against your applications before it reaches users. Without that owner, the service drifts.
Can Ollama serve several users safely without a proxy?
No, not for shared production traffic. By default Ollama binds to 127.0.0.1 on port 11434, and its local API does not require authentication. Concurrency is also limited by defaults: OLLAMA_NUM_PARALLEL is 1 request per model, and OLLAMA_MAX_QUEUE holds 512 queued requests. Exposing the port directly to a network would allow anyone who can reach it to send requests. Put a reverse proxy with authentication, rate limits and logging in front, and test concurrency under realistic load. When throughput or concurrent users grow, plan the move to vLLM, which is designed for multi-user serving.
Do I have to turn off Ollama's cloud features?
If your data must stay on your own infrastructure, yes, you should disable them and verify it. Ollama states that when it runs locally it does not see your prompts or data, and cloud features can be turned off with OLLAMA_NO_CLOUD=1 or in the server configuration file. Direct cloud inference at ollama.com requires an API key sent as a Bearer token, so if no key is configured, your applications should not call those endpoints. Confirm the setting on every server, document the change for your compliance reviewers, and check that outbound network rules block the cloud endpoints.
When should a team move from Ollama to vLLM instead?
Consider vLLM when you need several concurrent users, measurable throughput, or an endpoint that behaves more like a production inference service. vLLM exposes OpenAI-compatible endpoints including /v1/chat/completions and /v1/embeddings, so the application-side change is usually small. It also exposes Prometheus metrics at /metrics, which helps capacity planning. Hardware, GPU planning and operations take more effort than Ollama, and the --api-key option does not protect every endpoint, so a reverse proxy is still required. Ask a consultant to confirm the current release and hardware fit before committing.
How DeployOpen helps teams evaluate, implement or move off the OpenAI API
DeployOpen plans, implements, hardens, documents and supports self-hosted open-source platforms inside your own cloud accounts, data centers or air-gapped networks. For OpenAI API vs Ollama, that means helping you decide whether a local Ollama server is enough or whether a multi-user endpoint needs vLLM, then building and handing over the one you choose.
How we scope
- We start with your user count, expected concurrency, data classification and whether any prompts may leave your network.
- We confirm which OpenAI API endpoints your applications call so compatibility gaps are listed before any build starts.
- Engagements are fixed-scope, with scope and deliverables agreed in writing before work begins, and we then provide a quote from that written scope.
How we build
- We configure Ollama with OLLAMA_HOST, OLLAMA_NO_CLOUD and concurrency settings, or deploy vLLM behind a reverse proxy that exposes only user-facing endpoints.
- We set up authentication, network isolation and a gateway in front of the model server, and test requests against your existing OpenAI-style client code.
- We run load tests against your target concurrency and review /metrics output so you can see queueing and latency before users depend on the server.
How we hand over
- You receive configuration, network diagrams, runbooks for model updates and a written record of which cloud features were disabled and how.
- We document the parts of the switch that change for application teams, including model names, error handling and any output differences you should validate.
- Handover includes a review session with your platform, security and application owners so the people who run the service can operate it.
How we support
- Ongoing support, upgrades and continuous testing are available as retainers, covering version upgrades for Ollama or vLLM after handover.
- We can repeat security testing of the endpoint and its authentication after changes, and report findings against your written scope.
- We prepare systems, controls and evidence for compliance assessments, but independent auditors and certification bodies issue any audit reports or certifications.
