One engine. Batch, stream, and serverless. 10–25× faster.
Falcon is a compiled, Kubernetes-native distributed compute engine. It replaces legacy systems like Spark, Databricks, EMR, and custom solutions — without changing your storage, your catalog, or your governance layer. Deployed in your environment. Priced for efficiency.
Falcon spins up all executors and distributes jobs evenly
Pre-compiled jobs, distributed compute, no GC pauses
Given the same spend, do up to 10x more with Falcon
Wasted time and compute spend on a slow cold start
Runtime compilation, queued compute, long GC pauses
Same hour, same spend, most of the queue still waiting
A single engine for the workloads that drive your bill.
Batch ETL, streaming, AI inference, content extraction, analytics, low-latency serving — handled by one runtime, configured through one tool, deployed wherever you need it.
Batch ETL that finishes in minutes, not hours.
Compiled pipelines and operator fusion turn the same Spark workload into a fraction of its runtime — without changing your code.
Up to 25x faster with refactoring.
Falcon optimized pipelines compile down to vectorized native code. Same data. Same outputs. Different engine.
2-5x faster as a direct PySpark replacement.
A F100 Systems Integrator tested Falcon on a Spark ETL + ONNX inference pipeline with no code changes.
Every aspect, designed for efficiency.
Without Haevek: Runtime interpretation, garbage-collection pauses, and a single-driver bottleneck pile overhead around actual tasks.
With Haevek:
One compute layer. Every workload between source and consumer.
Falcon sits between the systems that produce data and the systems that consume it — a single, compiled engine for batch, streaming, and inference. The lakehouse, catalog, and infrastructure underneath stay yours.
If you run Kubernetes, Falcon deploys.
Helm-installed into your namespace. OIDC for auth. RBAC for access. IAM for storage. No external network paths required. Up and running in minutes.
Cloud.
EKS, AKS, GKE — your existing managed Kubernetes, your existing cloud account, your existing IAM.
On-prem.
OpenShift, Rancher, vanilla K8s. Bring your own hardware; we run inside your firewall.
Air-gapped.
Zero external dependencies. Image registries you control. Suitable for FedRAMP, IL4+ and classified work.
Edge.
MicroK8s or k3s on a single-core sensor pod. Same binary as the cloud. Identical operating model.
Cloud.
EKS, AKS, GKE — your existing managed Kubernetes, your existing cloud account, your existing IAM.
On-prem.
OpenShift, Rancher, vanilla K8s. Bring your own hardware; we run inside your firewall.
Air-gapped.
Zero external dependencies. Image registries you control. Suitable for FedRAMP, IL4+ and classified work.
Edge.
MicroK8s or k3s on a single-core sensor pod. Same binary as the cloud. Identical operating model.
Why workloads cost up to 95% less on Falcon.
The differences aren't configuration tweaks. They're architectural. Spark and its derivatives carry overhead that Falcon was designed to eliminate.
| Spark | Falcon | |
|---|---|---|
| Compiles ahead of time to native code — no JVM, no interpretation | ||
| Controls memory directly — no garbage collector, no mid-job pauses | ||
| Executes deterministically, fixed at compile time | ||
| Guarantees concurrency safety in the compiler | ||
| Runs two layers from the hardware instead of three | ||
| Scales to zero by default — no always-on driver footprint | ||
| Deploys cloud, on-prem, air-gap — down to a single core | ||
| Ships Secure-by-Design: mTLS, hardened containers, Zero Trust | ||
Built for the most stringent environments in the world.
Our team built and operated Top Secret and Special Access programs. Falcon was designed under these constraints from day one.
0 Crit/High/Med CVEs at release. Memory safety enforced at compile time. Concurrency safety guaranteed by the orchestration layer.
End-to-end encryption. mTLS service-to-service. OIDC/SSO for users. RBAC for resources. Full audit logging.
We never see your data. Falcon runs in your cluster, reads from your storage, writes to your storage. No egress, no externally-managed service.
Priced by maximum throughput. No hidden consumption costs.
Tiered annual subscriptions, from starter to enterprise. You pay for capacity, not utilization — the more you use, the more you save.
| Tier | Max concurrent cores & positioning |
|---|---|
| Tier 1 | 64 coresPilot or single workload — proves out a batch or streaming job in production. |
| Tier 3 | 256 coresMost common land — a team or business unit consolidating several workloads onto Falcon. |
| Tier 5 | 1,024 coresProduction at scale — multiple teams, mixed batch + streaming + inference, mission-critical SLAs. |
| Tier 7 | 4,096 coresEnterprise-wide — Falcon as the standard compute layer across the org, replacing legacy Spark estate. |
| Tier 9 | CustomNegotiated — global rollouts, regulated industries, sovereign / air-gapped deployments. |
Bring three pipelines. We'll bring the benchmarks.
2–4 weeks. Apples-to-apples results on your real workloads. No replatforming. No commitment.