Why high availability team chat is a different build for large organizations
For a large organization, chat is often the channel people reach for when something else is failing, so availability has to be designed in before the platform is chosen. The short answer: Mattermost high availability clustering is part of the Enterprise and Enterprise Advanced plans, Rocket.Chat multi-instance and microservice designs require a paid plan, and Matrix scales through Synapse worker processes backed by PostgreSQL. The edition decision therefore comes first, because it determines which high availability pattern you are allowed to run at all, and what support you can expect when something breaks.
Availability is not a toggle. In every option it is a property of three layers: the database, the file storage and the load balancing in front of the application servers. Mattermost high availability assumes redundant application servers, redundant database servers and redundant load balancers, and a cluster needs shared file storage on Amazon S3, an S3-compatible service or NAS mounted on every node, because local file system storage will not work correctly and risks corrupting files. Rocket.Chat 8.x requires MongoDB 8.0 or later as a replica set, with three members strongly recommended. Synapse depends on PostgreSQL and Redis for replication between workers. Each of these choices must be sized and tested by the team that will run them.
Before you commit, confirm with the vendor which edition, support tier and reference architecture match your user count, sites and recovery targets, and get the high availability terms in writing. Then plan a failover test that stops a database primary, a file store node and an application node during a realistic load, and record how long messages, calls and logins take to recover. Illustrative: a team that has never failed over its file store may discover during the test that its uploads were written only to one node.
Which availability requirements shape the build and where they come from
This table maps each availability requirement to the source that creates it and what it means for the build. Read it as a checklist: each row names a vendor document or regulation, so you can confirm the requirement against your own compliance owner before choosing an edition and architecture. The main takeaway is that clustering, shared storage and database replication are vendor prerequisites, while audit and retention duties come from regulation and must be designed in from the start.
| Requirement | Source | What it means for the build |
|---|---|---|
| Redundant application servers, databases and load balancers | Mattermost high availability docs | Mattermost clustering is limited to Enterprise and Enterprise Advanced, so the edition decision comes before the architecture. |
| Shared file storage mounted on every node | Mattermost high availability docs | Use S3, an S3-compatible service or NAS on each node. Local file storage will not work correctly and risks file corruption. |
| Replica set for the chat database | Rocket.Chat system requirements | Rocket.Chat 8.x needs MongoDB 8.0 or later as a replica set, with three members strongly recommended for high availability. |
| Multi-instance and microservice designs | Rocket.Chat reference architectures and plans | Load-balanced instances linked by NATS and Kubernetes designs require a paid plan. Confirm the tier that covers your user count. |
| Read replicas to scale concurrent users | Mattermost high availability docs | Reads go to replicas and writes go to the primary database. Plan connection routing and replica lag monitoring with the database team. |
| Audit controls for systems holding ePHI | 45 CFR 164.312(b) (HIPAA Security Rule) | Chat that carries ePHI needs audit logging of activity. Confirm which edition provides audit trails and how logs are retained and reviewed. |
| Message retention for broker-dealer communications | 17 CFR 240.17a-4(b)(4) (SEC Rule 17a-4) | Where this rule applies, keep originals of business communications for at least three years. High availability must not create gaps in the archive. |
| Survivable channel during an incident | Organizational crisis plan (confirm with your regulator's guidance) | The chat service should run outside the failure domain it supports. Test failover and confirm access for responders when primary systems are down. |
Which platforms fit large-scale availability and why
Each platform reaches high availability in a different way, and the edition you buy decides which of those paths you can use. Mattermost and Rocket.Chat gate clustering or multi-instance designs behind paid plans, while Matrix with Synapse scales by adding worker processes you operate yourself. Confirm the edition, skills and federation needs before you size the platform.
Mattermost clustering and replicas
Mattermost high availability adds redundant application servers, database servers and load balancers, and it is available only on the Enterprise and Enterprise Advanced plans. The core binaries are MIT licensed, but the clustering features are an edition decision, not a free option. Read and search replicas spread query load, with writes still going to the primary database. Confirm with Mattermost which edition covers your user count, and plan shared file storage on S3, an S3-compatible service or NAS, because local storage on a cluster node can corrupt files.
