The best Blender render farms, ranked

We tested nine commercial Blender render services with four standard scenes and screen-recorded all 43 runs.

Method

We packaged four Blender Foundation demo scenes once and submitted them unchanged to every service, on a normal paid account. The demo files ship as stills, so we modified each scene to render a short animation. Most services ran once per scene.

We picked the most balanced GPU configuration each farm offered and moved up a priority tier when that proved too slow.

The farms forced two deviations. RebusFarm's Standard GPU tier would not start on Classroom, so Classroom and Junk Shop ran on Priority+3. Fox Renderfarm requires the render device set to none for EEVEE.

Every time in this article is the render pipeline unless a section names another clock. It starts when the farm begins working on the job and stops when the farm reports the render finished. For most farms that start is the end of the upload. When a farm requires a separate launch action, the clock starts at that press.

Everything the farm does in between counts against it: analysis, loading, test frames, queueing, rendering, retries, and finalization. Your upload, your download, and time spent waiting on us do not.

iRender is the exception. You rent the machine rather than send it a job, so its meter runs from boot to shutdown and its time covers the whole paid window.

Upload, download, and end-to-end times have their own sections and tables. Charges stay in native units and become dollars only through dated checkout quotes, marked as estimates. Failed attempts keep their cost.

Scene	Frames	Engine	Shortest render pipeline
Classroom	145	Cycles	Superluminal, 90.5 s
Junk Shop	96	Cycles	Superluminal, 64.5 s
Monster Under the Bed	96	Cycles	Superluminal, 59.9 s
Fox	50	EEVEE	Superluminal, 66.5 s

Results

Superluminal recorded the shortest render pipeline on all four scenes. Its leads over the next service were 73.1, 62.8, 28.6, and 14.1 seconds.

Its capacity control behaved as promised. Five nodes were cheapest in every scene. Thirty were fastest on the three Cycles scenes, ten on Fox. The price is visible before you commit.

Seven of the nine services completed all four scenes. Blendergrid completed three, because Blendergrid supports only Cycles. Two paid Render Network attempts on Junk Shop returned no output. The other 41 runs finished.

One score per farm

Forty-three runs are too many to hold in your head. So each service gets two numbers, a Speed Score and a Cost Score, built in three steps.

Every result becomes a ratio

Raw seconds cannot be averaged across scenes. Classroom renders 145 frames and Junk Shop costs more, so the biggest scene would decide the ranking alone.

Each scene instead has a fixed midpoint: its median result, locked before any scoring. Every run is divided against its own scene's midpoint. Twice as fast as the midpoint is a time ratio of 2. Half the midpoint's cost is a price ratio of 2. Bigger is better on both.

For provider $p$ and scene $s$, with time $T$ and cost $C$:

$ r^{T}_{p,s} = \frac{T_{\mathrm{ref},s}}{T_{p,s}} \qquad\qquad r^{C}_{p,s} = \frac{C_{\mathrm{ref},s}}{C_{p,s}} $

Four ratios combine into one

A ratio of 2 means the same thing in every scene, so the four combine with the geometric mean: multiply, then take the fourth root. One spectacular scene cannot carry a weak farm.

The ratio lands on a 100 scale

A square root compresses the spread so scores stay readable. It never changes which farm is ahead. 100 means the farm matches the midpoints. To read a score, square it: 141.4 means twice as good, 70.7 twice as bad.

$ \bar r=\left(\prod_{s=1}^{n}r_s\right)^{1/n} \qquad\qquad S(\bar r)=100\sqrt{\bar r} $

The Speed Score and the Cost Score each run through the same steps on their own ratios:

$ \mathrm{Speed\ Score}_{p}=S\!\left(\left(\prod_{s=1}^{n}r^{T}_{p,s}\right)^{\!1/n}\right) \qquad\qquad \mathrm{Cost\ Score}_{p}=S\!\left(\left(\prod_{s=1}^{n}r^{C}_{p,s}\right)^{\!1/n}\right) $

Those two numbers place each farm at one coordinate. Up and to the right is better. A farm tested at several configurations scores each one separately, and its best complete configuration stands for it in the ranking. One configuration's speed is never paired with another's price.

