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, simple animation. The appendix includes the modified packages. We tested most services 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 table lists what we selected and how each farm charged at checkout.

The tested farms forced three deviations. RebusFarm's Standard GPU tier would not start on Classroom, so Classroom and Junk Shop ran on Priority+3 while Monster and Fox (EEVEE) started on Standard. Fox Renderfarm requires the render device set to none for EEVEE. RenderStreet never issued us an account of our own, so its runs used an existing customer's account. The RenderStreet comparison records that sign-up history.

Every time in this article is the render pipeline unless a section names another clock. The render pipeline times the farm, not your internet connection. It starts the moment a service begins working on the job and stops when that service reports the render finished. For most farms the starting moment is the end of the upload. RenderStreet starts only when you press Launch job. Everything the farm does in between counts against it: analysis, loading, test frames, queueing, rendering, retries, and finalization. Your upload, your download, and any stretch where the job was waiting on us instead of on the farm stay outside the clock.

iRender is the exception, because you rent the machine rather than send it a job. Its meter runs from boot to shutdown no matter what you are doing, so its time covers the whole paid window through to the finished files.

Upload, download, and complete end-to-end times are all in the published data. They simply get their own sections and their own tables rather than being folded into one number.

Charges stay in each provider's native unit and become dollars only through dated checkout quotes, always 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 for Classroom, Junk Shop, Monster, and Fox (EEVEE).

Superluminal's capacity control behaved exactly as you would want it to. Five nodes were cheapest in every scene. Thirty nodes were fastest on Classroom, Junk Shop, and Monster, and ten were fastest on the Fox scene. You pick the end of that tradeoff you need, and you can see the price before you commit.

Blendergrid finished three scenes, taking between 3,747.6 and 5,123.7 seconds each. It never ran Fox (EEVEE).

RenderStreet is worth a closer look, because it sells two products that behave nothing alike. Its On Demand GPU plan rendered Classroom in 530.3 seconds. Its One subscription rendered the same scene in 12,022.4 seconds, a wait 22.7 times longer, because One renders on CPUs.

One costs $59.97 a month with no per-job charge, which makes it impossible to price a single job honestly. It stays out of the cost chart and the scores, and it stays in the downloadable data as its own result. For scale, the monthly fee is worth about 13.7 Classroom renders on the On Demand plan at $4.37 each. Run 14 in a month and the fee works out near On Demand's per-job price, with the far longer wait attached.

One score per farm

Forty-three runs are too many to hold in your head. So each service also gets two numbers: a Time Score and a Price Score. Both are built in three small steps.

Step one: turn every result into a ratio. You cannot simply average raw seconds across the four scenes. Classroom renders 145 frames and Junk Shop costs more to run, so whichever scene has the biggest numbers would decide the ranking on its own. Instead, each scene has a fixed midpoint: the median result for that scene. The midpoints were locked in before any scoring, so no later result can shift the center under everyone else. Every run is then divided against the midpoint of its own scene. A farm twice as fast as its scene's midpoint gets a time ratio of 2. A farm charging half the midpoint gets a price ratio of 2. Ratios point the same way on both metrics: bigger is better.

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}} $

Step two: combine a farm's four ratios into one. Because a ratio of 2 means the same thing in every scene, the four ratios can be multiplied together and averaged. We use the geometric mean, the average that works on ratios: multiply the four numbers, then take the fourth root. It stops a single spectacular scene from carrying an otherwise weak farm.

Step three: put it on a 100 scale. The combined ratio becomes the published score through one squashing step. A score of 100 means the farm matches the midpoints exactly. The square root only compresses the spread so scores stay readable instead of running from 5 to 900. It never changes which of two farms is ahead.

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

To read a score, square it. 141.4 means twice as good overall, because 1.414 squared is 2. 70.7 means twice as bad. Time and price each run through the same three steps on their own ratios:

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

$ \left(\mathrm{Time\ Score}_{p},\ \mathrm{Price\ Score}_{p}\right) $

Up and to the right is better. A farm we tested at several configurations scores each one separately, and the configuration with the best complete result stands for the farm in the ranking. We never pair one configuration's speed with another configuration's price.