Rocket.Chat multi-instance scaling
Rocket.Chat's core is MIT licensed, but code in its ee directories falls under a separate enterprise license. Its reference architectures move from one instance with a MongoDB replica set to load-balanced instances linked by NATS above 500 concurrent users, then to microservices on Kubernetes above 5,000. Both multi-instance and microservice designs require a paid plan. Rocket.Chat 8.x needs MongoDB 8.0 or later, and a three-member replica set is strongly recommended. Ask the vendor how the paid plan covers automated failover and which MongoDB operator you should use for it.
Matrix with Synapse workers
Synapse is AGPL-3.0 licensed and scales by splitting work into worker processes. The main process and all workers connect to Redis, which relays replication between them, and the generic worker handles client, federation and event traffic. Production deployments should use PostgreSQL, and Synapse 1.143.0 and later require PostgreSQL 14 or newer. Your team will assemble the load balancer, Redis and worker topology, so confirm how much of that design your partner supplies. Also check the upgrade notes, which warn that rolling back can be difficult because of schema changes.
Choosing by skills and federation
Mattermost suits teams that want a supported clustered product with vendor-tracked upgrades. Rocket.Chat suits teams that accept a MongoDB replica set and a paid tier for scaling. Synapse suits teams that need Matrix federation with other organizations and can run a distributed stack. The right fit depends on existing skills, federation needs and the edition budget. Ask each vendor for a written list of which features your user count and deployment model require, and get quoted prices in writing, since none of the three publishes a full price list.
How database and file storage design decide whether chat stays up
Storage design decides more about uptime than the number of application servers. Mattermost high availability runs redundant application servers, redundant database servers and redundant load balancers, but every node must also see the same files. The Mattermost high availability documentation requires shared file storage on Amazon S3, an S3-compatible service or NAS mounted on each node, and warns that local file system storage will not work correctly and risks corrupting files. Before go-live, confirm with your storage team that the NAS or object store meets the latency and throughput your peak upload volume needs, and that every node mounts the same path or bucket with the same permissions.
Rocket.Chat and Synapse put the same question in different places. Rocket.Chat 8.x requires MongoDB 8.0 or later running as a replica set, and a three-member replica set is strongly recommended for high availability, so a single database node is a hidden single point of failure even when the chat instances are duplicated. Synapse recommends PostgreSQL in production, requires PostgreSQL 14 or newer for version 1.143.0 and later, and scales beyond one process through workers that replicate events over Redis. Ask your partner which database engines, replica counts and Redis topology the design includes, and who is responsible for failover of each tier.
Back up the database and the file tier as one unit, and rehearse the restore. A database snapshot taken at a different moment than the matching file store produces messages that point to attachments that do not exist. Define one recovery point for both tiers, test a full restore into an isolated environment, and record how long it takes. Matrix adds a constraint on rollback: the Synapse upgrade notes warn that schema changes between releases can make rolling back difficult, so take a database backup immediately before each upgrade and treat the upgrade as a one-way change unless the vendor documents a downgrade path. Confirm these procedures in writing with your partner, since they determine whether a bad release can be undone during an incident.
How a high availability chat rollout moves from design to go-live
The sequence turns a chat requirement for thousands of users into a topology you have signed off, a runbook your operations team can execute, and failover results you have seen with your own eyes before any staff depend on the platform.
Fix the edition and topology decision
DeployOpen reviews the user count, guest and partner needs, identity requirements and the chosen platform (Mattermost, Rocket.Chat or Matrix) against the edition that licenses the features you need. High availability clustering on Mattermost is an Enterprise plan capability, and Rocket.Chat multi-instance designs require a paid plan, so the licensing conversation with the vendor happens here, not after build. The output is a signed topology document covering node counts, database and file layout, load balancer design, and the edition named in writing. Confirm the current plan terms with the vendor, since pricing is quoted rather than published.
Size capacity and design the database
Concurrent users, peak message rates, channel counts and search load drive the sizing of application servers, the primary database and any read or search replicas. Mattermost distributes reads across replicas and sends writes to the primary, while Rocket.Chat 8.x requires MongoDB 8.0 or later running as a replica set, with three members strongly recommended for high availability. Your team provides expected concurrency, growth projections and any load profile from existing tools. The output is a capacity worksheet and a database design that names backup, restore and replication responsibilities, reviewed with your database administrators.
Build shared storage and network paths
Every application node in a Mattermost cluster must see the same file storage, which means Amazon S3, an S3-compatible service or a NAS mounted on each node. Local disk will not work and risks file corruption, so this decision shapes the whole build. DeployOpen builds the load balancers, DNS, TLS certificates, firewall rules and, where applicable, the NATS or Redis connections that link nodes. Your network and security teams supply addressing, certificate authority rules and change windows. The output is a documented build with each path tested for read and write consistency across nodes before applications are installed on top.
