Nova vs BigPanda 2026
Two AIOps platforms, two different bets on autonomy. BigPanda doubled down on alert correlation as the front door. Nova built around agentic remediation and unified post-mortems. Here's how they actually compare.
The product thesis
BigPanda's pitch is straightforward: you already have ten monitoring tools, they all generate alerts, BigPanda is the brain that correlates them into one incident view. That thesis was sharp in 2018; it still works. The product is mature, the integration count is high, and the operations team understands what it does.
Nova's pitch is broader: stop treating alert correlation as the goal. The goal is incident closure, diagnosis, remediation, post-mortem, learning loop. Correlation is one step, not the destination. Nova ships 100 specialized agents that handle everything from detection to runbook execution to post-mortem authorship.
That difference shapes everything below. BigPanda is a great correlation hub. Nova is an end-to-end incident-to-resolution platform with correlation embedded inside it.
Detection and noise
BigPanda doesn't generate alerts, it ingests them from your existing tools (Datadog, New Relic, Splunk, Prometheus). Detection quality is whatever your underlying tools provide. The value-add is deduplication and noise reduction at the alert layer.
Nova generates its own signals. The platform deploys agents that watch metrics, logs, and traces directly, with anomaly models tuned per service. You can also forward existing tool alerts in for correlation, but the core detection is native. The advantage: detection is tightly coupled with the remediation loop, so when an agent flags a regression it can immediately trigger the runbook.
For teams who want to keep their existing monitoring stack and just add correlation, BigPanda fits cleanly. For teams who want one platform that detects, correlates, and remediates, the dual-tool model is friction.
Correlation engine
BigPanda's correlation is rule-based with a topology overlay. You define correlation patterns; the engine groups matching alerts. There's some ML to suggest patterns, but the heavy lifting is operator-defined logic. It works well once tuned; tuning takes a quarter.
Nova's correlation engine is graph-based with embedding similarity. Alerts are nodes, services and dependencies are edges, and the engine clusters by topological proximity plus semantic similarity of the alert text. No rule-writing for the common cases. The 38% p95 latency cut in v2.7 came from rebuilding the embedding index to handle the cluster structure natively.
The practical difference: BigPanda needs an operator to tune correlation rules continuously as services change. Nova picks up new services from the topology graph and starts correlating their alerts within hours.
Automation and remediation
BigPanda's automation is mostly notification routing, the right team gets paged, the right Slack channel gets the alert. There's some integration with runbook tools (Rundeck, Ansible Tower) but the platform itself doesn't execute remediation; it hands off.
Nova's automation is the core of the product. The Remediate agent picks up an open incident, evaluates known remediations from the runbook library, executes the safe ones automatically, and queues the riskier ones for human approval. Audit ledger logs every action with who-or-what initiated it.
If your operating model is "humans do the fixing, the platform tells them what to fix," BigPanda is well-fit. If you want the platform to actually close low-risk incidents without paging anyone, Nova is the choice.
Post-mortems
BigPanda doesn't author post-mortems. The platform exports incident timelines and integrates with post-mortem tools (Jeli, FireHydrant), but the writing is human work.
Nova's v2.7 release shipped AI Post-Mortems. The Postmortem agent assembles a draft from the incident timeline, the actions taken, the customer impact, and the chat history, then routes it for human sign-off. Average time-to-published-post-mortem dropped from 8 days to 18 hours in our customer cohort.
For teams running 50+ incidents a month, this difference compounds. BigPanda exports the data; you still need to write the post-mortem. Nova writes the draft; you edit and ship.
Integrations
BigPanda's integration count is the longest in the industry, 200+ source integrations. Anything that emits alerts, BigPanda probably ingests. This is its single strongest competitive moat.
Nova ships 80+ integrations focused on the cloud-native stack: AWS/Azure/GCP, Kubernetes, Datadog, Prometheus, OpenTelemetry, Slack, Jira, ServiceNow, GitHub, GitLab. Less breadth than BigPanda, but each integration is bidirectional, Nova can both read signals and execute actions through the same connector.
If your stack includes 20-year-old tools (Tivoli, OpenView, custom SNMP), BigPanda probably has the integration and Nova doesn't. If your stack is cloud-native, Nova covers it with deeper integration depth.
Pricing and TCO
BigPanda prices on alert volume, typically $50k-$300k+ annually for mid-market, more for enterprise. The model penalises noisy environments, which is the environment most buyers have when they go shopping.
Nova prices on host count or service count, with unlimited alert ingestion. The pricing model encourages full instrumentation rather than penalising it. Mid-market deals run $30k-$150k; enterprise scales with infrastructure rather than incident volume.
Three-year TCO including the operator time to tune correlation rules: BigPanda lands 20-30% higher for the same detection coverage in our benchmark customer set, mostly because of the correlation-tuning labour and the alert-volume pricing.
Which one to pick
Pick BigPanda if you have an existing monitoring stack you're not replacing, your operating model puts humans in the loop for every fix, and you need correlation across 100+ legacy tool integrations. The product is mature and reliable.
Pick Nova if you want one platform for detection, correlation, remediation, and post-mortem; you're cloud-native; and you want agents that actually close incidents rather than just route them. The end-to-end model is the bigger lift.
Both products are real. The choice is about your operating model, humans-in-the-loop coordination, or agentic closure with humans on approvals. There's no wrong answer, but there's a wrong fit if you pick the one that doesn't match your team.