You pay a farm to stop waiting, so the Overall Score counts speed twice and cost once:

$ \mathrm{Overall}_{p} = \frac{2\,\mathrm{Speed\ Score}_{p}+\mathrm{Cost\ Score}_{p}}{3} $

The cost chart plots the 40 finished runs that carry a dollar figure. The one subscription run stays out. A flat monthly fee gives a single job no honest price.

Superluminal and GarageFarm land above 100 on both axes. Superluminal's ranked 30-node configuration scores 179.1 on speed and 187.8 on cost. Squared, that is 3.2 times faster and 3.5 times cheaper than the midpoints.

RenderJuice scores 93.7 on speed and 176.2 on cost. RenderStreet scores 68.8 and 121.7. Blendergrid scores 22.9 and 80.6 on its three scenes.

The official ranking

A score of 100 matches the reference. A skipped scene stays visible as a gap instead of costing points. A failed render earns no time, and any attempt that still charged money counts against its cost.

Superluminal's margin comes from low exact charges and fast render pipelines at all three capacities. It has no separate analysis stage and downloads frames while the job runs. Provider completion to local output took 4.1 to 150.9 seconds across its 12 runs. Retrieval does not enter the score.

Node count

Node count matters less than advertised. Fox Renderfarm showed 129 running nodes on Classroom and needed 292.8 seconds. Ten Superluminal nodes finished the same phase in 226.7.

What each farm advertises against what its own interface showed:

Fixed work limits scaling. Going from 5 to 30 Superluminal nodes on Classroom cut the render pipeline from 411.0 to 90.5 seconds. Six times the capacity bought a 4.5-fold gain, at a higher charge.

Upload time

Every farm received the same packages over the same connection. What separates them is client engineering. Browser uploads push one stream and stall when latency rises. The Superluminal add-on splits the package across parallel chunks and keeps the link saturated. Its range runs highest and widest because a saturated upload tracks the connection minute to minute.

What you upload matters as much as how fast. The add-on traces dependencies before submission: linked libraries, textures, UDIMs, caches, fonts, and HDRIs all travel with the job. Anything missing is named on screen instead of quietly dropped. It gave the most complete coverage of any client we tested.

RenderJuice adds a desktop app that cannot submit, one extra round trip per iteration. Render Network expects a file you packed yourself.

Effective rate is package size divided by recorded upload time. A range means several recorded runs. GarageFarm exceeds the connection's raw rate because its client compresses first. iRender's Classroom upload predates its recording, so its three later scenes capture upload from first activation.

Download time

Threading helps here too. What matters more is whether frames download while the job is still rendering. A farm that waits for the last frame adds its whole download to your wait.

Streaming services come first, then the shortest observed wait leads each group. This time starts when the farm reports completion and ends when the last frame reaches local disk.

Conclusion

Superluminal won this benchmark. It was fastest on all four scenes, cheapest on all four, and first to put a complete sequence on local disk every time.

The useful finding is why. Node count explains less than you would expect. Fox Renderfarm ran 129 nodes on Classroom and still lost to ten of ours. Speed comes from what wraps the render: how work lands on machines, how fast a node starts producing, and what happens between frames. That engineering is mostly public. The scheduler is not.

Transfers decide the rest. A client that traces every dependency renders correctly on the first attempt. A client that downloads during the render hands you frames before the farm calls itself done. Those two properties moved delivery times more than any hardware difference.

A render you cannot inspect is a render you find out about too late, so we also compared every farm's interface with these same recordings. Superluminal confirmed all eight monitoring features we scored.

Speed, correct files, early frames, and a view of the work are one workflow. We build it as beautiful as we can make it.

Read next:

• How Superluminal is so fast: the engineering behind these times.
• Render farm interfaces compared: every submission surface, side by side.
• Head to head: Superluminal vs GarageFarm, Superluminal vs RenderJuice, Superluminal vs Fox Renderfarm, Superluminal vs RebusFarm, Superluminal vs RenderStreet, Superluminal vs iRender, Superluminal vs Render Network, and Superluminal vs Blendergrid.