Configure identity and retention controls
Single sign-on, directory synchronization, multi-factor authentication, retention periods and audit logging are configured on the clustered environment, not on a single test node, so that behavior matches production. Feature availability differs by edition, and some compliance-oriented settings depend on paid plans, so each control is checked against the licensed edition during this step. Your identity team supplies the identity provider configuration and group mappings, and your compliance owner supplies the retention schedule. The output is a configuration baseline mapped to each requirement, with any gap recorded as an open decision.
Run load tests against the cluster
Synthetic users simulate messaging, file uploads, search and reconnect storms at the concurrency level in the capacity worksheet, with headroom agreed in advance. The test confirms that latency and error rates stay within thresholds your operations team sets, and that database replicas and load balancers distribute work as designed. Results feed back into node counts or database sizing if the cluster falls short. The output is a load test report listing the scenarios, the measured results, and any changes made, reviewed and accepted by your platform owner before the next step.
Test failover and record the results
Each failure mode in the topology is induced on purpose: one application node stopped, the primary database failed over, a load balancer removed, and the file storage path interrupted. For each case, the team records what users see, how long recovery takes in your environment, and whether messages and files remain intact. Any unexpected behavior becomes a defect that is fixed and retested. The output is a failover test report with pass or fail results per scenario, signed by your operations lead and DeployOpen, and an explicit list of accepted residual risks.
Hand over the runbook and cut over
The tested runbook covers routine operations, upgrades, backup and restore, scaling, incident steps and vendor support paths. Upgrades matter here because Rocket.Chat versions go unsupported on a fixed cycle and Mattermost publishes server releases monthly, so the runbook must state who applies patches and how they are staged across nodes. Staff are trained on the runbook, user migration is scheduled, and cutover is executed with a rollback path documented. The output is a handover pack, a completed cutover record, and a named owner for each operational task after go-live.
What failover testing must prove before go-live
What happens when one application node fails under load?
Clients on the failed node should reconnect to a surviving node, and no message sent during the switch should be lost or delivered twice. Run the test under a load profile that matches your expected peak concurrency, then stop one application node and record how long reconnection takes, the error rate users see and whether websocket sessions resume without manual action. Keep the load tool output, load balancer logs and health check records that show the node was removed from rotation. Add a delivery check that compares messages sent with messages received by test accounts on several clients. Mattermost high availability depends on redundant application servers, so confirm the node count and version in the test plan. For Rocket.Chat, confirm that the multi-instance design you buy is the one being tested, since its reference architecture above 500 concurrent users relies on several instances linked by NATS.
Does chat keep working when the primary database fails over?
The test should prove that a replica takes over as primary, the application reconnects and no committed message is lost. Mattermost sends writes to the primary database and spreads reads across read replicas, so writes stop until promotion completes, and the team must measure that gap in its own environment. Rocket.Chat 8.x requires MongoDB 8.0 or later running as a replica set, where an election selects a new primary. Measure the election time and confirm the application resumes writes without a restart. Record the time of the injected failure, the time of promotion and the time of the first successful write. Compare message counts and replication lag before the event with the counts afterward. Confirm whether promotion is automatic or follows a documented manual runbook, because an operator who has never run that runbook will be slower under pressure.
How should a storage node loss be tested for shared file storage?
Mattermost requires shared file storage on Amazon S3, an S3-compatible service or NAS mounted on every node, and local file system storage will not work correctly and risks corrupting files. The test should remove one storage node, volume or endpoint and confirm that uploads, downloads and file previews keep working from a surviving copy or fail with clear, logged errors. Upload a fixed set of files before the failure and during it, then compare checksums after recovery to detect partial or corrupted objects. Ask the storage provider how data is replicated across failure domains, what happens to uploads in progress and how long a rebuild takes at your data volume. Keep the provider's failover records, the application error logs and the checksum report with the test evidence. Confirm that database backups and file storage can be restored to the same point in time.
What should a load balancer failure test prove?