You pay a farm to stop waiting, so the combined score counts time twice as heavily as price:

$ \mathrm{Overall}_{p} = \frac{2\,\mathrm{Time\ Score}_{p}+\mathrm{Price\ Score}_{p}}{3} $

Of the 43 recordings, 41 finished a render. The cost chart plots the 40 of those with a dollar figure behind them. RenderStreet One bills a flat subscription that no single job can fairly carry. Two Render Network attempts ended with nothing to download.

Superluminal and GarageFarm land above 100 on both axes. Superluminal's ranked 30-node configuration has a Time Score of 179.1 and a Price Score of 187.8. Squared, those scores say its runs averaged 3.2 times faster and 3.5 times cheaper than the scene midpoints.

RenderJuice scores 93.7 on time and 176.2 on price. Blendergrid's three-scene scores are 22.9 on time and 80.6 on price. Untested scenes remain visible but do not change a score.

RenderStreet scores 68.8 on time and 121.7 on price. Its full-coverage Overall Score is 86.5, placing it fifth in the nine-service ranking. These scores use the four On Demand runs, not the supplemental One test.

The official ranking

The Overall Score counts speed twice and price once, because waiting is what you are paying to avoid. A score of 100 matches the reference. A farm that skipped a scene keeps that gap visible in its row rather than losing points for it. A failed render earns no time at all, and any attempt that still charged money counts against its price.

Superluminal earned the winning margin through low exact charges and strong render-pipeline times across the three tested capacities. Its 30-node runs were fastest on three scenes, while 10 nodes were fastest on the Fox scene. It has no separate analysis stage, lets you choose 5, 10, or 30 nodes, and downloads frames while the job runs. Provider completion to local output took 4.1 to 150.9 seconds across the 12 runs, but that retrieval time 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 for its render pipeline. Ten Superluminal nodes completed the same phase in 226.7 seconds. On Junk Shop, five nodes cost $0.48, while 30 nodes cut the render pipeline to 64.5 seconds.

What each farm advertises against what its own interface showed during the runs:

Fixed work limits scaling on small jobs. 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, and it also raised the charge.

Upload time

Every farm received the same packages over the same connection, so what separates them is client engineering. Browser uploads push a single stream and stall whenever latency rises. The Superluminal add-on splits the package across parallel chunks and keeps the link saturated. That is why its recorded range runs highest and widest. A multithreaded upload tracks the connection minute to minute, so ordinary broadband fluctuation shows up as a range instead of one flat number.

What you upload matters as much as how fast you upload it. The Superluminal add-on traces dependencies before submission and gave the most complete coverage of any client we tested. Linked libraries, textures, UDIMs, caches, fonts, and HDRIs all travel with the job. Anything missing or on another drive gets named on screen instead of quietly dropped. Nothing is left behind. ZIP mode packs it into one archive, and Project mode preserves your folder layout and re-sends only what changed. RenderJuice packs in a separate desktop app that cannot submit, costing an extra round trip every iteration. Render Network expects you to have packed the file yourself.

Effective rate is package size divided by recorded upload time. A range means several recorded runs: twelve for Superluminal, four for RenderStreet, and three for Blendergrid.

GarageFarm exceeds the connection's raw rate because its client compresses before sending. iRender's Classroom upload predates its recording, so its three later scenes capture upload from the first activation.

Download time

Threading helps here too, but 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.

Services that stream during the render 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 and cheapest on all four, and it was the first to put a complete sequence on local disk every time.

The more useful finding is why. Node count is the number farms advertise, and it explains less than you would expect. Fox Renderfarm ran 129 nodes on Classroom and still took longer than ten Superluminal nodes. Speed comes from everything wrapped around the render. How work lands on machines, how fast a node starts producing, and what happens between frames decide more of your wait than raw capacity. That engineering is where our lead comes from. Most of it is public. The scheduler is not.

The transfers count too. A client that traces every dependency makes the render correct on the first attempt. A client that downloads during the render gives you frames minutes before the farm calls itself done. Those two properties moved delivery times here by more than any hardware difference.

Then there is what you can see. A render you cannot inspect is a render you find out about too late, so we compare every farm's interface with the same recordings behind this benchmark. Superluminal leads there as well, confirming all eight monitoring features we scored.

Speed, correct files, early frames, and a view of the work are the same thing seen from four angles. That is the workflow we build, as beautiful as we can make it.

Read next:

• How Superluminal is so fast: the engineering behind the farm times in this benchmark.
• Render farm interfaces compared: every submission surface from these recordings, 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.