It should prove that traffic moves to a standby balancer within the switchover window your operations team has agreed, and that users do not have to sign in again unexpectedly. Mattermost high availability includes redundant load balancers, as described in the Mattermost high availability documentation, so test the exact failover mechanism you deploy, whether virtual IP, DNS or a cloud service. Check health check behavior, TLS certificates on the standby, and how sticky sessions and websocket connections are handled. Evidence should include the failover event log, health check records, reconnect results from clients on several networks and certificate validation on both balancers. Also confirm that desktop, web and mobile clients reach the endpoint through the failover path, because a balancer that passes browser tests can still break mobile push or a desktop client.
How do we test a network partition between two data centers?
A partition test shows what chat does when two sites cannot see each other, which is the scenario most likely to expose split-brain behavior. Cut the link between sites under controlled load, then verify that each side keeps serving its own users and that the side allowed to accept writes is the one your design names in advance. After the link returns, messages and presence should reconcile without duplicates or silent loss. Evidence includes the timestamps of the partition and restoration, per-site message counts, database replication state and a reconciliation report listing any conflicts and how they were resolved. Decide before the test which site is authoritative, how users are told to behave during the event and how the crisis channel stays reachable. For Matrix deployments, federation behavior during a partition depends on the homeserver configuration and the state of the federated servers, so confirm that with the chosen setup rather than assuming it.
Who runs the failover tests, and what does DeployOpen cover?
Ownership should be settled in the written scope before the test plan is written, and the client's operations team should attend every test. DeployOpen plans, implements, integrates, hardens, backs up, documents and supports self-hosted open-source platforms inside your own infrastructure, and most engagements are fixed-scope, with deliverables and timelines agreed in writing before work starts. Whether failover test design, runbooks and evidence packs are included, and who executes each test, should be listed explicitly in that scope. Ongoing support, upgrades and continuous testing are available as retainers. Ask for the test catalogue, the environment the tests will use, who approves production changes, which tests are excluded and how your staff will be trained on the runbooks. The main cost drivers are the number of sites, the number of test cases and how closely the test environment matches production. DeployOpen does not publish a price list, so a quote follows from the written scope.
What evidence should a go-live failover report contain?
A go-live report should let the CISO, an internal auditor or an external assessor trace each test from its expected behavior to its recorded result. Include the test plan, the topology under test with platform and component versions, the load profile, timestamps for each injected failure and recovery, metrics and logs, and a defect list with fixes and retest results. Tie the tests to the obligations your organization carries. The HIPAA Security Rule at 45 CFR 164.312(b) calls for audit controls that record and examine activity in systems containing ePHI, so confirm that audit logs survive node loss. SEC Rule 17a-4(b)(4) requires broker-dealers to preserve communications for not less than three years, so verify that archives remain complete after a failover. Store the report in your governance system, and keep the raw evidence for the period your assessor will sample.
Should Rocket.Chat or Matrix change the failover test plan?
Yes, because each platform depends on different components, and the test plan must cover the ones you actually run. Rocket.Chat's multi-instance and microservice designs require a paid plan, and its reference architectures use NATS for instances above 500 concurrent users, so tests must cover the message bus as well as MongoDB. Its paid plans list built-in redundancy with automated failover, which is a claim your test should verify rather than accept from a datasheet. Matrix with Synapse scales through worker processes and Redis, so tests should cover Redis loss, worker restarts and PostgreSQL primary failure. Confirm the edition, topology and exact versions for each test run, and repeat the suite after each upgrade, since Rocket.Chat unsupported versions lose fixes and mobile client compatibility. Plan for monthly Mattermost releases in the same way, and rerun the failover suite whenever the topology or version changes.
Which availability and audit evidence auditors and regulators ask for
This table maps the availability and audit evidence an assessor or regulator is likely to request to the component that produces it on Mattermost, Rocket.Chat, or a Matrix homeserver, plus the database and monitoring layers beneath them. Use it to plan what to collect before go-live, and treat each documented feature as something to verify in your own environment rather than a finished control.
| Evidence | What it shows | Where it comes from | What to confirm |
|---|---|---|---|
| Failover test results | Whether the chat survives loss of an application node, database primary or load balancer | Your test runbook and monitoring records; the platform itself does not issue this | Test scope, dates, pass criteria and who signed off on each run |
| Configuration exports | The clustering, SSO, retention and federation settings in force at a point in time | Mattermost server configuration and admin console; Rocket.Chat workspace settings; Synapse homeserver configuration files | How you version and store exports, and whether changes are tracked |
| Audit and compliance logs | Admin actions, sign-ins and message or file activity | Mattermost audit and compliance features; Rocket.Chat tamper-evident audit trails on paid plans; Synapse logs for homeserver activity | Which edition supports the log types you need, export format and retention period |
| Backup and restore tests | That PostgreSQL, MongoDB and shared file storage can be restored to a consistent state | Database backup tool logs and restore test records; file store snapshot records | Restore time achieved in tests, and that message history and files are restored together |
| Version and support-window records | Which release each node runs and whether it is still supported | Mattermost server release notes and support end dates; Rocket.Chat version durability policy; Synapse upgrade notes | A dated inventory of versions per node and a plan for upgrades before end of support |
| Access reviews | Who holds admin, moderator and database access and whether it was reviewed | Directory sync and SSO records, admin console user lists, database account lists | Review cadence, reviewer and evidence of removal for leavers |
| Monitoring and alert history | Availability, latency and replication health over time, and how incidents were handled | Your monitoring stack, database replication status and incident tickets | Retention of monitoring data and whether alerts map to documented response steps |
What to ask an implementation partner about high availability chat
Start with the edition question, because it decides whether the design is possible at all. Ask the partner to show, in writing, which plan covers the clustering or multi-instance design you need. For Mattermost, the high availability clustering feature sits in the Enterprise and Enterprise Advanced plans. For Rocket.Chat, multi-instance high availability and microservices require a paid plan, and the Starter plan does not cover them. Ask for the licensed seat count behind the quote, and confirm with the vendor which features apply to your air-gapped or FIPS requirements, since those are separate plan entries.
Next, ask how the shared state is sized and protected. Ask who designs the file storage layer, whether it is Amazon S3, an S3-compatible service or NAS mounted on every node, and how the partner proves that local storage is excluded. Ask how the primary database, read replicas and search replicas will be sized against your concurrent user peak, how the Rocket.Chat MongoDB replica set members are placed across failure domains, and how backups are taken for the database and the file store together so they can be restored to a consistent point. Request a written restore test with a measured recovery time, not a promise of one.
Then ask how upgrades and failover are proven and who answers at night. Request the rolling upgrade procedure for your cluster, including how a schema change is handled and how a rollback is planned, since the Synapse upgrade notes warn that rolling back can be difficult. Ask for a failover test plan that kills a node, a database primary and a load balancer under load, with pass criteria and retained evidence. Finally, confirm how a monthly Mattermost release or a Rocket.Chat standard release is kept within support, which LTS or ESR line is chosen, and whether the partner or your team carries the on-call rota. Ask for the written scope and deliverables before any work starts.
How much does high availability team chat cost and how long does it take?
What does high availability team chat cost to run?
Cost comes from four places: the vendor subscription, infrastructure, people, and support. Mattermost sells Professional, Enterprise and Enterprise Advanced as prepaid annual subscriptions priced by seat licenses, and its pricing page lists no public prices, so you will need a quote from the vendor. High availability clustering is included from the Enterprise plan. Rocket.Chat's paid plans are also priced by quote, and multi-instance high availability requires a paid plan. Infrastructure covers redundant application servers, database servers, load balancers and shared file storage. Ask the vendor for a written quote by edition, seat count and term, and ask your team to size the servers and storage against your concurrent user count.
Which chat costs are one-time and which recur?
Most of the spend recurs. Seat licenses and subscriptions renew on their term, and servers, storage, database hosting and backups run every month. One-time costs include architecture and sizing, identity integration with your directory, migration of existing history, failover testing before go-live, and documentation and runbooks. Upgrades add recurring effort because both vendors ship frequent releases. Mattermost publishes a new server version on the 16th of each month, and Rocket.Chat standard releases are supported for 6 months while LTS releases are supported for 12 months. Budget for a recurring upgrade cycle, not only the initial build.
Can our own team run high availability chat without a partner?
Yes, if you have people who already operate PostgreSQL or MongoDB clusters, load balancers, shared storage and Kubernetes or Linux hosting. Mattermost high availability needs redundant application servers, redundant database servers and redundant load balancers, plus shared file storage on Amazon S3, an S3-compatible service or NAS on each node. Rocket.Chat 8.x requires MongoDB 8.0 or later as a replica set. The harder work is failover testing, upgrade rehearsal and on-call coverage. DeployOpen plans, implements, hardens, backs up, upgrades and documents self-hosted platforms in your own infrastructure, and most engagements are fixed-scope with the scope agreed in writing before work starts.
What do our infrastructure and identity teams need to provide?
The build depends on several teams. Infrastructure needs to provide compute for each application node, a highly available database tier, load balancers, shared file storage that every node can mount, backup targets and network paths between nodes. The identity team needs to provide SSO through SAML, OIDC or OAuth and AD or LDAP sync, which Mattermost includes from its Professional plan. Security needs to review TLS, secrets handling and audit logging. Network teams should confirm DNS, certificates and firewall rules for users and mobile clients. Start these requests early, because directory and firewall changes often set the timeline more than the chat software does.
How long does a high availability chat deployment take?
Duration depends on scope, not on a fixed template. The main variables are the number of users and sites, how many integrations and bots you run, whether you migrate Slack or Teams history, how many environments you need (test, staging, production), how many identity sources you connect, and how much failover testing your risk team requires. A single-region deployment with SSO and no migration is a smaller project than a multi-site build with history import and compliance export. Ask the implementation partner for a written schedule with milestones, and check that it includes a failover test and a rollback plan before go-live.
How long does vendor support last for the version we deploy?
Support windows set how often you must upgrade, which affects recurring cost. Mattermost v12.0 has support until 2027-01-15 and v11.11 until 2026-12-15. Mattermost v11.7 is an Extended Support Release with support until 2027-05-15, and v10.11 reached end of life on 2026-08-15. Rocket.Chat standard releases get 6 months of support and LTS releases get 12 months. After end of life, Rocket.Chat versions receive no security or bug fixes, and cloud services such as the push notification gateway stop working. Choose a long-support line where your change process allows it, and confirm current dates on the vendor's release pages before you sign.
What should we ask for in a quote for high availability chat?
Ask for each cost component in its own line so you can compare offers. Request the vendor subscription by edition, seat count, term and support level, including whether high availability, SSO and air-gapped operation are in the chosen plan. Ask the partner for a fixed-scope statement that lists architecture, database and storage design, identity integration, migration, failover testing, documentation, handover and training. Ask what is excluded, what a retainer covers for upgrades and monitoring, and how changes to scope are priced. DeployOpen does not publish a price list, and its quotes follow from written scope.
What does failover testing add to the cost and timeline?
Failover testing adds one-time effort and needs a dedicated window. Test node loss at the application, database and load balancer layers, verify that shared file storage stays consistent, and confirm that clients reconnect and that message history is intact. Local file storage will not work correctly in a Mattermost cluster and risks corrupting files, so test the shared storage path explicitly. Include a failed upgrade and a restore from backup in the plan. Ask the partner which tests are in the fixed scope, how results are documented, and whether a retest is included if a test fails.
How DeployOpen scopes, builds and supports high availability chat
DeployOpen starts with a written topology decision, not a product install. For a Mattermost deployment that must stay up, the plan covers redundant application servers, redundant database servers and redundant load balancers, plus the shared file store each node mounts. For a Rocket.Chat deployment, the plan covers the choice between a single instance on a MongoDB replica set, load-balanced instances linked by NATS, or the microservice design on Kubernetes, and whether the chosen design needs a paid plan. For Matrix, the plan covers Synapse worker layout, Redis, PostgreSQL and the federation listener. Each option is costed in the scope document with the licensing it requires, because the edition decision drives the architecture. Before build starts, the client confirms in writing which edition, which sites and which failure scenarios are in scope.
Implementation runs inside your cloud accounts, data centers or air-gapped network. DeployOpen configures the database replication and backup routines, the shared storage, SSO and directory sync, TLS termination, and hardening of the application and database tiers. It then runs failover tests against the scenarios agreed in scope: a node loss, a database primary failover, a load balancer failure and a storage outage. Each test is recorded with its expected result, actual result and the operator actions taken. The output is a runbook covering failover and failback steps, restore procedures, and the upgrade path for the chosen release line. Ask your partner to show test records from a rehearsal, not only a plan that says failover will work.
After go-live, DeployOpen can support the platform under a retainer that covers patching, version upgrades on the vendor's release cadence and periodic failover rehearsal. Retainer scope, response expectations and what is excluded should be written into the agreement, and the upgrade and support windows of your chosen release line should be confirmed with the vendor before signing. DeployOpen prepares evidence for compliance assessments, such as configuration records, backup logs, test results and access reviews, but does not issue audit reports, attestations or certifications. Independent auditors and certification bodies make those determinations. Engagements are fixed-scope, with deliverables, acceptance criteria and timelines agreed in writing before work begins.
